WO2020042859A1 - 智能驾驶控制方法和装置、车辆、电子设备、存储介质 - Google Patents
智能驾驶控制方法和装置、车辆、电子设备、存储介质 Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 77
- 238000001514 detection method Methods 0.000 claims abstract description 188
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/0055—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots with safety arrangements
- G05D1/0061—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots with safety arrangements for transition from automatic pilot to manual pilot and vice versa
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W60/00—Drive control systems specially adapted for autonomous road vehicles
- B60W60/005—Handover processes
- B60W60/0059—Estimation of the risk associated with autonomous or manual driving, e.g. situation too complex, sensor failure or driver incapacity
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/0098—Details of control systems ensuring comfort, safety or stability not otherwise provided for
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/0055—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots with safety arrangements
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2420/00—Indexing codes relating to the type of sensors based on the principle of their operation
- B60W2420/40—Photo, light or radio wave sensitive means, e.g. infrared sensors
- B60W2420/403—Image sensing, e.g. optical camera
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2552/00—Input parameters relating to infrastructure
- B60W2552/15—Road slope, i.e. the inclination of a road segment in the longitudinal direction
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
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- B60W2552/53—Road markings, e.g. lane marker or crosswalk
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2554/00—Input parameters relating to objects
- B60W2554/40—Dynamic objects, e.g. animals, windblown objects
- B60W2554/402—Type
- B60W2554/4029—Pedestrians
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2554/00—Input parameters relating to objects
- B60W2554/40—Dynamic objects, e.g. animals, windblown objects
- B60W2554/406—Traffic density
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2555/00—Input parameters relating to exterior conditions, not covered by groups B60W2552/00, B60W2554/00
- B60W2555/20—Ambient conditions, e.g. wind or rain
Definitions
- the present disclosure relates to intelligent driving technology, and in particular, to a method and device for intelligent driving control, a vehicle, an electronic device, and a storage medium.
- An embodiment of the present disclosure provides an intelligent driving control technology.
- a confidence degree obtaining unit configured to obtain a confidence degree of a detection result of at least one vehicle driving environment according to data collected by a sensor provided on the vehicle;
- a safety level determining unit configured to determine a driving safety level corresponding to the vehicle according to a mapping relationship between the confidence level and the driving safety level;
- An intelligent driving unit is configured to perform intelligent driving control on the vehicle according to the determined driving safety level.
- an electronic device including a processor, where the processor includes the intelligent driving control device according to any one of the foregoing.
- an electronic device including: a memory for storing executable instructions;
- a computer storage medium for storing computer-readable instructions that, when executed, perform the operations of the intelligent driving control method according to any one of the foregoing.
- a computer program product including computer-readable code, and when the computer-readable code runs on a device, a processor in the device executes to implement any of the above.
- An instruction of the intelligent driving control method is provided.
- the intelligent driving control method and device according to data collected by sensors provided on the vehicle, obtain the confidence of at least one detection result of the driving environment of the vehicle; according to Mapping relationship between confidence and driving safety level to determine the corresponding driving safety level of the vehicle; intelligent driving control of the vehicle according to the determined driving safety level; comprehensive detection results of at least one vehicle driving environment to evaluate the current safety status
- the driving safety level controls the driving mode of the vehicle, which improves the safety and convenience of the vehicle.
- FIG. 1 is a schematic flowchart of a smart driving control method according to an embodiment of the present disclosure.
- FIG. 3 is a schematic structural diagram of an intelligent driving control device according to an embodiment of the present disclosure.
- FIG. 4 is a schematic structural diagram of an electronic device suitable for implementing a terminal device or a server of an embodiment of the present disclosure.
- Embodiments of the present disclosure may be applied to a computer system / server, which may operate with many other general or special purpose computing system environments or configurations.
- Examples of well-known computing systems, environments, and / or configurations suitable for use with computer systems / servers include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, based on Microprocessor systems, set-top boxes, programmable consumer electronics, network personal computers, small computer systems, mainframe computer systems, and distributed cloud computing technology environments including any of the above, and so on.
- a computer system / server may be described in the general context of computer system executable instructions, such as program modules, executed by a computer system.
- program modules may include routines, programs, target programs, components, logic, data structures, and so on, which perform specific tasks or implement specific abstract data types.
