CN115257813B

CN115257813B - Intelligent driving control method through construction barrier and vehicle

Info

Publication number: CN115257813B
Application number: CN202210999747.7A
Authority: CN
Inventors: 刘婷; 褚永强
Original assignee: Zhiji Automobile Technology Co Ltd
Current assignee: Zhiji Automobile Technology Co Ltd
Priority date: 2022-08-19
Filing date: 2022-08-19
Publication date: 2024-07-09
Anticipated expiration: 2042-08-19
Also published as: CN115257813A

Abstract

The invention discloses an intelligent driving control method through construction obstacles and a vehicle, wherein the method comprises the steps of judging whether a current driving path enters a construction road section, if so, acquiring position coordinates of the construction obstacle nearest to a vehicle according to vehicle-mounted sensing information; the method comprises the steps of extracting a rule control track line in an intelligent driving system, wherein the rule control track line is generated by the intelligent driving system according to a driving environment; calculating the position relation between the gauge control track line and the nearest construction obstacle away from the vehicle; and determining a confidence coefficient score based on the position relation, and reminding a user to pay attention to the road condition when the confidence coefficient score is lower than a reminding threshold value. According to the traffic confidence of the self-vehicle driving and the construction obstacle, the system capacity of the support system for processing the construction obstacle scene is timely informed to the driver, the user is reminded of taking over the driving, and the problem that the user encounters the condition that the construction scene is not taken over timely in the intelligent driving process is solved.

Description

Intelligent driving control method through construction barrier and vehicle

Technical Field

The invention relates to the field of intelligent driving, in particular to an intelligent driving control method through construction obstacles and a vehicle.

Background

At present, the intelligent driving technology is limited, and when a traffic cone and other construction protection road sections are met, the situation that obstacles are identified by mistake or are not identified by mistake is caused, so that an intelligent driving system cannot accurately change the road or stop the road in advance before the construction and other obstacles are caused, a driver is panicked, and a serious driver causes traffic accidents; in addition, the problem that manual takeover is not timely because the effective early warning can not be carried out on a driver can also be generated.

The prior art is therefore still in need of further development.

Disclosure of Invention

Aiming at the technical problems, the invention provides an intelligent driving control method through construction obstacles and a vehicle.

In a first aspect of the present invention, there is provided an intelligent driving control method through a construction barrier, comprising:

judging whether the current running path enters a construction road section, if so, acquiring the position coordinates of a construction barrier nearest to the vehicle according to vehicle-mounted sensing information;

the method comprises the steps of extracting a rule control track line in an intelligent driving system, wherein the rule control track line is generated by the intelligent driving system according to a driving environment;

calculating the position relation between the gauge control track line and the nearest construction obstacle away from the vehicle;

And determining a confidence coefficient score based on the position relation, and reminding a user to pay attention to the road condition when the confidence coefficient score is lower than a reminding threshold value.

In a second aspect of the present invention, there is provided an intelligent driving control method through a construction barrier, comprising:

Judging whether the current driving path enters a construction road section, if so, acquiring position coordinates from a construction barrier according to vehicle-mounted sensing information;

Calculating waiting time of autonomous lane change driving of the own vehicle, and determining a first confidence score based on the waiting time;

Calculating collision time of the vehicle and the construction obstacle, and determining a second confidence score based on the collision time;

Comparing the first confidence score with the second confidence score, taking the confidence score of the minimum value as the confidence score of the intelligent driving control, and reminding a user to pay attention to road conditions when the confidence score of the intelligent driving control is lower than a reminding threshold value.

In a third aspect the present invention provides a vehicle comprising a processor, a memory and a computer program stored on the memory and capable of running on the processor, the computer program when executed by the processor implementing a method according to the first or second aspect of an embodiment of the invention.

In a fourth aspect of the invention, a computer-readable storage medium is provided, on which a computer program is stored which, when run by a computer, performs the method according to the first or second aspect of the embodiments of the invention.

According to the traffic confidence of the self-vehicle driving and the construction obstacle, the system capacity of the support system for processing the construction obstacle scene is timely informed to the driver, the user is reminded of taking over the driving, and the problem that the user encounters the condition that the construction scene is not taken over timely in the intelligent driving process is solved.

