WO2021077315A1

WO2021077315A1 - Systems and methods for autonomous driving

Info

Publication number: WO2021077315A1
Application number: PCT/CN2019/112654
Authority: WO
Inventors: Muchenxuan Tong; Yun Jiang
Original assignee: Beijing Voyager Technology Co., Ltd.
Priority date: 2019-10-23
Filing date: 2019-10-23
Publication date: 2021-04-29
Also published as: CN112105956A

Abstract

The present disclosure relates to systems and methods for autonomous driving. The system may receive point cloud data captured by a sensor device. The system may also divide the point cloud data into a plurality of groups of point cloud data based on a map associated with the point cloud data. The system further may process the plurality of groups of point cloud data.

Description

SYSTEMS AND METHODS FOR AUTONOMOUS DRIVING

TECHNICAL FIELD

The present disclosure generally relates to systems and methods for autonomous driving, and in particular, to systems and methods for processing point cloud data based on map information.

BACKGROUND

With the development of micro-electronic and robotic technologies, the exploration of autonomous driving has developed rapidly nowadays. Commonly, after receiving point cloud data captured by a sensor device (e.g., a Lidar) , an autonomous driving system can quickly identify surrounding objects (e.g., other vehicles, obstacles) by processing the point cloud data and determine a driving path for a vehicle based on information (e.g., velocity information, position information) associated with the surrounding objects. However, in some situations, the point cloud data associated with different objects (e.g., a vehicle on a road and an obstacle which is located in the vicinity of the vehicle and outside the road) may be almost connected together, which may result in that an error may occur in the identification and/or the processing of the objects. Therefore, it is desirable to provide systems and methods for distinguishing point cloud data almost connected together, thereby improving the efficiency and accuracy of the processing of the point cloud data.

SUMMARY

An aspect of the present disclosure relates to a system for autonomous driving. The system may include a storage medium to store a set of instructions and a processor communicatively coupled to the storage medium. The system may receive point cloud data captured by a sensor device; divide the point cloud data into a plurality of groups of point cloud data based on a map associated with the point cloud data; and process the plurality of groups of point cloud data.

In some embodiments, the sensor device may include a light detection and ranging (Lidar) device.

In some embodiments, to divide the point cloud data into a plurality of groups of point cloud data based on a map associated with the point cloud data, the system may further project the point cloud data onto a two-dimensional plane corresponding to the map; identify at least one reference line on the two-dimensional plane based on the map; and divide the projected point cloud data into the plurality of groups of point cloud data based on the at least one reference line on the two-dimensional plane.

In some embodiments, the at least one reference line may include a road boundary, a lane line, and/or a sidewalk.

In some embodiments, to process the plurality of groups of point cloud data, for each of the plurality of groups of point cloud data, the system may further perform an aggregation operation on the group of point cloud data and identify at least one object from the group of point cloud data based on the aggregation operation.

In some embodiments, to process the plurality of groups of point cloud data, the system may further perform the aggregation operation on the group of point cloud data based on a plurality of point cloud blocks corresponding to a plurality of grids.

In some embodiments, the at least one object may include a vehicle, a pedestrian, a building, and/or an obstacle.

In some embodiments, the system may further determine a driving path for the vehicle based on the at least one object and transmit signals to one or more control components of the vehicle to direct the vehicle to follow the driving path.

In some embodiments, the map may contain information of an accuracy of a centimeter level and/or a millimeter level.

In some embodiments, the point cloud data may be captured by the sensor device within a predetermined range from a vehicle.

Another aspect of the present disclosure relates to a method implemented on a computing device including at least one processor, at least one storage medium, and a communication platform connected to a network. The method may include receiving point cloud data captured by a sensor device; dividing the point cloud data into a plurality of groups of point cloud data based on a map associated with the point cloud data; and processing the plurality of groups of point cloud data.

In some embodiments, the dividing the point cloud data into the plurality of groups of point cloud data based on the map associated with the point cloud data may include projecting the point cloud data onto a two-dimensional plane corresponding to the map; identifying at least one reference line on the two-dimensional plane based on the map; and dividing the projected point cloud data into the plurality of groups of point cloud data based on the at least one reference line on the two-dimensional plane.

In some embodiments, the processing the plurality of groups of point cloud data may include for each of the plurality of groups of point cloud data, performing an aggregation operation on the group of point cloud data and identifying at least one object from the group of point cloud data based on the aggregation operation.

In some embodiments, the performing the aggregation operation on the group of point cloud data may include performing the aggregation operation on the group of point cloud data based on a plurality of point cloud blocks corresponding to a plurality of grids.

In some embodiments, the method may further include determining a driving path for the vehicle based on the at least one object and transmitting signals to one or more control components of the vehicle to direct the vehicle to follow the driving path.

A further aspect of the present disclosure relates to a vehicle configured for autonomous driving. The vehicle may include a detecting component, a planning component, and a control component. The planning component may be configured to receive point cloud data captured by the detecting component; divide the point cloud data into a plurality of groups of point cloud data based on a map associated with the point cloud data; and process the plurality of groups of point cloud data.

In some embodiments, the detecting component may include a light detection and ranging (Lidar) device.

In some embodiments, to divide the point cloud data into the plurality of groups of point cloud data based on the map associated with the point cloud data, the planning component may be configured to project the point cloud data onto a two-dimensional plane corresponding to the map; identify at least one reference line on the two-dimensional plane based on the map; and divide the projected point cloud data into the plurality of groups of point cloud data based on the at least one reference line on the two-dimensional plane.

In some embodiments, to process the plurality of groups of point cloud data, for each of the plurality of groups of point cloud data, the planning component may be configured to perform an aggregation operation on the group of point cloud data and identify at least one object from the group of point cloud data based on the aggregation operation.

In some embodiments, to perform the aggregation operation on the group of point cloud data, the planning component may be configured to perform the aggregation operation on the group of point cloud data based on a plurality of point cloud blocks corresponding to a plurality of grids.

In some embodiments, the planning component may be further configured to determine a driving path for the vehicle based on the at least one object and transmit signals to one or more control components of the vehicle to direct the vehicle to follow the driving path.

In some embodiments, the point cloud data may be captured by the detecting component within a predetermined range from a vehicle.

A still further aspect of the present disclosure relates to a system for autonomous driving. The system may include a receiving module, a division module, and a processing module. The receiving module may be configured to receive point cloud data captured by a sensor device. The division module may be configured to divide the point cloud data into a plurality of groups of point cloud data based on a map associated with the point cloud data. The processing module may be configured to process the plurality of groups of point cloud data.

A still further aspect of the present disclosure relates to a non-transitory computer readable medium including executable instructions. When the executable instructions are executed by at least one processor, the executable instructions may direct the at least one processor to perform a method. The method may include receiving point cloud data captured by a sensor device; dividing the point cloud data into a plurality of groups of point cloud data based on a map associated with the point cloud data; and processing the plurality of groups of point cloud data.