- the computer system / server can be implemented in a distributed cloud computing environment. In a distributed cloud computing environment, tasks are performed by remote processing devices linked through a communication network. In a distributed cloud computing environment, program modules may be located on a local or remote computing system storage medium including a storage device.
- FIG. 1 is a schematic flowchart of a smart driving control method according to an embodiment of the present disclosure. As shown in FIG. 1, the method in this embodiment includes:
- Step 110 Obtain a confidence level of a detection result of at least one vehicle running environment according to data collected by a sensor provided on the vehicle.
- the accuracy of the obtained driving safety level is improved.
- step S110 may be executed by the processor calling a corresponding instruction stored in the memory, or may be executed by the confidence obtaining unit 31 executed by the processor.
- Step 120 Determine the driving safety level corresponding to the vehicle according to the mapping relationship between the confidence level and the driving safety level.
- At least one driving safety level may be determined through a mapping relationship between the confidence level and the driving safety level, and these driving safety levels respectively correspond to different vehicle driving environments.
- a lower driving safety level for example, the lowest driving safety level
- the vehicle can be controlled according to the lower driving safety level
- the adjustment improves the safety of the vehicle.
- step S120 may be executed by the processor calling a corresponding instruction stored in the memory, or may be executed by the security level determining unit 32 executed by the processor.
- Step 130 Perform intelligent driving control on the vehicle according to the determined driving safety level.
- Intelligent driving control of the vehicle through the driving safety level enables the vehicle to execute a more suitable driving mode, for example, when automatic driving can be performed, automatic driving can save the driver's energy; when it is not suitable for automatic driving, manual driving can be performed Or assist driving to improve vehicle safety.
- this step S130 may be executed by the processor calling a corresponding instruction stored in the memory, or may be executed by the intelligent driving unit 33 executed by the processor.
- the confidence of at least one detection result of the driving environment of the vehicle is obtained according to data collected by sensors provided on the vehicle; according to the mapping between the confidence and the driving safety level Determine the driving safety level corresponding to the vehicle; perform intelligent driving control on the vehicle according to the determined driving safety level; integrate the detection results of at least one vehicle driving environment, evaluate the current safety status, and finally obtain the driving safety level to control the driving mode of the vehicle, Improved vehicle safety and convenience.
- the method of the embodiment of the present disclosure further includes: displaying related information of the determined driving safety level, and / or sending related information of the determined driving safety level.
- this embodiment may display related information on driving safety level through a display device such as a car display screen or a mobile phone display screen.
- the related information includes but is not limited to driving safety level Corresponding driving mode, camera screen corresponding to driving safety level, etc.
- This embodiment may further include sending related information of driving safety level, and optionally, the related information may be sent to a device preset by the user (such as a mobile phone, a computer, etc.) Terminal), which can be displayed and viewed through the device.
- the device can be an in-vehicle device or a remote device.
- the remote device can enable a preset user to view information related to the driving safety level, which can improve the emergency situation of the vehicle. Processing efficiency and reduce accidents.
- step 120 may include: according to the mapping relationship between the confidence level and the driving safety level, respectively mapping the confidence level of the detection result of at least one vehicle driving environment to obtain at least one Driving safety level;
- the lowest driving safety level of at least one driving safety level is taken as the corresponding driving safety level of the vehicle.
- the confidence levels for the detection results of at least one vehicle driving environment are mapped separately to obtain at least one driving safety level.
- the driving safety level of a vehicle may cause automatic driving due to a higher driving safety level, while automatic driving cannot handle a situation with a lower driving safety level, thereby causing the vehicle to be dangerous.
- a lower driving safety level (for example, the lowest driving safety level) is used as the driving safety level of the vehicle; for example, the value range of processing confidence is 0 to 1, when the driving safety level includes The following 4 levels: low security level, medium low security level, medium security level, high security level, and set low security level, medium low security level, medium security level, and high security level corresponding to 1, 2, 3, and 4 levels, respectively.
- the corresponding driving safety level is obtained based on the confidence map by the following formula (1):
- a and B are fixed coefficients obtained through parameter adjustment
- Conf x is the confidence level corresponding to the driving environment of various vehicles
- Level x is the driving safety level.