Drawings

FIG. 1 is a schematic diagram of a network structure of a vehicle intelligent driving system according to an embodiment of the present invention;

FIG. 2 is a schematic diagram of a vehicle control function according to an embodiment of the present invention;

FIG. 3 is a schematic flow chart of an intelligent driving control method through construction obstacles in an embodiment of the invention;

FIG. 4 is a schematic view of a control track line of a vehicle greater than one lane width according to an embodiment of the present invention;

FIG. 5 is a schematic illustration of a control track line of a vehicle smaller than one lane width and larger than half lane width according to an embodiment of the present invention;

FIG. 6 is a schematic diagram of a control track line of a vehicle smaller than a lane width according to an embodiment of the present invention;

Fig. 7 is a flow chart of another intelligent driving control method through construction obstacles in an embodiment of the invention.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

Reference in the specification to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment.

The intelligent driving system may make driving decisions using intelligent perception calculations in response to the obstacle. The current mainstream intelligent driving system only has two gears to control the capacity state of the system, and only reminds the driver to take over through emergency alarm when the intelligent driving system exits under dangerous working conditions to carry out manual driving. Aiming at a complex scene of a construction environment, a driver is not early warned in advance according to the specific environment, and traffic accidents caused by untimely taking over are easy to occur. For example, if the intelligent driving system senses that the obstacle is a cone, but the current road driving environment is complex, the vehicle frequently changes lanes, the driving difficulty is increased, the intelligent driving system cannot complete braking or lane changing in time, and the driver is not sufficiently reminded.

FIG. 1 is a block diagram of the partial components of an intelligent drive system according to one embodiment of the present invention. Referring to fig. 1, the intelligent drive system includes a host vehicle 101 that may be coupled to servers 103, 104 through a network 102. Network 102 may be, for example, a Local Area Network (LAN), wide Area Network (WAN) (such as the Internet, cellular network, satellite network, or a combination thereof). The servers 103, 104 may be any kind of server or cluster of servers, such as Web or cloud servers, application servers, backend servers, or a combination thereof. The servers 103, 104 may be data analysis servers, content servers, traffic information servers, map and point of interest (MPOI) servers, or location servers, etc.

The host vehicle 101 refers to a vehicle configured to be in an autonomous driving mode in which the vehicle navigates with little or no driver intervention. The host vehicle 101 includes a perception system having one or more sensors configured to detect information about the driving environment of the vehicle therein. The host vehicle and its associated controller(s) use the detected information to navigate the drive. The host vehicle 101 may complete autonomous driving or assisted driving in a manual mode, a full autonomous mode, or a partial autonomous mode.

In one embodiment, the self-vehicle 101 includes, but is not limited to, a perception and planning system 110, a vehicle control system 111, a wireless communication system 112, a user interface system 113, and a sensor system 114. The host vehicle 101 also includes certain general-purpose components within the vehicle, such as an engine, braking system, chassis, transmission, power cells, etc., which may be signaled by the vehicle control system 111 and/or the perception and planning system 110 to control vehicle travel, such as acceleration, deceleration, steering, lane changes, etc., using various communication signals and/or commands.

The components of the host vehicle 101, such as 110-115, may be coupled to each other via a signal interconnect, a CAN bus, a network, a local area network, or a combination thereof. For example, 110-115 may be communicatively coupled to each other via a controller over a CAN bus. Among them, the CAN bus is a vehicle bus standard designed to allow microcontrollers and devices to communicate with each other in applications without a host, a message-based protocol.

Referring to fig. 2, sensor system 114 includes, but is not limited to, one or more cameras 211, a Global Positioning System (GPS) unit 212, an Inertial Measurement Unit (IMU) 213, a radar unit 214, and a lidar unit 215. The GPS system 212 may include a transceiver operable to provide information regarding the location of the vehicle. The IMU unit 213 may sense changes in the position and orientation of the vehicle based on inertial acceleration.