Additional features will be set forth in part in the description which follows, and in part will become apparent to those skilled in the art upon examination of the following and the accompanying drawings or may be learned by production or operation of the examples. The features of the present disclosure may be realized and attained by practice or use of various aspects of the methodologies, instrumentalities, and combinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplary embodiments. These exemplary embodiments are described in detail with reference to the drawings. The drawings are not to scale. These embodiments are non-limiting schematic embodiments, in which like reference numerals represent similar structures throughout the several views of the drawings, and wherein:

FIG. 1 is a schematic diagram illustrating an exemplary autonomous driving system according to some embodiments of the present disclosure;

FIG. 2 is a schematic diagram illustrating exemplary hardware and/or software components of an exemplary computing device according to some embodiments of the present disclosure;

FIG. 3 is a schematic diagram illustrating exemplary hardware and/or software components of an exemplary mobile device according to some embodiments of the present disclosure;

FIG. 4 is a block diagram illustrating an exemplary processing device according to some embodiments of the present disclosure;

FIG. 5 is a flowchart illustrating an exemplary process for processing point cloud data according to some embodiments of the present disclosure;

FIG. 6 is a flowchart illustrating an exemplary process for dividing point cloud data into a plurality of groups of point cloud data according to some embodiments of the present disclosure;

FIG. 7-A is a schematic diagram illustrating exemplary reference lines on a map according to some embodiments of the present disclosure;

FIG. 7-B is a schematic diagram illustrating an exemplary process for dividing point cloud data into a plurality of groups of point cloud data according to some embodiments of the present disclosure; and

FIG. 8 is a schematic diagram illustrating an exemplary aggregation operation based on point cloud blocks according to some embodiments of the present disclosure.

DETAILED DESCRIPTION

The following description is presented to enable any person skilled in the art to make and use the present disclosure, and is provided in the context of a particular application and its requirements. Various modifications to the disclosed embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the present disclosure. Thus, the present disclosure is not limited to the embodiments shown, but is to be accorded the widest scope consistent with the claims.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting. As used herein, the singular forms “a, ” “an, ” and “the” may be intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprise, ” “comprises, ” and/or “comprising, ” “include, ” “includes, ” and/or “including, ” when used in this disclosure, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

These and other features, and characteristics of the present disclosure, as well as the methods of operations and functions of the related elements of structure and the combination of parts and economies of manufacture, may become more apparent upon consideration of the following description with reference to the accompanying drawings, all of which form part of this disclosure. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended to limit the scope of the present disclosure. It is understood that the drawings are not to scale.

The flowcharts used in the present disclosure illustrate operations that systems implement according to some embodiments of the present disclosure. It is to be expressly understood, the operations of the flowcharts may be implemented not in order. Conversely, the operations may be implemented in inverted order, or simultaneously. Moreover, one or more other operations may be added to the flowcharts. One or more operations may be removed from the flowcharts.

Moreover, while the systems and methods disclosed in the present disclosure are described primarily regarding a transportation system in land, it should be understood that this is only one exemplary embodiment. The systems and methods of the present disclosure may be applied to any other kind of transportation system. For example, the systems and methods of the present disclosure may be applied to transportation systems of different environments including land, ocean, aerospace, or the like, or any combination thereof. The autonomous vehicle of the transportation systems may include a taxi, a private car, a hitch, a bus, a train, a bullet train, a high-speed rail, a subway, a vessel, an aircraft, a spaceship, a hot-air balloon, or the like, or any combination thereof.

An aspect of the present disclosure relates to systems and methods for autonomous driving. The system may receive point cloud data captured by a sensor device (e.g., a Lidar) of a vehicle (e.g., an autonomous vehicle) and determine a map (e.g., a high-definition map) associated with the point cloud data. According to the map associated with the point cloud data, the system may divide the point cloud data into a plurality of groups of point cloud data. For example, the system may divide the point cloud data into a group of point cloud data on a road and a group of point cloud data outside the road based on a road boundary in the map. Further, the system may process the plurality of groups of point cloud data. For example, the system may perform an aggregation operation on each of the plurality of groups of point cloud data and identify at least one object (e.g., a vehicle, a pedestrian, a building, an obstacle) from the group of point cloud data based on the aggregation operation. Also, the system may determine a driving path for the vehicle based on the at least one identified object and direct the vehicle to follow the driving path. According to the systems and methods of the present disclosure, point cloud data may be divided into a plurality of groups of point cloud data based on map information (e.g., a reference line in a map) , thereby improving the efficiency and accuracy of the processing of the point cloud data.

FIG. 1 is a schematic diagram illustrating an exemplary autonomous driving system according to some embodiments of the present disclosure. In some embodiments, the autonomous driving system 100 may include a vehicle 110 (e.g. 110-1, 110-2, …, 110-n) , a server 120, a terminal device 130, a storage device 140, a network 150, and a positioning and navigation system 160.

The vehicle 110 may be any type of autonomous vehicle, unmanned aerial vehicle, etc. As used herein, an autonomous vehicle or an unmanned aerial vehicle may refer to a vehicle that is capable of achieving a certain level of driving automation. Exemplary levels of driving automation may include a first level at which the vehicle is mainly supervised by a human and has a specific autonomous function (e.g., autonomous steering or accelerating) , a second level at which the vehicle has one or more advanced driver assistance systems (ADAS) (e.g., an adaptive cruise control system, a lane-keep system) that can control a braking, a steering, and/or an acceleration of the vehicle, a third level at which the vehicle is able to drive autonomously when one or more certain conditions are met, a fourth level at which the vehicle can operate without a human input or oversight but still is subject to some constraints (e.g., be confined to a certain area) , a fifth level at which the vehicle can operate autonomously under all circumstances, or the like, or any combination thereof.

In some embodiments, the vehicle 110 may have equivalent structures that enable the vehicle 110 to move around or fly. For example, the vehicle 110 may include structures of a conventional vehicle, for example, a chassis, a suspension, a steering device (e.g., a steering wheel) , a brake device (e.g., a brake pedal) , an accelerator, etc. As another example, the vehicle 110 may have a body and at least one wheel. The body may be a body of any style, such as a sports vehicle, a coupe, a sedan, a pick-up truck, a station wagon, a sports utility vehicle (SUV) , a minivan, or a conversion van. The at least one wheel may be configured as all-wheel drive (AWD) , front-wheel drive (FWR) , rear-wheel drive (RWD) , etc. In some embodiments, it is contemplated that the vehicle 110 may be an electric vehicle, a fuel cell vehicle, a hybrid vehicle, a conventional internal combustion engine vehicle, etc.

In some embodiments, the vehicle 110 may be capable of sensing its environment and navigating with one or more detecting units 112. The plurality of detection units 112 may include a sensor device (e.g., a radar (e.g., a light detection and ranging (Lidar) device) ) , a global position system (GPS) module, an inertial measurement unit (IMU) , a camera, or the like, or any combination thereof. The radar (e.g., the Lidar device) may be configured to scan the surrounding of the vehicle 110 and generate point cloud data. The point cloud data may be used to generate digital three dimensional (3D) representations of one or more objects surrounding the vehicle 110. The GPS module may refer to a device that is capable of receiving geolocation and time information from GPS satellites and then determining the device’s geographical position. The IMU may refer to an electronic device that measures and provides a vehicle’s specific force, angular rate, and sometimes a magnetic field surrounding the vehicle, using various inertial sensors. In some embodiments, the various inertial sensors may include an acceleration sensor (e.g., a piezoelectric sensor) , a velocity sensor (e.g., a Hall sensor) , a distance sensor (e.g., a radar, an infrared sensor) , a steering angle sensor (e.g., a tilt sensor) , a traction-related sensor (e.g., a force sensor) , etc. The camera may be configured to obtain one or more images relating to objects (e.g., a person, an animal, a tree, a roadblock, a building, or a vehicle) that are within the scope of the camera.