- the Level x into the set K 1, K 1 set of stored driving safety level corresponding to each driving scene. Since the impact of each driving scenario on autonomous driving safety is independent of each other, the lower level of driving safety is the bottleneck of autonomous driving safety, so the minimum value of the set K 1 is taken as the autonomous driving safety level: Level safe min ⁇ K 1 ⁇ Level safe is the safety level of autonomous driving.
- the intelligent driving control includes: switching control of a driving mode of the vehicle, and the driving mode includes at least two of the following: an automatic driving mode, a manual driving mode, and an assisted driving mode.
- the automatic driving mode does not require manual participation, and the machine automatically completes the environment observation and vehicle control without manual participation in vehicle control operations, providing convenient services for the driver;
- the manual driving mode is a fully manual control mode, and the driver Operation and observation to control the vehicle, from observing the surrounding environment to controlling the vehicle driving and other functions are manually completed;
- the assisted driving mode can include automatically collecting information and manually controlling the vehicle.
- the assisted driving mode has more Flexibility; manual driving mode and assisted driving mode can be used when driving safety level is low, while automatic driving mode can only be applied when driving safety mode is high; for example: the current road conditions are more complicated and the automatic driving mode cannot be handled correctly In the case of the driver, the driver will be prompted to switch to the manual driving mode or the assisted driving mode.
- the driver may also actively switch the driving mode to the automatic driving mode or the manual driving mode or the assisted driving mode.
- the driving safety level includes at least two of the following: low safety level, medium and low safety level, medium safety level, and high safety level.
- the low safety level has the lowest safety level
- the medium and low safety level has a slightly higher safety level than the low safety level.
- the driving safety level includes at least two types.
- step 130 may include:
- the vehicle In response to the driving safety level being a medium safety level or a high safety level, the vehicle is controlled to execute the automatic driving mode, or the vehicle is controlled to execute the manual driving mode or the assisted driving mode according to the feedback information.
- the vehicle driving environment may include, but is not limited to, at least one of the following: roads, objects, scenes, and number of obstacles;
- the road segmentation result includes at least one of the following: a lane line segmentation result, a stop line segmentation result, and an intersection segmentation result.
- the object detection result includes at least one of the following: a pedestrian detection result, a motor vehicle detection result, a non-motor vehicle detection result, an obstacle detection result, and a dangerous object detection result.
- the scene recognition result includes at least one of the following: a rainy day recognition result, a foggy day recognition result, a sand storm identification result, a flood recognition result, a typhoon recognition result, a cliff recognition result, a steep slope recognition result, a hillside dangerous road recognition result, and light recognition. result.
- obstacles may include, but are not limited to, pedestrians, vehicles, non-motor vehicles, other objects, etc.
- Other objects may include, but are not limited to, fixed buildings, temporary stacking of objects, etc .; in general, the more obstacles in front of the vehicle, the more road surface conditions
- This embodiment passes The detection of the number of different obstacles separately improves the accuracy of the detection results of the number of each obstacle, and further improves the accuracy of the detection results of the number of obstacles.
- step 110 may include:
- the sensor may include but is not limited to a camera
- the collected data may be an image, for example, when the camera is set in front of the vehicle, the collected image is an image in front of the vehicle.
- Images of various environmental information related to the vehicle can be obtained through the sensor.
- the image can be processed by a deep neural network to obtain a confidence level corresponding to the driving environment of each vehicle.
- the confidence level indicates that a certain vehicle driving environment appears. Probability of the situation, for example, if lane lanes, stop lines, or intersections are not recognized in the road information, a confidence level will be obtained, and the highest confidence level will be used as the road information's confidence level to determine that the current road recognition is blocked. What is the degree of confidence in the value? When the possibility of road recognition is blocked, the lower the safety level.
- the detection result of the vehicle driving environment includes at least one of the following: a road segmentation result, an object detection result, and a scene recognition result;
- detection is performed based on at least one vehicle driving environment, and the confidence of at least one detection result is obtained, including:
- each vehicle driving environment determine at least one initial confidence level of each detection result based on the detection result of the vehicle driving environment, and each vehicle driving environment corresponds to at least one detection result;
- the confidence of each detection result is determined based on the average confidence.
- a corresponding confidence level is obtained.
- the corresponding confidence level is determined for at least one of the road segmentation result, the object detection result, and the scene recognition result.