In some other embodiments, the sensor system 114 also includes other sensors, such as temperature sensors, wheel speed sensors, cam position sensors, crankshaft position sensors, pressure sensors, sonar sensors, infrared sensors, steering sensors, throttle sensors, brake sensors, and sound sensors (e.g., microphones), etc., to obtain information from vehicle conditions, etc. The steering sensor may be configured to sense a steering angle of a steering wheel, a vehicle wheel, or a combination thereof. The throttle sensor and the brake sensor sense a throttle position and a brake position of the vehicle, respectively. In some cases, the throttle sensor and the brake sensor may be integrated as an integrated throttle/brake sensor, and the controller may control the vehicle to accelerate or decelerate using the detected information. In some embodiments, any combination of sensors of the perception system (e.g., cameras, lidar, etc.) may be collected for detection of obstacles.

In one embodiment, the vehicle control system 111 includes, but is not limited to, a drive unit 201, a throttle unit 202 (also referred to as an acceleration unit), and a brake unit 203. The driving unit 201 is used to drive the vehicle forward direction. The throttle unit 202 is used to control the power output of a motor or engine, which in turn can control the speed and acceleration of the vehicle. The brake unit 203 is used to slow down the vehicle by providing a braked wheel. It should be understood that the implementation functions of the components shown in fig. 2 may be implemented in hardware, software, or a combination thereof.

The wireless communication system 112 in fig. 1 is used to allow communication between the host vehicle 101 and external systems (such as server devices, smart keys, other vehicles, etc.). For example, the wireless communication system 112 may communicate wirelessly with one or more mobile phone terminals directly or via a communication network, such as servers 103, 104 through network 102. The wireless communication system 112 may communicate with another component or system using any cellular communication network or Wireless Local Area Network (WLAN), for example using WiFi. The wireless communication system 112 commonly employs a Telematics-BOX, simply referred to as an onboard T-BOX. The wireless communication system 112 can be linked with the user interface system 113 to realize various functional interactions of the user; interaction with the host vehicle or server may be accomplished within the host vehicle 101 using, for example, a keyboard, touch screen display, microphone and speaker, etc.

Some or all of the functions of the cart 101 may be controlled by the perception and planning system 110. The perception and planning system 110 includes the necessary hardware (e.g., processor, memory, storage) and software (e.g., operating system, regulation program) to receive information from the sensor system 114, control system 111, wireless communication system 112, and/or user interface system 113, process the received information, plan driving routes, emergency avoidance, overtake, lane change, etc., and then travel based on the planning and control information. Alternatively, the perception and planning system 110 may be integrated with the vehicle control system 111 to form a central computing platform.

For example, the driver sets a start position and a destination of the trip based on the user interface system 113. The perception and planning system 110 may obtain location and navigation route information from the server 104. The server provides map services and POIs.

The perception and planning system 110 may also obtain real-time traffic information from the navigation service as the host vehicle 101 travels along the planned route. Based on the real-time traffic information, MPOI information and location information acquired, sensed by the sensor system 114, and real-time local environment data (e.g., obstacles, crowd, nearby vehicles), the awareness and planning system 110 may plan an optimal route, such as driving the vehicle 101 via the control system 111 according to the planned route, to safely and efficiently reach the specified destination.

The server 103 may be a service cluster that performs data analysis services, data storage, data analysis, etc. functions for various clients. In one embodiment, data analysis system 103 includes a data collector 121 and a machine learning engine 122. The data collector 121 collects driving statistics 123 from individual vehicles (motor vehicles or conventional vehicles driven by human drivers). The driving statistics 123 include information indicating issued driving commands (e.g., driving habits, commutes, etc.) and responses of the vehicle captured by the sensors of the vehicle at different points in time. The driving statistics 123 may also include information describing driving environments at different points in time, such as, for example, a driving route, road conditions, weather conditions, and the like.

Based on the driving statistics 123, the machine learning engine 122 can meet various intelligent demands, and complete training and application of the artificial intelligent model which is beneficial to intelligent driving. The algorithm model 124 may include functions to calculate the current position of the obstacle, identify the type of obstacle, a predicted trajectory for the obstacle, and a trajectory.

Further, by utilizing the modules, the navigation map, the high-precision map, the perception fusion information (such as sensor information of a radar, a camera and the like), the vehicle body data and part or all of the navigation data can be obtained, and the passing driving and corresponding reminding functions of traffic cones on the corresponding construction road can be completed.