In some embodiments, the server 120 may be a single server or a server group. The server group may be centralized or distributed (e.g., the server 120 may be a distributed system) . In some embodiments, the server 120 may be local or remote. For example, the server 120 may access information and/or data stored in the terminal device 130, the detecting units 112, the vehicle 110, the storage device 140, and/or the positioning and navigation system 160 via the network 150. As another example, the server 120 may be directly connected to the terminal device 130, the detecting units 112, the vehicle 110, and/or the storage device 140 to access stored information and/or data. In some embodiments, the server 120 may be implemented on a cloud platform or an onboard computer. Merely by way of example, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an inter-cloud, a multi-cloud, or the like, or any combination thereof. In some embodiments, the server 120 may be implemented on a computing device 200 including one or more components illustrated in FIG. 2 in the present disclosure.

In some embodiments, the server 120 may include a processing device 122. The processing device 122 may process information and/or data associated with driving information associated with the vehicle 110 to perform one or more functions described in the present disclosure. For example, the processing device 122 may divide point cloud data captured by the detecting units 112 (e.g., a sensor device) of the vehicle 110 into a plurality of groups of point cloud data and further process the plurality of groups of point cloud data respectively. Further, the processing device 122 may identify one or more objects based on the point cloud data and determine a driving path for the vehicle 110. That is, the processing device 122 may be configured as a planning component of the vehicle 110. In some embodiments, the processing device 122 may include one or more processing devices (e.g., single-core processing device (s) or multi-core processor (s) ) . Merely by way of example, the processing device 122 may include a central processing unit (CPU) , an application-specific integrated circuit (ASIC) , an application-specific instruction-set processor (ASIP) , a graphics processing unit (GPU) , a physics processing unit (PPU) , a digital signal processor (DSP) , a field-programmable gate array (FPGA) , a programmable logic device (PLD) , a controller, a microcontroller unit, a reduced instruction set computer (RISC) , a microprocessor, or the like, or any combination thereof. In some embodiments, the processing device 122 may be integrated into the vehicle 110 and/or the terminal device 130.

In some embodiments, the terminal device 130 may include a mobile device 130-1, a tablet computer 130-2, a laptop computer 130-3, a built-in device in a vehicle 130-4, a wearable device130-5, or the like, or any combination thereof. In some embodiments, the mobile device 130-1 may include a smart home device, a smart mobile device, a virtual reality device, an augmented reality device, or the like, or any combination thereof. The smart home device may include a smart lighting device, a control device of an intelligent electrical apparatus, a smart monitoring device, a smart television, a smart video camera, an interphone, or the like, or any combination thereof. The smart mobile device may include a smartphone, a personal digital assistant (PDA) , a gaming device, a navigation device, a point of sale (POS) device, or the like, or any combination thereof. The virtual reality device and/or the augmented reality device may include a virtual reality helmet, a virtual reality glass, a virtual reality patch, an augmented reality helmet, an augmented reality glass, an augmented reality patch, or the like, or any combination thereof. For example, the virtual reality device and/or the augmented reality device may include a Google ^TM Glass, an Oculus Rift, a HoloLens, a Gear VR, etc. In some embodiments, the built-in device in the vehicle 130-4 may include an onboard computer, an onboard television, etc. In some embodiments, the wearable device 130-5 may include a smart bracelet, a smart footgear, a smart glass, a smart helmet, a smart watch, smart clothing, a smart backpack, a smart accessory, or the like, or any combination thereof. In some embodiments, the terminal device 130 may be a device with positioning technology for locating the location of the terminal device 130. In some embodiments, the server 120 may be integrated into the vehicle 110 and/or the terminal device 130.

The storage device 140 may store data and/or instructions. In some embodiments, the storage device 140 may store data obtained from the vehicle 110, the detecting units 112, the processing device 122, the terminal device 130, the positioning and navigation system 160, and/or an external storage device. For example, the storage device 140 may store the point cloud data captured by the detecting units 112. In some embodiments, the storage device 140 may store data and/or instructions that the server 120 may execute or use to perform exemplary methods described in the present disclosure. For example, the storage device 140 may store instructions that the processing device 122 may execute or use to divide the point cloud data into a plurality of groups of point cloud data. In some embodiments, the storage device 140 may include a mass storage, a removable storage, a volatile read-and-write memory, a read-only memory (ROM) , or the like, or any combination thereof. Exemplary mass storage may include a magnetic disk, an optical disk, a solid-state drive, etc. Exemplary removable storage may include a flash drive, a floppy disk, an optical disk, a memory card, a zip disk, a magnetic tape, etc. Exemplary volatile read-and-write memory may include a random access memory (RAM) . Exemplary RAM may include a dynamic RAM (DRAM) , a double date rate synchronous dynamic RAM (DDR SDRAM) , a static RAM (SRAM) , a thyristor RAM (T-RAM) , and a zero-capacitor RAM (Z-RAM) , etc. Exemplary ROM may include a mask ROM (MROM) , a programmable ROM (PROM) , an erasable programmable ROM (EPROM) , an electrically-erasable programmable ROM (EEPROM) , a compact disk ROM (CD-ROM) , and a digital versatile disk ROM, etc. In some embodiments, the storage device 140 may be implemented on a cloud platform. Merely by way of example, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an inter-cloud, a multi-cloud, or the like, or any combination thereof.

In some embodiments, the storage device 140 may be connected to the network 150 to communicate with one or more components (e.g., the server 120, the terminal device 130, the detecting units 112, the vehicle 110, and/or the positioning and navigation system 160) of the autonomous driving system 100. One or more components of the autonomous driving system 100 may access the data or instructions stored in the storage device 140 via the network 150. In some embodiments, the storage device 140 may be directly connected to or communicate with one or more components (e.g., the server 120, the terminal device 130, the detecting units 112, the vehicle 110, and/or the positioning and navigation system 160) of the autonomous driving system 100. In some embodiments, the storage device 140 may be part of the server 120. In some embodiments, the storage device 140 may be integrated into the vehicle 110.

The network 150 may facilitate exchange of information and/or data. In some embodiments, one or more components (e.g., the server 120, the terminal device 130, the detecting units 112, the vehicle 110, the storage device 140, or the positioning and navigation system 160) of the autonomous driving system 100 may send information and/or data to other component (s) of the autonomous driving system 100 via the network 150. For example, the server 120 may obtain a map associated with point cloud data from the storage device 140 via the network 150. In some embodiments, the network 150 may be any type of wired or wireless network, or combination thereof. Merely by way of example, the network 150 may include a cable network, a wireline network, an optical fiber network, a telecommunications network, an intranet, an Internet, a local area network (LAN) , a wide area network (WAN) , a wireless local area network (WLAN) , a metropolitan area network (MAN) , a wide area network (WAN) , a public telephone switched network (PSTN) , a Bluetooth network, a ZigBee network, a near field communication (NFC) network, or the like, or any combination thereof. In some embodiments, the network 150 may include one or more network access points. For example, the network 150 may include wired or wireless network access points (e.g., 150-1, 150-2) , through which one or more components of the autonomous driving system 100 may be connected to the network 150 to exchange data and/or information.