- the higher the confidence level, the lower the possibility of recognizing the road segmentation result, and the lower the driving safety level; the higher the confidence level of the object detection result, the lower the probability of detecting the object, the lower the driving safety level; and the scene recognition result The higher the confidence level, the higher the probability of identifying the scene and the lower the driving safety level; the confidence level can indicate which of the vehicle's driving environment is more serious, which is blocked road recognition, or the presence of pedestrian vehicles and other objects.
- each vehicle driving environment will get a corresponding safety level, the more serious the problem, the lower the safety level; and each vehicle driving environment corresponds to at least one detection result, in order to obtain a more accurate confidence .
- One of the confidence levels can be used as the confidence level of the driving environment, or Based on the mean of the plurality of confidence as the confidence of the traveling environment.
- the initial confidence of the road information is evaluated by the average confidence, a sliding window with a length of T slide is set, and the confidence of the category within the time window is integrated and divided by the time window length to obtain the average confidence.
- the degree avr_Conf i formula is shown in formula (2):
- t time
- Conf i (t) represents the initial confidence corresponding to the i-th type of road information at time t
- i represents the i-th type of road information in the road information.
- determining the confidence of the detection result of the vehicle driving environment from the confidence of at least one detection result including:
- the maximum value of the confidence level of at least one detection result is determined as the confidence level of the detection result of the vehicle running environment.
- Obtaining the maximum value in the confidence level can be achieved by the following formula (3), and taking the maximum value in the set K 2 as the confidence level under the driving environment of the vehicle:
- detection is performed based on at least one vehicle driving environment, and the confidence of at least one detection result is obtained, including:
- the number of each obstacle can be obtained based on the following formula (4).
- a sliding window of length T slide is set, and the number of the category in the time window is counted:
- ConfThr j is the confidence threshold of category j
- i is the sequence number of the category object
- j is the sequence number of this category
- Conf ij represents the confidence level of the appearance of the i-th object in category j
- Num j represents the number of objects in category j.
- the number of mean values corresponding to each obstacle can be obtained based on the following formula (5).
- the number of j-type objects is integrated and divided by the length of the time window.
- t time
- Num j (t) represents the number of pairs of obstacles in the jth category at time t
- j represents the category of obstacles, including 0 to N types, for example :
- obtaining the confidence level corresponding to each obstacle based on the number of averages includes:
- the numerical value of the quotient corresponding to the type of obstacle is limited, and the confidence level corresponding to each obstacle is obtained.
- the numerical limitation of the quotient corresponding to the obstacle can be implemented by a limiting function, which limits the value between 0 and 1.
- the confidence level corresponding to each obstacle can be obtained by the following formula (6 ), The weighted mean number is mapped to the confidence level by an inverse proportional function:
- (*) Is a limit function, used to limit the value in parentheses to between 0 and 1, the value less than 0 is set to 0, and the value greater than 1 is set to 1, where NumThr j represents the number of obstacles in the jth category. Threshold, Conf j represents the confidence level of the j-th class obstacle. If Conf j ⁇ 0, it is added to the set K 3 , and the set K 3 includes the confidence of each type of obstacle.
- determining the confidence of the detection result of the vehicle driving environment from the confidence of at least one detection result including:
- the maximum value of the confidence level of at least one detection result is determined as the confidence level of the detection result of the vehicle running environment.
- the maximum value in the confidence of the detection result can be obtained by replacing K 2 in the above formula (3) with K 3 .
- the senor includes a camera.
- FIG. 2 is a flowchart of driving safety level control in an example of an intelligent driving control method provided by an embodiment of the present disclosure.
- the safety levels include: four safety levels: low safety level, medium low safety level, medium safety level, and high safety level; according to the obtained vehicle driving environment, the obtained driving is judged Whether the safety level is less than or equal to the low-medium safety level; if it is less than or equal to the low-medium safety level, switch the vehicle's driving mode to manual driving mode or assisted driving mode; if it is higher than the low-medium safety level, keep the automatic driving mode.
- the foregoing program may be stored in a computer-readable storage medium.
- the program is executed, the program is executed.
- the method includes the steps of the foregoing method embodiment; and the foregoing storage medium includes: a ROM, a RAM, a magnetic disk, or an optical disc, which can store various program codes.
- FIG. 3 is a schematic structural diagram of an intelligent driving control device according to an embodiment of the present disclosure.