Based on the sensor data provided by the sensor system 114 and the positioning information obtained by the GPS unit 212, and the traveling road conditions known by the navigation map, the host vehicle can learn whether to travel to the construction section, sense the relative host vehicle position of the traffic cone of the construction section, sense whether there are other obstacles, other vehicles, the traveling state of other vehicles, the current host vehicle lane position, etc., by the sensor system 114 sensing the surrounding environment of the road. In addition to the above description, the perceived information may include traffic lights, relative locations of other vehicles, pedestrians, buildings, crosswalks, or other traffic related signs (e.g., parking signs, yielding signs), etc.

The sensor system 114 includes a computer vision system or functionality of a computer vision system to process and analyze images captured by one or more cameras to identify objects and/or features in the environment of the host vehicle. The objects may include traffic signals, road boundaries, other vehicles, pedestrians, and/or other obstacles, etc. Computer vision systems may use object recognition algorithms, video tracking, and other computer vision techniques. In some embodiments, the computer vision system may map the environment, track the object, and estimate the speed of the object, etc.

A predicted trajectory of an obstacle that predicts a path of moving the obstacle in an area related to the current travel path. The predicted trajectory may be generated based on a current state of the moving obstacle (e.g., a speed, a position, a heading, an acceleration, or a type of the moving obstacle), map data, and traffic rules.

For example, the intelligent driving system may recognize an obstacle as a vehicle sensed to travel in a driving lane according to a forward direction and a position of the obstacle, and complete driving control such as overtaking, lane changing, idle running, and the like.

Intelligent driving systems are generally divided into two different functions, one is an intelligent driving system with an autonomous lane change function and the other is an intelligent driving system without an autonomous lane change function. For intelligent driving systems without an autonomous lane change function, tracking cruising, preceding vehicle following and the like are performed when automatic driving is performed. The processing capacity of the intelligent driving system when the construction obstacle is met is indicated by adopting different confidence degrees for the two intelligent driving systems, so that a driver is reminded. And calculating the control process of intelligent driving by using the probability of driving and obstacle avoidance as the confidence coefficient.

Referring to fig. 3, a flow chart of an intelligent driving control method through a construction obstacle is shown, which is suitable for an intelligent driving system without an autonomous lane change function. The process comprises the following steps:

Step 310: and judging whether the current driving path enters a construction road section, if so, acquiring the position coordinates of the construction barrier nearest to the vehicle according to the vehicle-mounted sensing information. The self-vehicle can acquire the traffic condition of the navigation route through the navigation function, so as to judge whether the self-vehicle runs into a construction road section or not; or by pushing the server 104, when the vehicle locates the adjacent construction section, the server 104 pushes information to the vehicle through the wireless communication system 112, and the vehicle can determine that the construction section has been entered according to the information.

The camera of the sensor system 114, the laser radar and the like can acquire images of the driving path and obstacle information, whether the traffic cone exists on the road ahead can be identified through an image identification technology, and the example and position coordinates of the traffic cone can be positioned by utilizing the point cloud data of the laser radar.

Step 320: and extracting a regulation track line in the intelligent driving system, wherein the regulation track line is generated by the intelligent driving system according to the driving environment. The sensing and planning system 110 and the control system 111 can control the vehicle to run according to the sensing of the navigation and sensor system 114, and the automatic driving can run according to the rule and track line. For example, the intelligent driving system plans the expected regulation track within 6 seconds in the future according to vision, obstacles, lane lines and vehicle body gestures, and updates the expected regulation track in real time. Based on this, the rule trace line can be extracted from the intelligent driving system, so that the relation between the rule trace line and the obstacle can be used for establishing confidence score calculation.

Step 330: and calculating the position relation between the rule track line and the nearest construction obstacle of the distance vehicle.

Step 340: and determining a confidence coefficient score based on the position relation, and reminding a user to pay attention to the road condition when the confidence coefficient score is lower than a reminding threshold value.