The positioning and navigation system 160 may determine information associated with an object, for example, the terminal device 130, the vehicle 110, etc. In some embodiments, the positioning and navigation system 160 may include a global positioning system (GPS) , a global navigation satellite system (GLONASS) , a compass navigation system (COMPASS) , a BeiDou navigation satellite system, a Galileo positioning system, a quasi-zenith satellite system (QZSS) , etc. The information may include a location, an elevation, a velocity, an acceleration of the object, a current time, etc. The positioning and navigation system 160 may include one or more satellites, for example, a satellite 160-1, a satellite 160-2, and a satellite 160-3. The satellites 160-1 through 160-3 may determine the information mentioned above independently or jointly. The positioning and navigation system 160 may send the information mentioned above to the server 120, the vehicle 110, and/or the terminal device 130 via wireless connections.

One of ordinary skill in the art would understand that when an element (or component) of the autonomous driving system 100 performs, the element may perform through electrical signals and/or electromagnetic signals. For example, when the terminal device 130 transmits out a request to the server 120, a processor of the terminal device 130 may generate an electrical signal encoding the request. The processor of the terminal device 130 may then transmit the electrical signal to an output port. If the terminal device 130 communicates with the server 120 via a wired network, the output port may be physically connected to a cable, which further may transmit the electrical signal to an input port of the server 120. If the terminal device 130 communicates with the server 120 via a wireless network, the output port of the terminal device 130 may be one or more antennas, which convert the electrical signal to an electromagnetic signal. Within an electronic device, such as the terminal device 130 and/or the server 120, when a processor thereof processes an instruction, transmits out an instruction, and/or performs an action, the instruction and/or action is conducted via electrical signals. For example, when the processor retrieves or saves data from a storage medium (e.g., the storage device 140) , it may transmit out electrical signals to a read/write device of the storage medium, which may read or write structured data in the storage medium. The structured data may be transmitted to the processor in the form of electrical signals via a bus of the electronic device. Here, an electrical signal may refer to one electrical signal, a series of electrical signals, and/or a plurality of discrete electrical signals.

FIG. 2 is a schematic diagram illustrating exemplary hardware and/or software components of an exemplary computing device according to some embodiments of the present disclosure. In some embodiments, the server 120 and/or the terminal device 130 may be implemented on the computing device 200. For example, the processing device 122 may be implemented on the computing device 200 and configured to perform functions of the processing device 122 disclosed in this disclosure.

The computing device 200 may be used to implement any component of the autonomous driving system 100 of the present disclosure. For example, the processing device 122 of the autonomous driving system 100 may be implemented on the computing device 200, via its hardware, software program, firmware, or a combination thereof. Although only one such computer is shown for convenience, the computer functions related to the autonomous driving system 100 as described herein may be implemented in a distributed manner on a number of similar platforms to distribute the processing load.

The computing device 200 may include communication (COM) ports 250 connected to and from a network (e.g., the network 150) connected thereto to facilitate data communications. The computing device 200 may also include a processor (e.g., a processor 220) , in the form of one or more processors (e.g., logic circuits) , for executing program instructions. For example, the processor may include interface circuits and processing circuits therein. The interface circuits may be configured to receive electronic signals from a bus 210, wherein the electronic signals encode structured data and/or instructions for the processing circuits to process. The processing circuits may conduct logic calculations, and then determine a conclusion, a result, and/or an instruction encoded as electronic signals. Then the interface circuits may send out the electronic signals from the processing circuits via the bus 210.

The computing device 200 may further include program storage and data storage of different forms, for example, a disk 270, a read-only memory (ROM) 230, or a random access memory (RAM) 240, for storing various data files to be processed and/or transmitted by the computing device 200. The computing device 200 may also include program instructions stored in the ROM 230, the RAM 240, and/or another type of non-transitory storage medium to be executed by the processor 220. The methods and/or processes of the present disclosure may be implemented as the program instructions. The computing device 200 also includes an I/O component 260, supporting input/output between the computing device 200 and other components therein. The computing device 200 may also receive programming and data via network communications.

Merely for illustration, only one processor is described in the computing device 200. However, it should be noted that the computing device 200 in the present disclosure may also include multiple processors, and thus operations that are performed by one processor as described in the present disclosure may also be jointly or separately performed by the multiple processors. For example, the processor of the computing device 200 executes both operation A and operation B. As another example, operation A and operation B may also be performed by two different processors jointly or separately in the computing device 200 (e.g., the first processor executes operation A and the second processor executes operation B, or the first and second processors jointly execute operations A and B) .

FIG. 3 is a schematic diagram illustrating exemplary hardware and/or software components of an exemplary mobile device according to some embodiments of the present disclosure. In some embodiments, the terminal device 130 may be implemented on the mobile device 300. As illustrated in FIG. 3, the mobile device 300 may include a communication platform 310, a display 320, a graphics processing unit (GPU) 330, a central processing unit (CPU) 340, an I/O 350, a memory 360, a mobile operating system (OS) 370, and storage 390. In some embodiments, any other suitable component, including but not limited to a system bus or a controller (not shown) , may also be included in the mobile device 300.

In some embodiments, the mobile operating system 370 (e.g., iOS ^TM, Android ^TM, Windows Phone ^TM) and one or more applications 380 may be loaded into the memory 360 from the storage 390 in order to be executed by the CPU 340. The applications 380 may include a browser or any other suitable mobile app for receiving and rendering information relating to positioning or other information from the processing device 122. User interactions with the information stream may be achieved via the I/O 350 and provided to the processing device 122 and/or other components of the autonomous driving system 100 via the network 150.

To implement various modules, units, and their functionalities described in the present disclosure, computer hardware platforms may be used as the hardware platform (s) for one or more of the elements described herein. A computer with user interface elements may be used to implement a personal computer (PC) or any other type of work station or terminal device. A computer may also act as a server if appropriately programmed.

FIG. 4 is a block diagram illustrating an exemplary processing device according to some embodiments of the present disclosure. The processing device 122 may include a receiving module 410, a division module 420, and a processing module 430.

The receiving module 410 may be configured to receive point cloud data captured by a sensor device (e.g., a Lidar device) associated with a subject (e.g., the vehicle 110) . The receiving module 410 may receive the point cloud data from the sensor device, the storage device 140, and/or the terminal device 130 via the network 150. More descriptions of the point cloud data may be found elsewhere in the present disclosure (e.g., FIG. 5 and the descriptions thereof) .

The division module 420 may be configured to divide the point cloud data into a plurality of groups of point cloud data based on a map associated with the point cloud data. In some embodiments, the map may be a high-definition map containing information of an accuracy of a centimeter level or a millimeter level. In some embodiments, the division module 420 may divide the point cloud data into the plurality of groups of point cloud data based on at least one reference line on the map. The at least one reference line may include a road boundary, a lane line, a line (e.g., a center line, an edge line) on a sidewalk, or the like, or a combination thereof. More descriptions of the division of the point cloud data may be found elsewhere in the present disclosure (e.g., FIG. 6 and the descriptions thereof) .