- the apparatus of this embodiment may be used to implement the foregoing method embodiments of the present disclosure. As shown in FIG. 3, the apparatus of this embodiment includes:
- the confidence degree obtaining unit 31 is configured to obtain a confidence degree of a detection result of at least one vehicle running environment according to data collected by a sensor provided on the vehicle.
- the safety level determining unit 32 is configured to determine a driving safety level corresponding to the vehicle according to a mapping relationship between the confidence level and the driving safety level.
- the intelligent driving unit 33 is configured to perform intelligent driving control on the vehicle according to the determined driving safety level.
- the sensor may include but is not limited to a camera
- the collected data may be an image, for example, when the camera is set in front of the vehicle, the collected image is an image in front of the vehicle.
- Images of various environmental information related to the vehicle can be obtained through the sensor.
- the image can be processed by a deep neural network to obtain a confidence level corresponding to the driving environment of each vehicle.
- the confidence level indicates that a certain vehicle driving environment appears. Probability of the situation, for example, if lane lanes, stop lines, or intersections are not recognized in the road information, a confidence level will be obtained, and the highest confidence level will be used as the road information's confidence level to determine that the current road recognition is blocked. What is the degree of confidence in the value? When the possibility of road recognition is blocked, the lower the safety level.
- the detection result of the vehicle driving environment includes at least one of the following: a road segmentation result, an object detection result, and a scene recognition result;
- the environment detection module is configured to process the data collected by the sensors using a deep neural network to obtain detection results of at least one vehicle driving environment; for each vehicle driving environment, determine at least each detection result based on the detection results of the vehicle driving environment.
- An initial confidence level each vehicle driving environment corresponding to at least one of the detection results; at least one initial confidence level based on the detection results to obtain an average confidence level of the detection results within a set time; and determining each detection result based on the average confidence level Confidence.
- the detection result of the driving environment of the vehicle is a detection result of the number of obstacles
- the environment detection module is used to process the data collected by the sensor using a deep neural network to obtain at least one obstacle quantity detection result; based on the detection result of each obstacle quantity, determine the corresponding quantity of each obstacle; at a set time The average number of each obstacle is averaged to obtain the average number of each obstacle; based on the average number, the confidence corresponding to the detection result of the number of each obstacle is obtained.
- the environment detection module when it obtains the confidence corresponding to each obstacle based on the number of averages, it is used to divide the number of averages by the set number threshold of the number of obstacles corresponding to the number of averages to obtain the quotient of the type of obstacle ; Numerically limit the quotient corresponding to the type of obstacle to obtain the confidence level corresponding to each obstacle.
- the environment confidence determination module is configured to determine, for each vehicle running environment, the maximum value of the confidence of at least one detection result as the confidence of the detection result of the vehicle running environment.
- the senor includes a camera.
- a vehicle including the intelligent driving control device according to any one of the above embodiments.
- an electronic device including a processor, where the processor includes the intelligent driving control device according to any one of the above embodiments.
- the electronic device may be a vehicle-mounted electronic device.
- an electronic device including: a memory for storing executable instructions;
- a processor configured to communicate with the memory to execute the executable instructions to complete operations of the intelligent driving control method according to any one of the above embodiments.
- a computer-readable storage medium for storing computer-readable instructions, which are executed when the instructions of the intelligent driving control method according to any one of the embodiments are executed. operating.
- a computer program product including computer-readable code, and when the computer-readable code runs on a device, a processor in the device executes to implement any of the foregoing.
- An instruction of the intelligent driving control method according to an embodiment.
- An embodiment of the present disclosure further provides an electronic device, such as a mobile terminal, a personal computer (PC), a tablet computer, a server, and the like.
- an electronic device such as a mobile terminal, a personal computer (PC), a tablet computer, a server, and the like.
- FIG. 4 illustrates a schematic structural diagram of an electronic device 400 suitable for implementing a terminal device or a server of an embodiment of the present disclosure.
- the electronic device 400 includes one or more processors and a communication unit.
- the one or more processors are, for example, one or more central processing unit (CPU) 401, and / or one or more special-purpose processors, and the special-purpose processors may be used as the acceleration unit 413, which may include but is not limited to images Processors (GPUs), FPGAs, DSPs, and other dedicated processors such as ASIC chips, etc.
- the processors can be loaded into random access memory (from the memory portion 408 according to executable instructions stored in read-only memory (ROM) 402) RAM) 403 to execute various appropriate actions and processes.