Since the coordinates P (a, b, c) of the construction barrier are known, the own vehicle can calculate the distance relationship with the construction barrier based on the coordinate system. As an embodiment, in the calculation process, when the lateral distance between the rule track line and the nearest construction obstacle of the own vehicle is smaller than half the width of the lane (the width is known), triggering a reminding event, and setting the confidence score to be 1 level, wherein the confidence score is lower than a reminding threshold value; the users are reminded of paying attention to road conditions in a part or all of the modes of words, sounds, visual animation, vibration and the like, so that dangers are avoided. On the contrary, when the transverse distance between the gauge track line and the nearest construction obstacle from the vehicle is not smaller than half of the lane width, the confidence coefficient can be set to be level 2 and level 3, and the confidence coefficient is higher than the reminding threshold value, so that a driver is not required to be reminded to take over driving, and the confidence coefficient can be displayed as display data. Wherein the confidence score may also be represented in terms of a numerical percentage, presented to the user. The numerical percentages represent less than 30% with very low confidence, more than 70% with high confidence, and between 30% and 70% with medium confidence.

Calculating the transverse distance between the gauge control track line and the nearest construction obstacle of the vehicle; the lateral distance is based on the lane width. When the transverse distance between the gauge track line and the nearest construction obstacle from the vehicle is smaller than half of the lane width, the confidence score is displayed as level 1; when the transverse distance between the gauge track line and the nearest construction obstacle from the vehicle is more than half the width of the lane and less than one width of the lane, the confidence score is displayed as level 2; when the transverse distance between the gauge track line and the nearest construction obstacle from the vehicle is larger than one lane width, the confidence score is displayed as level 3; wherein, the confidence score is lower than the reminding threshold value when the confidence score is 1 level.

Please refer to the confidence score illustrations of the vehicle traveling in different driving environments in conjunction with fig. 4-6. In fig. 4, three trolleys are three positions where the self-vehicle sequentially runs along the arrow direction, the traffic cone is located on the self-vehicle seat, the distance between the self-vehicle and the traffic cone is larger than one lane width, the confidence score is 3, and the traffic cone closest to the self-vehicle distance is approximately the same. In fig. 5, the lateral distance of the vehicle from the nearest traffic cone is different at different locations, but the lateral distance is between one lane width and half lane width, with a confidence score of 2 steps. In fig. 6, the vehicle is located in the middle lane, different trolleys indicate that the lateral distance between the vehicle and the nearest traffic cone is different at different positions, and other vehicles are located on the right side, and as the vehicle is continuously driven, the distance between the vehicle and the nearest traffic cone is gradually smaller than half the width of the lane, the confidence score is reduced to level 1, and the user needs to be reminded of focusing on the road condition under the confidence score.

In the above embodiment, the confidence score or the level corresponding to the confidence score is defined by using the lateral distance between the rule trace line and the traffic cone, and in other embodiments, the level corresponding to the confidence score or the level corresponding to the confidence score may be defined by using the distance relation between the front end or the side edge of the vehicle and the traffic cone.

The invention calculates the confidence score by utilizing the distance between the rule control track line of the self-vehicle running and the nearest obstacle (one of a plurality of traffic cones), triggers reminding when the confidence score is lower, takes over driving or pays attention to road conditions by the driver, can improve the efficiency of taking over driving by the driver, and is convenient for the driver to master the system processing capacity of the intelligent driving system.

Referring to fig. 7, a flow chart of another intelligent driving control method through construction obstacle is shown, which is applicable to intelligent driving system with autonomous lane change function, and the flow chart includes the following steps:

Step 710: and judging whether the current driving path enters a construction road section, and if so, acquiring position coordinates from the construction barrier according to the vehicle-mounted sensing information. The self-vehicle can acquire the traffic condition of the navigation route through the navigation function, so as to judge whether the self-vehicle runs into a construction road section or not; or by pushing the server 104, when the vehicle locates the adjacent construction section, the server 104 pushes information to the vehicle through the wireless communication system 112, and the vehicle can determine that the construction section has been entered according to the information. The camera of the sensor system 114, the laser radar and the like can acquire images of the driving path and obstacle information, whether the traffic cone exists on the road ahead can be identified through an image identification technology, and the example and position coordinates of the traffic cone can be positioned by utilizing the point cloud data of the laser radar.