The processing module 430 may be configured to process the plurality of groups of point cloud data. In some embodiments, for each of the plurality of groups of point cloud data, the processing module 430 may segment the group of point cloud data into a plurality of point cloud blocks. In some embodiments, for each of the plurality of groups of point cloud data, the processing module 430 may perform an aggregation operation on the group of point cloud data and determine one or more point cloud clusters based on the aggregation operation. When performing the aggregation operation on the group of point cloud data, the processing module 430 may use the point cloud blocks as processing units. Further, the processing module 430 may identify at least one object from the group of point cloud data based on the one or more point cloud clusters. In some embodiments, the at least one object may include a vehicle, a pedestrian, a building, an obstacle, or the like, or a combination thereof. In some embodiments, after identifying the at least one object, the processing module 430 may determine a driving path for the subject (e.g., the vehicle 110) based on the at least one object. More descriptions of the processing of the point cloud data may be found elsewhere in the present disclosure (e.g., FIG. 5 and the descriptions thereof) .

The modules in the processing device 122 may be connected to or communicate with each other via a wired connection or a wireless connection. The wired connection may include a metal cable, an optical cable, a hybrid cable, or the like, or any combination thereof. The wireless connection may include a Local Area Network (LAN) , a Wide Area Network (WAN) , a Bluetooth, a ZigBee, a Near Field Communication (NFC) , or the like, or any combination thereof. Two or more of the modules may be combined as a single module, and any one of the modules may be divided into two or more units. For example, the processing device 122 may also include a transmission module (not shown) configured to transmit signals (e.g., electrical signals, electromagnetic signals) to one or more control components (e.g., a steering device, an accelerator, a braking device) of the subject (e.g., the vehicle 110) to direct the subject (e.g., the vehicle 110) to follow the driving path. As another example, the processing module 430 may include a determination unit (not shown) configured to determine the driving path for the subject (e.g., the vehicle 110) based on information (e.g., velocity information, position information, acceleration information) associated with the at least one object. As a further example, the processing device 122 may include a storage module (not shown) used to store information and/or data (e.g., the point cloud data, the map associated with the point cloud data, the plurality of groups of point cloud data) associated with the autonomous driving.

FIG. 5 is a flowchart illustrating an exemplary process for processing point cloud data according to some embodiments of the present disclosure. In some embodiments, the process 500 may be implemented as a set of instructions (e.g., an application) stored in the storage ROM 230 or RAM 240. The processor 220 and/or the modules in FIG. 4 may execute the set of instructions, and when executing the instructions, the processor 220 and/or the modules may be configured to perform the process 500. The operations of the illustrated process presented below are intended to be illustrative. In some embodiments, the process 500 may be accomplished with one or more additional operations not described and/or without one or more of the operations herein discussed. Additionally, the order in which the operations of the process as illustrated in FIG. 5 and described below is not intended to be limiting.

In 510, the processing device 122 (e.g., the receiving module 410) (e.g., the interface circuits of the processor 220) may receive point cloud data captured by a sensor device (e.g., a Lidar device) associated with a subject (e.g., the vehicle 110) . The processing device 122 may receive the point cloud data from the sensor device via the network 150.

As used herein, the point cloud data may include a set of data points associated with one or more objects within a predetermined range of the subject (e.g., the vehicle 110) . The predetermined range may be a default setting of the autonomous driving system 100 or may be adjustable under different situations. For example, the predetermined range may depend on (or partially depend on) a scanning range of the sensor device (e.g., the Lidar device) . The one or more objects may include a vehicle, a pedestrian, a building, an obstacle, or the like, or any combination thereof. The data point of the point cloud data may correspond to a physical point or region of an object in a space around an estimated location of the subject.

In some embodiments, as described in connection with FIG. 1, the sensor device (e.g., the Lidar device) may emit laser pulses to scan a surrounding environment of the subject. The laser pulses may be reflected by physical points in the surrounding environment and return to the sensor device. The sensor device may generate the point cloud data representative of the surrounding environment based on one or more characteristics of the return laser pulses. In some embodiments, the point cloud data may be captured according to a time period (e.g., 10 milliseconds, 100 milliseconds, 1 second, 2 seconds) when the subject (e.g., the vehicle 110) stops or travels along a road. During the acquisition of the point cloud data, the sensor device may rotate in a scanning angle range (e.g., 360 degrees, 180 degrees, 120 degrees) and scan the surrounding environment in a certain scanning frequency (e.g., 10Hz, 15Hz, 20 Hz) .

In some embodiments, the point cloud data may include at least one feature value of at least one feature of the physical points. Exemplary features of a physical point may include a location (e.g., a geographic position, a relative position with respect to the sensor device) of the physical point, an intensity (e.g., a return strength of the laser pulses emitted from the sensor device and reflected by the physical point) of the physical point, a classification (e.g., a type) of the physical point, a scan direction (e.g., a direction in which a scanning mirror of the sensor device was directed to when a corresponding data point was detected) associated with the physical point, or the like, or any combination thereof.

In 520, the processing device 122 (e.g., the division module 420) (e.g., the processing circuits of the processor 220) may divide the point cloud data into a plurality of groups of point cloud data based on a map associated with the point cloud data.

As used herein, the map may be a map presenting driving assistance information associated with a geographic region, such as a representation of a road network, for example, roads, intersections, traffic signals, lane rules, etc. As described above, a range of the geographic region may be the same as or larger than the predetermined range where the point cloud data is captured. A shape of the geographic region may be a regular triangle, a rectangle, a square, a regular hexagon, a circle, etc. For example, the shape of the geographic region may be a rectangle with a size of M meters × N meters, wherein M and N may be positive numbers (e.g., 5, 10, 20, 50, 100, 500) . In some embodiments, the map may be a three-dimensional (3D) map, a two-dimensional (2D) map, a four-dimensional (4D) map, etc.

In some embodiments, the map may be a high-definition map containing information of an accuracy of a centimeter level or a millimeter level. In some embodiments, the high-definition map may be generated online or offline. For example, the high-definition map may be generated offline based on data (e.g., point cloud data) captured by a plurality of detection units (e.g., the detection units described in FIG. 1) installed on a test vehicle which is used to execute a measurement trip. As the test vehicle moves along a road, the plurality of detection units may generate point cloud data associated with a surrounding environment of the test vehicle. Further, a processing device (e.g., the processing device 122) may generate a plurality of high-definition maps corresponding to different geographic regions based on the point cloud data and store the plurality of high-definition maps in a storage device (e.g., the storage device 140) of the autonomous driving system 100. Accordingly, the processing device 122 may access the storage device and retrieve a corresponding high-definition map based on an estimated location of the subject.

In some situations, the point cloud data associated with different objects (e.g., a vehicle on a road and an obstacle which is located in the vicinity of the vehicle and outside the road, a first vehicle on a left lane of a road and a second vehicle which is located in the vicinity of the first vehicle and on a right lane of the road, an obstacle on a road and a pedestrian which is located in the vicinity of the obstacle and on a sidewalk) may be almost connected together, for example, when a vehicle is driving along an outermost edge of a road, point cloud data associated with the vehicle may be almost connected with point cloud data associated with a flower bed which is located outside the road and in the vicinity of the outermost edge of the road, which may result in that an error may occur in the processing of the point cloud data (e.g., in the identification of the objects) . In order to reduce the interference, the processing device 122 may divide the point cloud data into a plurality of groups of point cloud data based on the map associated with point cloud data.