- the communication unit 412 may include, but is not limited to, a network card, and the network card may include, but is not limited to, an IB (Infiniband) network card.
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Abstract
Description
Claims (39)
- 一种智能驾驶控制方法,其特征在于,包括:根据车辆上设置的传感器采集的数据,获取至少一种车辆行驶环境的检测结果的置信度;根据置信度和驾驶安全等级之间的映射关系,确定所述车辆对应的驾驶安全等级;根据所述确定的驾驶安全等级对所述车辆进行智能驾驶控制。
- 根据权利要求1所述的方法,其特征在于,还包括:显示确定的驾驶安全等级的相关信息,和/或,发送确定的驾驶安全等级的相关信息。
- 根据权利要求1或2所述的方法,其特征在于,所述根据置信度和驾驶安全等级之间的映射关系,确定与所述车辆对应的驾驶安全等级,包括:根据置信度和驾驶安全等级之间的映射关系,分别将所述至少一种车辆行驶环境的检测结果的置信度进行映射,获得至少一个驾驶安全等级;将所述至少一个驾驶安全等级中最低的驾驶安全等级作为所述车辆对应的驾驶安全等级。
- 根据权利要求1-3任一所述的方法,其特征在于,所述智能驾驶控制包括:对车辆进行驾驶模式的切换控制,所述驾驶模式包括以下至少二种:自动驾驶模式,人工驾驶模式,辅助驾驶模式。
- 根据权利要求4所述的方法,其特征在于,所述驾驶安全等级包括以下至少两种:低安全等级、中低安全等级、中安全等级、高安全等级。
- 根据权利要求5所述的方法,其特征在于,所述根据所述确定的驾驶安全等级对所述车辆进行智能驾驶控制,包括:响应于所述驾驶安全等级为低安全等级或中低安全等级,控制所述车辆执行人工驾驶模式,和/或发出提示信息,并按照反馈信息控制所述车辆执行人工驾驶模式、辅助驾驶模式或自动驾驶模式;和/或,响应于所述驾驶安全等级为中安全等级或高安全等级,控制所述车辆执行自动驾驶模式,或根据反馈信息控制车辆执行人工驾驶模式或辅助驾驶模式。
- 根据权利要求1-6任一所述的方法,其特征在于,所述车辆行驶环境包括以下至少一种:道路、对象、场景、障碍物数量;所述车辆行驶环境的检测结果包括以下至少一种:道路分割结果、对象检测结果、场景识别结果、障碍物数量检测结果。
- 根据权利要求7所述的方法,其特征在于,所述道路分割结果包括以下至少一种:车道线分割结果、停止线分割结果、路口分割结果。
- 根据权利要求7或8所述的方法,其特征在于,所述对象检测结果包括以下至少一种:行人检测结果、机动车检测结果、非机动车检测结果、障碍物检测结果、危险物检测结果。
- 根据权利要求7-9任一所述的方法,其特征在于,所述场景识别结果包括以下至少一种:雨天识别结果、雾天识别结果、沙尘暴识别结果、洪水识别结果、台风识别结果、悬崖识别结果、陡坡识别结果、傍山险路识别结果、光线识别结果。
- 根据权利要求7-10任一所述的方法,其特征在于,所述障碍物数量检测结果包括以下至少一种:行人数量检测结果、机动车数量检测结果、非机动车数量检测结果、其他物体数量检测结果。
- 根据权利要求1-11任一所述的方法,其特征在于,所述根据车辆上设置的传感器采集的数据,获取至少一种车辆行驶环境的检测结果的置信度,包括:根据车辆上设置的传感器采集的数据,分别基于所述至少一种车辆行驶环境进行检测,获得至少一个检测结果的置信度,每种所述车辆行驶环境对应至少一个检测结果的置信度;对每种所述车辆行驶环境,分别从所述至少一个检测结果的置信度中确定所述车辆行驶环境的检测结果的置信度。