Step 720: and calculating the waiting time of the self-vehicle autonomous lane change driving, and determining a first confidence score based on the waiting time. The autonomous channel change control mainly comprises dynamic channel change track planning and channel change track tracking control. The dynamic brain track planning method can acquire real-time information according to the V2V technology to update the track changing track, so that the vehicle is better adapted to the change of the motion state of surrounding vehicles. The lane change track tracking control calculates a desired speed and a heading angle (or yaw rate) required by the track mainly through deviation between an actual position and a desired position of the vehicle. The lane change is usually planned and executed by using lane change direction information, surrounding vehicle information, front image information, vehicle state and the like. Calculating a waiting time T, wherein the first confidence score is defined, for example, the waiting time T is smaller than a threshold value 1, the confidence score F (T) =3, the threshold value 1 is smaller than the waiting time T is smaller than a threshold value 2, and the confidence score F (T) =2; latency T > threshold 2, confidence F (T) =1 level.

Step 730: and calculating the collision time of the vehicle and the construction obstacle, and determining a second confidence score based on the collision time. The time of intersection with the contour edge of the construction barrier can be calculated from the velocity vector of the contour edge of the own vehicle and recorded as the collision time. Illustratively, the TTC (time to collision) times of the own vehicle, including the AEBS (ADVANCED EMERGENCY Braking System, emergency Braking assistance System), and the construction barrier are calculated, the predicted collision times of the own vehicle and the construction barrier can be calculated. Referring to the first confidence score, calculating to obtain collision time T, wherein T is less than a threshold value 1, and the confidence F (TTC) =3 levels; threshold 1 is less than or equal to T and less than threshold 2, confidence F (TTC) =2 level; t > threshold 2, confidence F (TTC) =level 1.

The threshold values 1 and 2 are understood to be 1 minute and 2 minutes; the threshold is of course variable and is not sufficient to limit the invention.

Step 740: comparing the first confidence score with the second confidence score, taking the confidence score of the minimum value as the confidence score of the intelligent driving control, and reminding a user to pay attention to road conditions when the confidence score of the intelligent driving control is lower than a reminding threshold value.

Specifically, the first confidence score and the second confidence score correspond to confidence levels. When one of the first confidence score and the second confidence score is smaller than a preset confidence level, the confidence score is lower than a reminding threshold value; and displaying the smaller one of the first confidence score and the second confidence score to a user. Further, while reminding, when the user takes over the driving system, the automatic driving mode is exited.

The first confidence score is 2 levels, the second confidence score is 1 level, the second confidence score is recorded as the confidence score of intelligent driving control, the second confidence score is 1 level, the reminding function is triggered, and the user is reminded of paying attention to the road condition in a part or all modes of characters, sounds, visual animation, vibration and the like, so that danger is avoided. Still further exemplary, the first confidence score is 2 levels, the second confidence score is 3 levels, the second confidence score is recorded as the confidence score of the intelligent driving control, and the second confidence score is 2, the reminding function is not triggered, and only the confidence score of the intelligent driving control can be displayed, so that the driver can know the control capability of the current intelligent driving system.

The invention calculates the confidence score by utilizing the lane changing time and the obstacle collision time of the self-vehicle driving, parameterizes the system capacity of the intelligent driving system, and is convenient for a driver to know the intelligent driving capacity state; when the confidence score is lower, the reminding is triggered, the driver takes over driving or pays attention to road conditions, the efficiency of taking over driving by the driver can be improved, and meanwhile, the driver can conveniently master the system processing capacity of the intelligent driving system.

Aiming at the auxiliary driving scene of road construction, the confidence score is introduced to express the system processing capacity of the intelligent driving system for the construction obstacle, so that a driver can fully know the current driving state, and the subjective judgment of the driver is facilitated. Compared with the direct system in the prior art, the method and the system for giving out the driving control right are easier for the driver to receive, and the man-machine co-driving experience is improved.

The invention also provides a vehicle comprising a processor, a memory and a computer program stored on the memory and capable of running on the processor, which when executed by the processor implements the steps of the intelligent driving control method as described above through a construction barrier.

The present invention also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the intelligent driving control method through a construction barrier as described above.

It is understood that the computer-readable storage medium may include: any entity or device capable of carrying a computer program, a recording medium, a USB flash disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a software distribution medium, and so forth. The computer program comprises computer program code. The computer program code may be in the form of source code, object code, executable files, or in some intermediate form, among others. The computer readable storage medium may include: any entity or device capable of carrying computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a software distribution medium, and so forth.

In some embodiments of the present invention, the apparatus may include a controller, which is a single-chip microcomputer chip, integrated with a processor, a memory, a communication module, etc. The processor may refer to a processor comprised by the controller. The Processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), off-the-shelf Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like.

Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and additional implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.

Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, in computer software, or in a combination of the two, and that the elements and steps of the examples have been generally described in terms of function in the foregoing description to provide a clear understanding of the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent driving control method through construction barriers is characterized by comprising the following steps:

The method comprises the steps of extracting a rule control track line in an intelligent driving system, wherein the rule control track line is generated by the intelligent driving system according to a driving environment; calculating the position relation between the rule track line and the nearest construction obstacle away from the vehicle, calculating the waiting time of the vehicle for self-lane change driving, determining a first confidence score based on the waiting time, calculating the collision time of the vehicle and the construction obstacle, and determining a second confidence score based on the collision time;

determining a confidence score based on the position relation, comparing the magnitudes of the first confidence score and the second confidence score, and taking the confidence score of the minimum value as the confidence score of intelligent driving control;

Reminding a user to pay attention to road conditions when the confidence score is lower than a reminding threshold value, wherein the confidence score is lower than the reminding threshold value when one of the first confidence score and the second confidence score is smaller than a preset confidence level; displaying the smaller one of the first confidence score and the second confidence score to a user; wherein the first confidence score and the second confidence score each correspond to a confidence level.

2. The intelligent driving control method through a construction barrier according to claim 1, wherein the calculating the positional relationship of the control trajectory line and the nearest construction barrier from the host vehicle includes: calculating the transverse distance between the gauge control track line and the nearest construction obstacle of the vehicle; the lateral distance is based on the lane width.

3. The intelligent driving control method through a construction obstacle according to claim 1, wherein the determining a confidence score based on the positional relationship comprises:

When the transverse distance between the rule track line and the nearest construction obstacle from the vehicle is smaller than half of the width of the lane, the confidence coefficient score is lower than a reminding threshold value;

when the transverse distance between the gauge track line and the nearest construction obstacle from the vehicle is not smaller than half of the width of the lane, the confidence coefficient score is higher than a reminding threshold value;

Wherein the confidence score is represented by a reminder level or a numerical percentage and presented to the user.

4. The intelligent driving control method through a construction barrier according to claim 3, wherein the determining a confidence score based on the positional relationship further comprises:

when the transverse distance between the gauge track line and the nearest construction obstacle from the vehicle is smaller than half of the lane width, the confidence score is displayed as level 1;

when the transverse distance between the gauge track line and the nearest construction obstacle from the vehicle is more than half the width of the lane and less than one width of the lane, the confidence score is displayed as level 2;

when the transverse distance between the gauge track line and the nearest construction obstacle from the vehicle is larger than one lane width, the confidence score is displayed as level 3; wherein, the confidence score is lower than the reminding threshold value when the confidence score is 1 level.

5. An intelligent driving control method through construction barriers is characterized by comprising the following steps:

Comparing the first confidence score with the second confidence score, taking the confidence score with the minimum value as the confidence score of the intelligent driving control, and reminding a user to pay attention to road conditions when the confidence score of the intelligent driving control is lower than a reminding threshold value, wherein the method comprises the following steps:

When one of the first confidence score and the second confidence score is smaller than a preset confidence level, the confidence score is lower than a reminding threshold value; displaying the smaller one of the first confidence score and the second confidence score to a user;

Wherein the first confidence score and the second confidence score each correspond to a confidence level.

6. The intelligent driving control method through a construction barrier according to claim 5, wherein the calculating of the collision time of the own vehicle with the construction barrier includes: and calculating the time of crossing the contour edge of the construction barrier according to the speed vector of the contour edge of the vehicle, and recording the time as the collision time.

7. The intelligent driving control method through a construction barrier according to claim 5, further comprising: and when the user takes over the driving system, the automatic driving mode is exited.

8. A vehicle comprising a processor, a memory and a computer program stored on the memory and operable on the processor, the computer program when executed by the processor implementing the intelligent driving control method through a construction barrier according to any one of claims 1 to 4 or any one of claims 5 to 7.

9. A computer-readable storage medium, characterized in that a computer program is stored thereon, which, when executed by a computer, performs the intelligent driving control method through a construction obstacle according to any one of claims 1 to 4 or any one of claims 5 to 7.