For example, the processing device 122 may divide the point cloud data into a group of point cloud data associated with objects (e.g., a vehicle) on a road and a group of point cloud data associated with objects (e.g., a flower bed, a tree, a pole, a road railing, a building) outside the road based on a road boundary. As another example, the processing device 122 may divide the point cloud data into a group of point cloud data associated with objects (e.g., a vehicle, an obstacle) on a road and a group of point cloud data associated with objects (e.g., a pedestrian) on a sidewalk based on a line (e.g., an edge line) on the sidewalk. As a further example, the processing device 122 may divide the point cloud data into a group of point cloud data associated with objects on a left lane of a road and a group of point cloud data associated with objects on a right lane of the road based on a lane line. More descriptions of the division of the point cloud data may be found elsewhere in the present disclosure (e.g., FIG. 6 and the descriptions thereof) .

In 530, the processing device 122 (e.g., the processing module 430) (e.g., the processing circuits of the processor 220) may process the plurality of groups of point cloud data. In some embodiments, the processing device 122 may segment the point cloud data into a plurality of point cloud blocks and process the plurality of groups of point cloud data based on the point cloud blocks.

In some embodiments, the processing device 122 may specify a plurality of grids on a two-dimensional plane corresponding to the map and join the plurality of groups of point cloud data with the plurality of grids. In some embodiments, a shape of the plurality of grids may include a quadrangle, a hexagon, an irregular polygon, or the like, or a combination thereof. In some embodiments, a size of the plurality of grids may be default setting (e.g., 20 cm × 20 cm) of the autonomous driving system 100 or may be adjustable under different situations. Further, for each of the plurality of grids, the processing device 122 may integrate data points (which may be expressed as multiple data records) within the grid as a point cloud block (which may be expressed as a single data record) . Accordingly, the large amount of data points of the point cloud data can be integrated as a plurality of point cloud blocks corresponding to the plurality of grids respectively, thereby speeding up subsequent data reading and/or data processing.

In some embodiments, the processing device 122 may determine the plurality of point cloud blocks according to a geohash algorithm. For each of the data points of the point cloud data, the processing device 122 may obtain a longitude and a latitude of the data point and determine a character string (also referred to as a geohash value) based on the longitude and the latitude according to the geohash algorithm. For any two data points, the more similar the character strings corresponding to the two data points are, the smaller the distance between the two data points may be. Further, the processing device 122 may determine the plurality of point cloud blocks based on the character strings corresponding to the data points. For example, the processing device 122 may integrate data points within a region (e.g., a rectangle region, which is similar to the grid to a certain extent) as a point cloud block, which may be expressed as a same character string corresponding to the data points within the region. Similar to the above, the large amount of data points of the point cloud data can be integrated as a plurality of point cloud blocks corresponding to the plurality of regions respectively, thereby speeding up subsequent data reading and/or data processing.

In some embodiments, for each of the plurality of groups of point cloud data, the processing device 122 may perform an aggregation operation on the group of point cloud data and determine one or more point cloud clusters based on the aggregation operation. When performing the aggregation operation on the group of point cloud data, the processing device 122 may use the point cloud blocks as processing units, which can improve processing speed and reduce processing time. Further, the processing device 122 may identify at least one object from the group of point cloud data based on the one or more point cloud clusters. In some embodiments, the at least one object may include a vehicle, a pedestrian, a building, an obstacle, or the like, or a combination thereof. In some embodiments, the processing device 122 may identify the at least one object based on an object identification method. Exemplary object identification method may include an object recognition method based on local features, an object recognition method based on global features, an object recognition method based on a graph matching, an object recognition algorithm based on a machine learning, or the like, or a combination thereof.

In some embodiments, after identifying the at least one object, the processing device 122 (e.g., the processing module 430) (e.g., the processing circuits of the processor 220) may determine a driving path for the subject (e.g., the vehicle 110) based on information (e.g., velocity information, position information, acceleration information) associated with the at least one object and transmit signals (e.g., electrical signals, electromagnetic signals) to one or more control components (e.g., a steering device, an accelerator, a braking device) of the subject (e.g., the vehicle 110) to direct the subject (e.g., the vehicle 110) to follow the driving path.

It should be noted that the above description is merely provided for the purposes of illustration, and not intended to limit the scope of the present disclosure. For persons having ordinary skills in the art, multiple variations or modifications may be made under the teachings of the present disclosure. However, those variations and modifications do not depart from the scope of the present disclosure.

For example, one or more other optional operations (e.g., a storing operation) may be added elsewhere in the process 500. In the storing operation, the processing device 122 may store information and/or data (e.g., the point cloud data, the map associated with the point cloud data, the plurality of groups of point cloud data) associated with the autonomous driving in a storage device (e.g., the storage device 140) disclosed elsewhere in the present disclosure. As another example, operation 510 and operation 520 may be combined into a single operation in which the processing device 122 may both receive the point cloud data captured by the sensor device and divide the point cloud data into the plurality of groups of point cloud data. As a further example, the processing device 112 may receive the point cloud data from the storage device 140 or the terminal device 130 via the network 150. As still a further example, the processing device 122 may segment the point cloud data into the plurality of point cloud blocks (e.g., the point clout blocks corresponding to the plurality of grids) before dividing the point cloud into the plurality of groups of point cloud data.

FIG. 6 is a flowchart illustrating an exemplary process for dividing point cloud data into a plurality of groups of point cloud data according to some embodiments of the present disclosure. In some embodiments, the process 600 may be implemented as a set of instructions (e.g., an application) stored in the storage ROM 230 or RAM 240. The processor 220 and/or the modules in FIG. 4 may execute the set of instructions, and when executing the instructions, the processor 220 and/or the modules may be configured to perform the process 600. The operations of the illustrated process presented below are intended to be illustrative. In some embodiments, the process 600 may be accomplished with one or more additional operations not described and/or without one or more of the operations herein discussed. Additionally, the order in which the operations of the process as illustrated in FIG. 6 and described below is not intended to be limiting.

In 610, the processing device 122 (e.g., the division module 420) (e.g., the processing circuits of the processor 220) may project the point cloud data onto a two-dimensional plane corresponding to the map.

In some embodiments, as described in connection with operation 510, the map may be a three-dimensional map which can be represented in a three-dimensional rectangular coordinate system including an X-axis, a Y-axis, and a Z-axis. In this situation, the two-dimensional plane may be the X-Y plane corresponding to the geographic region of the three-dimensional map.

In some embodiments, the processing device 122 may project the point cloud data onto the two-dimensional plane according to a projection method, for example, a central projection method, a parallel projection method, etc. For a physical point corresponding to a data point of the point cloud data, the physical point corresponds to a projected point on the X-Y plane with a coordinate (x, y) , wherein x may refer to a longitude of the physical point and y may refer to a latitude of the physical point.

In 620, the processing device 122 (e.g., the division module 420) (e.g., the processing circuits of the processor 220) may identify at least one reference line on the two-dimensional plane based on the map. In some embodiments, the at least one reference line may include a road boundary, a lane line, a line (e.g., a center line, an edge line) on a sidewalk, or the like, or a combination thereof. As described elsewhere in the present disclosure, the map may be a high-definition map, accordingly, the reference line may be a line with a high-definition (e.g., an accuracy of a centimeter level or a millimeter level) . In some embodiments, the reference line may be a straight line or a curve.

In 630, the processing device 122 (e.g., the division module 420) (e.g., the processing circuits of the processor 220) may divide the projected point cloud data into a plurality of groups of point cloud data based on the at least one reference line on the two-dimensional plane.