- 根据权利要求12所述的方法,其特征在于,所述车辆行驶环境的检测结果包括以下至少一种:道路分割结果、对象检测结果、场景识别结果;所述根据车辆上设置的传感器采集的数据,分别基于所述至少一种车辆行驶环境进行检测,获得至少一个检测结果的置信度,包括:利用深度神经网络对所述传感器采集的数据进行处理,获得至少一种所述车辆行驶环境的检测结果;对每种所述车辆行驶环境,基于所述车辆行驶环境的检测结果确定每种所述检测结果的至少一个初始置信度,每种所述车辆行驶环境对应至少一种所述检测结果;基于所述检测结果的至少一个初始置信度在设定时间内获得所述检测结果的平均置信度;基于所述平均置信度确定每种所述检测结果的置信度。
- 根据权利要求12所述的方法,其特征在于,所述车辆行驶环境的检测结果为障碍物数量检测结果;所述根据车辆上设置的传感器采集的数据,分别基于所述至少一种车辆行驶环境进行检测,获得至少一个检测结果的置信度,包括:利用深度神经网络对所述传感器采集的数据进行处理,获得至少一种障碍物数量检测结果;基于每种所述障碍物数量检测结果,确定每种障碍物对应的数量;在设定时间内对每种所述障碍物对应的数量求平均值,获得每种所述障碍物对应的均值数量;基于所述均值数量获得每种所述障碍物数量检测结果对应的置信度。
- 根据权利要求14所述的方法,其特征在于,所述基于所述均值数量获得每种所述障碍物对应的置信度,包括:将所述均值数量除以所述均值数量对应种类的障碍物的设定数量阈值,得到所述种类的障碍物对应的商;对所述种类的障碍物对应的商进行数值限制,获得每种所述障碍物对应的置信度。
- 根据权利要求12-15任一所述的方法,其特征在于,所述对每种所述车辆行驶环境,分别从所述至少一个检测结果的置信度中确定所述车辆行驶环境的检测结果的置信度,包括:对每种所述车辆行驶环境,将所述至少一个所述检测结果的置信度中的最大值,确定为所述车辆行驶环境的检测结果的置信度。
- 根据权利要求1-16任一所述的方法,其特征在于,所述传感器包括摄像头。
- 一种智能驾驶控制装置,其特征在于,包括:置信度获取单元,用于根据车辆上设置的传感器采集的数据,获取至少一种车辆行驶环境的检测结果的置信度;安全等级确定单元,用于根据置信度和驾驶安全等级之间的映射关系,确定所述车辆对应的驾驶安全等级;智能驾驶单元,用于根据所述确定的驾驶安全等级对所述车辆进行智能驾驶控制。
- 根据权利要求18所述的装置,其特征在于,所述装置还包括:相关信息单元,用于显示确定的驾驶安全等级的相关信息,和/或,发送确定的驾驶安全等级的相关信息。
- 根据权利要求18或19所述的装置,其特征在于,所述安全等级确定单元,用于根据置信度和驾驶安全等级之间的映射关系,分别将所述至少一种车辆行驶环境的检测结果的置信度进行映射,获得至少一个驾驶安全等级;将所述至少一个驾驶安全等级中最低的驾驶安全等级作为所述车辆对应的驾驶安全等级。
- 根据权利要求18-20任一所述的装置,其特征在于,所述智能驾驶控制包括:对车辆进行驾驶模式的切换控制,所述驾驶模式包括以下至少二种:自动驾驶模式,人工驾驶模式,辅助驾驶模式。
- 根据权利要求21所述的装置,其特征在于,所述驾驶安全等级包括以下至少两种:低安全等级、中低安全等级、中安全等级、高安全等级。
- 根据权利要求22所述的装置,其特征在于,所述智能驾驶单元,用于响应于所述驾驶安全等级为低安全等级或中低安全等级,控制所述车辆执行人工驾驶模式,和/或发出提示信息,并按照反馈信息控制所述车辆执行人工驾驶模式、辅助驾驶模式或自动驾驶模式;和/或,响应于所述驾驶安全等级为中安全等级或高安全等级,控制所述车辆执行自动驾驶模式,或根据反馈信息控制车辆执行人工驾驶模式或辅助驾驶模式。
- 根据权利要求18-23任一所述的装置,其特征在于,所述车辆行驶环境包括以下至少一种:道路、对象、场景、障碍物数量;所述车辆行驶环境的检测结果包括以下至少一种:道路分割结果、对象检测结果、场景识别结果、障碍物数量检测结果。
- 根据权利要求24所述的装置,其特征在于,所述道路分割结果包括以下至少一种:车道线分割结果、停止线分割结果、路口分割结果。
- 根据权利要求24或25所述的装置,其特征在于,所述对象检测结果包括以下至少一种:行人检测结果、机动车检测结果、非机动车检测结果、障碍物检测结果、危险物检测结果。
- 根据权利要求24-26任一所述的装置,其特征在于,所述场景识别结果包括以下至少一种:雨天识别结果、雾天识别结果、沙尘暴识别结果、洪水识别结果、台风识别结果、悬 崖识别结果、陡坡识别结果、傍山险路识别结果、光线识别结果。
- 根据权利要求24-27任一所述的装置,其特征在于,所述障碍物数量检测结果包括以下至少一种:行人数量检测结果、机动车数量检测结果、非机动车数量检测结果、其他物体数量检测结果。
- 根据权利要求18-28任一所述的装置,其特征在于,所述置信度获取单元,包括:环境检测模块,用于根据车辆上设置的传感器采集的数据,分别基于所述至少一种车辆行驶环境进行检测,获得至少一个检测结果的置信度,每种所述车辆行驶环境对应至少一个检测结果的置信度;环境置信度确定模块,用于对每种所述车辆行驶环境,分别从所述至少一个检测结果的置信度中确定所述车辆行驶环境的检测结果的置信度。
- 根据权利要求29所述的装置,其特征在于,所述车辆行驶环境的检测结果包括以下至少一种:道路分割结果、对象检测结果、场景识别结果;所述环境检测模块,用于利用深度神经网络对所述传感器采集的数据进行处理,获得至少一种所述车辆行驶环境的检测结果;对每种所述车辆行驶环境,基于所述车辆行驶环境的检测结果确定每种所述检测结果的至少一个初始置信度,每种所述车辆行驶环境对应至少一种所述检测结果;基于所述检测结果的至少一个初始置信度在设定时间内获得所述检测结果的平均置信度;基于所述平均置信度确定每种所述检测结果的置信度。
- 根据权利要求29所述的装置,其特征在于,所述车辆行驶环境的检测结果为障碍物数量检测结果;所述环境检测模块,用于利用深度神经网络对所述传感器采集的数据进行处理,获得至少一种障碍物数量检测结果;基于每种所述障碍物数量检测结果,确定每种障碍物对应的数量;在设定时间内对每种所述障碍物对应的数量求平均值,获得每种所述障碍物对应的均值数量;基于所述均值数量获得每种所述障碍物数量检测结果对应的置信度。
- 根据权利要求31所述的装置,其特征在于,所述环境检测模块在基于所述均值数量获得每种所述障碍物对应的置信度时,用于将所述均值数量除以所述均值数量对应种类的障碍物的设定数量阈值,得到所述种类的障碍物对应的商;对所述种类的障碍物对应的商进行数值限制,获得每种所述障碍物对应的置信度。
- 根据权利要求29-32任一所述的装置,其特征在于,所述环境置信度确定模块,用于对每种所述车辆行驶环境,将所述至少一个所述检测结果的置信度中的最大值,确定 为所述车辆行驶环境的检测结果的置信度。
- 根据权利要求18-33任一所述的方法,其特征在于,所述传感器包括摄像头。
- 一种车辆,其特征在于,包括权利要求18至34任意一项所述的智能驾驶控制装置。
- 一种电子设备,其特征在于,包括处理器,所述处理器包括权利要求18至34任意一项所述的智能驾驶控制装置。
- 一种电子设备,其特征在于,包括:存储器,用于存储可执行指令;以及处理器,用于与所述存储器通信以执行所述可执行指令从而完成权利要求1至17任意一项所述智能驾驶控制方法的操作。
- 一种计算机存储介质,用于存储计算机可读取的指令,其特征在于,所述指令被执行时执行权利要求1至17任意一项所述智能驾驶控制方法的操作。
- 一种计算机程序产品,包括计算机可读代码,其特征在于,当所述计算机可读代码在设备上运行时,所述设备中的处理器执行用于实现权利要求1至17任意一项所述智能驾驶控制方法的指令。
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CN109358612A (zh) | 2019-02-19 |
US20210129869A1 (en) | 2021-05-06 |
SG11202100321WA (en) | 2021-02-25 |
CN109358612B (zh) | 2022-08-09 |
JP2021530394A (ja) | 2021-11-11 |
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