For example, according to the road boundary, the processing device 122 may divide the projected point cloud data into a group of point cloud data projected in a region on a road and a group of point cloud data projected in a region outside the road. As another example, according to the lane line, the processing device 122 may divide the projected point cloud data into a group of point cloud data projected in a region located in a left lane of the road and a group of point cloud data projected in a region located in a right lane of the road. As a further example, according to the edge line of the sidewalk, the processing device 122 may divide the projected point cloud data into a group of point cloud data projected in a region located in the sidewalk and a group of point cloud data projected in a region located outside the sidewalk. More descriptions of the division of the projected point cloud data may be found elsewhere in the present disclosure (e.g., FIGs. 7-A and 7-B and the descriptions thereof) .

In some embodiments, after dividing the projected point cloud data into the plurality of groups of point cloud data, the processing device 122 may project the plurality of groups of point cloud data back to the three-dimensional space for further processing (e.g., identifying objects based on the point cloud data) .

It should be noted that the above description is merely provided for the purposes of illustration, and not intended to limit the scope of the present disclosure. For persons having ordinary skills in the art, multiple variations or modifications may be made under the teachings of the present disclosure. However, those variations and modifications do not depart from the scope of the present disclosure. For example, one or more other optional operations (e.g., a storing operation) may be added elsewhere in the process 600. In the storing operation, the processing device 122 may store information and/or data (e.g., the two-dimensional plane, the at least one reference line) associated with the division of the projected point cloud data in a storage device (e.g., the storage device 140) disclosed elsewhere in the present disclosure. As another example, operation 610 and operation 620 may be combined into a single operation in which the processing device 112 may both project the point cloud data onto the two-dimensional plane corresponding to the map and identify the at least one reference line on the two-dimensional plane.

FIG. 7-A is a schematic diagram illustrating exemplary reference lines on a map according to some embodiments of the present disclosure. As illustrated in FIG. 7-A, the detecting units 112 of the vehicle 110 may capture point cloud data associated with one or more objects (e.g., a vehicle 710, a pedestrian (e.g., 721, 722) , a building (e.g., a bus stop 731, a flower bed 732) , an obstacle (e.g., a stone 740) ) within a predetermined range from the vehicle 110. As described in connection with operation 520, after receiving the point cloud data from the detecting units 112, the processing device 122 may obtain a high-definition map 700 (for brevity, a two-dimensional plane corresponding to the map is shown) from a storage device (e.g., the storage device 140 or an external data resource) , which presents driving assistance information associated with a geographic region the same as or larger than the predetermined range from the vehicle 110. Further, as described in connection with FIG. 6, the processing device 122 may divide the point cloud data into a plurality of groups of point cloud data based on at least one reference line on the map. As illustrated, the at least one reference line may include a road boundary (e.g., a right boundary 751, a left boundary 752) , a lane line 760, a line (e.g., a left edge line 771, a lower edge line 772, a center line 773, an upper edge line 774, a right edge line 775) on a sidewalk, etc.

FIG. 7-B is a schematic diagram illustrating an exemplary process for dividing the point cloud data into a plurality of groups of point cloud data according to some embodiments of the present disclosure. As illustrated in FIG. 7-B, the processing device 122 may project the point cloud data associated with the one or more objects (e.g., the vehicle 710, the pedestrian (e.g., 721, 722) , the building (e.g., the bus stop 731, the flower bed 732) , the obstacle (e.g., the stone 740) illustrated in FIG. 7-A) onto the two-dimensional plane 700 and obtain the projected point cloud data (e.g., 710’, 721’, 722’, 731’, 732’, 740’) corresponding to the one or more objects. It can be seen that part of the projected point cloud data is partially connected together (e.g., 710’ and 732’, 721’ and 740’) , which may result in an error in the processing of the point cloud data, for example, the vehicle 710 and the flower bed 732 may not be accurately identified.

Therefore, according to the at least one reference line on the map, the processing device 122 divides the projected point cloud data into a plurality of groups of point cloud data, for example, a first group of point cloud data (e.g., point cloud data within a quadrangle formed by the four edge lines of the sidewalk) , a second group of point cloud data (e.g., point cloud data within a quadrangle formed by the right boundary 751, a right portion of the lower edge line 775 of the sidewalk, and the lane line 760) , a third group of point cloud data (e.g., point cloud data within a quadrangle formed by the left boundary 752, a left portion of the lower edge line 775 of the sidewalk, and the lane line 760) , a fourth group of point cloud data (e.g., point cloud data within a region outside the road) , etc.

FIG. 8 is a schematic diagram illustrating an exemplary aggregation operation based on point cloud blocks according to some embodiments of the present disclosure. As described in connection with operation 530, the processing device 122 may determine a plurality of point cloud blocks corresponding to a plurality of grids on the two-dimensional plane corresponding to the map and perform an aggregation operation on each of the plurality of groups of the point cloud data based on the plurality of point cloud blocks. For brevity, only a portion of the plurality of groups of point cloud data is shown.

As illustrated in FIG. 8, a shape of the plurality of grids may be a square. The processing device 122 may join the point cloud data (which includes a plurality of data points) with the plurality of grids. Further, when performing the aggregation operation on the point cloud data, the processing device 122 may use the point cloud blocks as processing units. For example, for the group of point cloud data projected in a region on a road, the processing device 122 may access data records corresponding to point cloud blocks corresponding to grids represented by a solid box 802, perform the aggregation operation on the point cloud blocks, and identify a vehicle (e.g., the vehicle 710) based on the aggregation operation. As another example, for the group of point cloud data projected in a region outside the road, the processing device 122 may access data records corresponding to point cloud blocks corresponding to grids represented by a dashed box 804, perform the aggregation operation on the point cloud blocks, and identify a flower bed (e.g., the flower bed 732) based on the aggregation operation.

Having thus described the basic concepts, it may be rather apparent to those skilled in the art after reading this detailed disclosure that the foregoing detailed disclosure is intended to be presented by way of example only and is not limiting. Various alterations, improvements, and modifications may occur and are intended to those skilled in the art, though not expressly stated herein. These alterations, improvements, and modifications are intended to be suggested by this disclosure, and are within the spirit and scope of the exemplary embodiments of this disclosure.

Moreover, certain terminology has been used to describe embodiments of the present disclosure. For example, the terms “one embodiment, ” “an embodiment, ” and/or “some embodiments” mean that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Therefore, it is emphasized and should be appreciated that two or more references to “an embodiment” or “one embodiment” or “an alternative embodiment” in various portions of this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures or characteristics may be combined as suitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects of the present disclosure may be illustrated and described herein in any of a number of patentable classes or context including any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof. Accordingly, aspects of the present disclosure may be implemented entirely hardware, entirely software (including firmware, resident software, micro-code, etc. ) or combining software and hardware implementation that may all generally be referred to herein as a “unit, ” “module, ” or “system. ” Furthermore, aspects of the present disclosure may take the form of a computer program product embodied in one or more computer readable media having computer readable program code embodied thereon.

A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including electro-magnetic, optical, or the like, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that may communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable signal medium may be transmitted using any appropriate medium, including wireless, wireline, optical fiber cable, RF, or the like, or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET, Python or the like, conventional procedural programming languages, such as the "C" programming language, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages such as Python, Ruby and Groovy, or other programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN) , or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider) or in a cloud computing environment or offered as a service such as a Software as a Service (SaaS) .

Furthermore, the recited order of processing elements or sequences, or the use of numbers, letters, or other designations therefore, is not intended to limit the claimed processes and methods to any order except as may be specified in the claims. Although the above disclosure discusses through various examples what is currently considered to be a variety of useful embodiments of the disclosure, it is to be understood that such detail is solely for that purpose, and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover modifications and equivalent arrangements that are within the spirit and scope of the disclosed embodiments. For example, although the implementation of various components described above may be embodied in a hardware device, it may also be implemented as a software only solution, e.g., an installation on an existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description of embodiments of the present disclosure, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the various embodiments. This method of disclosure, however, is not to be interpreted as reflecting an intention that the claimed subject matter requires more features than are expressly recited in each claim. Rather, claimed subject matter may lie in less than all features of a single foregoing disclosed embodiment.

Claims

A system for autonomous driving, comprising:

a storage medium to store a set of instructions; and

a processor, communicatively coupled to the storage medium, to execute the set of instructions to:

receive point cloud data captured by a sensor device;

divide, based on a map associated with the point cloud data, the point cloud data into a plurality of groups of point cloud data; and

process the plurality of groups of point cloud data.
The system of claim 1, wherein the sensor device comprises a light detection and ranging (Lidar) device.
The system of claim 1 or claim 2, wherein to divide, based on the map associated with the point cloud data, the point cloud data into the plurality of groups of point cloud data, the processor is to:

project the point cloud data onto a two-dimensional plane corresponding to the map;

identify, based on the map, at least one reference line on the two-dimensional plane; and

divide the projected point cloud data into the plurality of groups of point cloud data based on the at least one reference line on the two-dimensional plane.
The system of claim 3, wherein the at least one reference line comprises at least one of a road boundary, a lane line, or a sidewalk.
The system of any of claims 1-4, wherein to process the plurality of groups of point cloud data, the processor is to:

for each of the plurality of groups of point cloud data,

perform an aggregation operation on the group of point cloud data; and

identify at least one object from the group of point cloud data based on the aggregation operation.
The system of claim 5, wherein to perform the aggregation operation on the group of point cloud data, the processor is to:

perform the aggregation operation on the group of point cloud data based on a plurality of point cloud blocks corresponding to a plurality of grids.
The system of claim 5 or claim 6, wherein the at least one object comprises at least one of a vehicle, a pedestrian, a building, or an obstacle.
The system of any of claims 5-7, the processor is further to:

determine a driving path for the vehicle based on the at least one object; and

transmit signals to one or more control components of the vehicle to direct the vehicle to follow the driving path.
The system of any of claims 1-8, wherein the map contains information of an accuracy of a centimeter level or a millimeter level.
The system of any of claims 1-9, wherein the point cloud data are captured by the sensor device within a predetermined range from a vehicle.
A method implemented on a computing device including at least one processor, at least one storage medium, and a communication platform connected to a network, the method comprising:

receiving point cloud data captured by a sensor device;

dividing, based on a map associated with the point cloud data, the point cloud data into a plurality of groups of point cloud data; and

processing the plurality of groups of point cloud data.
The method of claim 11, wherein the sensor device comprises a light detection and ranging (Lidar) device.
The method of claim 11 or claim 12, wherein the dividing, based on the map associated with the point cloud data, the point cloud data into the plurality of groups of point cloud data comprises:

projecting the point cloud data onto a two-dimensional plane corresponding to the map;

identifying, based on the map, at least one reference line on the two-dimensional plane; and

dividing the projected point cloud data into the plurality of groups of point cloud data based on the at least one reference line on the two-dimensional plane.
The method of claim 13, wherein the at least one reference line comprises at least one of a road boundary, a lane line, or a sidewalk.
The method of any of claims 11-14, wherein the processing the plurality of groups of point cloud data comprises:

for each of the plurality of groups of point cloud data,

performing an aggregation operation on the group of point cloud data; and

identifying at least one object from the group of point cloud data based on the aggregation operation.
The method of claim 15, wherein the performing the aggregation operation on the group of point cloud data comprises:

performing the aggregation operation on the group of point cloud data based on a plurality of point cloud blocks corresponding to a plurality of grids.
The method of claim 15 or claim 16, wherein the at least one object comprises at least one of a vehicle, a pedestrian, a building, or an obstacle.
The method of any of claims 15-17, wherein the method further comprises:

determining a driving path for the vehicle based on the at least one object; and

transmitting signals to one or more control components of the vehicle to direct the vehicle to follow the driving path.
The method of any of claims 11-18, wherein the map contains information of an accuracy of a centimeter level or a millimeter level.
The method of any of claims 11-19, wherein the point cloud data are captured by the sensor device within a predetermined range from a vehicle.
A vehicle configured for autonomous driving, comprising:

a detecting component, a planning component, and a control component, wherein the planning component is configured to:

receive point cloud data captured by the detecting component;

divide, based on a map associated with the point cloud data, the point cloud data into a plurality of groups of point cloud data; and

process the plurality of groups of point cloud data.
The vehicle of claim 21, wherein the detecting component comprises a light detection and ranging (Lidar) device.
The vehicle of claim 21 or claim 22, wherein to divide, based on the map associated with the point cloud data, the point cloud data into the plurality of groups of point cloud data, the planning component is configured to:

project the point cloud data onto a two-dimensional plane corresponding to the map;

identify, based on the map, at least one reference line on the two-dimensional plane; and

divide the projected point cloud data into the plurality of groups of point cloud data based on the at least one reference line on the two-dimensional plane.
The vehicle of claim 23, wherein the at least one reference line comprises at least one of a road boundary, a lane line, or a sidewalk.
The vehicle of any of claims 21-24, wherein to process the plurality of groups of point cloud data, the planning component is configured to:

for each of the plurality of groups of point cloud data,

perform an aggregation operation on the group of point cloud data; and

identify at least one object from the group of point cloud data based on the aggregation operation.
The vehicle of claim 25, wherein to perform the aggregation operation on the group of point cloud data, the planning component is configured to:

perform the aggregation operation on the group of point cloud data based on a plurality of point cloud blocks corresponding to a plurality of grids.
The vehicle of claim 25 or claim 26, wherein the at least one object comprises at least one of a vehicle, a pedestrian, a building, or an obstacle.
The vehicle of any of claims 25-27, wherein the planning component is further configured to:

determine a driving path for the vehicle based on the at least one object; and

transmit signals to one or more control components of the vehicle to direct the vehicle to follow the driving path.
The vehicle of any of claims 21-28, wherein the map contains information of an accuracy of a centimeter level or a millimeter level.
The vehicle of any of claims 21-29, wherein the point cloud data are captured by the detecting component within a predetermined range from a vehicle.
A system for autonomous driving, comprising:

a receiving module configured to receive point cloud data captured by a sensor device;

a division module configured to divide, based on a map associated with the point cloud data, the point cloud data into a plurality of groups of point cloud data; and

a processing module configured to process the plurality of groups of point cloud data.
A non-transitory computer readable medium, comprising executable instructions that, when executed by at least one processor, direct the at least one processor to perform a method, the method comprising:

receiving point cloud data captured by a sensor device;

dividing, based on a map associated with the point cloud data, the point cloud data into a plurality of groups of point cloud data; and

processing the plurality of groups of point cloud data.