WO2019174397A1 - 自动驾驶安全评估方法、装置和系统 - Google Patents
自动驾驶安全评估方法、装置和系统 Download PDFInfo
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- WO2019174397A1 WO2019174397A1 PCT/CN2019/072074 CN2019072074W WO2019174397A1 WO 2019174397 A1 WO2019174397 A1 WO 2019174397A1 CN 2019072074 W CN2019072074 W CN 2019072074W WO 2019174397 A1 WO2019174397 A1 WO 2019174397A1
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- G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
- G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
- G06F11/0706—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation the processing taking place on a specific hardware platform or in a specific software environment
- G06F11/0736—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation the processing taking place on a specific hardware platform or in a specific software environment in functional embedded systems, i.e. in a data processing system designed as a combination of hardware and software dedicated to performing a certain function
- G06F11/0739—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation the processing taking place on a specific hardware platform or in a specific software environment in functional embedded systems, i.e. in a data processing system designed as a combination of hardware and software dedicated to performing a certain function in a data processing system embedded in automotive or aircraft systems
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- G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
- G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
- G06F11/0751—Error or fault detection not based on redundancy
- G06F11/0754—Error or fault detection not based on redundancy by exceeding limits
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/36—Preventing errors by testing or debugging software
- G06F11/3668—Software testing
- G06F11/3672—Test management
- G06F11/3692—Test management for test results analysis
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- G06F2201/00—Indexing scheme relating to error detection, to error correction, and to monitoring
- G06F2201/81—Threshold
Definitions
- the present application relates to the field of automatic driving, and in particular, to an automatic driving safety assessment method, apparatus and system.
- the self-driving car also known as the driverless car, is capable of acquiring environmental information through sensors such as radar, and controlling the driving of the vehicle according to the environmental information, thereby reducing the time for the driver to control the car.
- Self-driving cars have the advantage of reducing traffic violations.
- the driver controls the car in the automatic driving mode for less time than the driver controls the car in the traditional manual driving mode there are still many uncertainties in the self-driving car, in order to reduce The risk of passengers using autonomous vehicles requires a rigorous assessment of their safety before selling autonomous vehicles.
- An existing automatic driving safety evaluation method is to input a manual driving route into a simulated automatic driving system, and respectively calculate safety-related factors in the artificial driving mode and the automatic driving mode, and obtain quantitative values of the artificial driving mode and the automatic driving mode.
- the above method can use the data in the manual driving mode to evaluate the safety of the automatic driving mode, and reduce the cycle time of the mass production of the self-driving car.
- the safety evaluation based on the simulated automatic driving system cannot restore the real driver control scene. As a result, the safety evaluation results obtained based on the simulated automatic driving system have a large deviation from the true automatic driving safety.
- the present application provides a method and apparatus for automatic driving safety assessment that can reduce the deviation of safety assessment results from true autonomous driving safety.
- an automatic driving safety assessment method comprising: obtaining r b and r c , wherein r b is a risk value of a shadow driving mode of a vehicle in a first unit of measurement, and r c is within a first unit of measurement
- the vehicle is based on the risk value of the automatic driving mode of the preset route, and the shadow driving mode is an automatic driving mode of the vehicle based on the real-time route, the real-time route is a route predicted according to the position and the motion parameter of the vehicle in the manual driving mode, the first measurement
- the unit is a time period or a distance; determining R B according to r b , R B is the risk value of the shadow driving mode of the vehicle in multiple measuring units, and the plurality of measuring units includes the first unit of measurement; determining R C , R according to r c C is a risk value of the preset driving-based automatic driving mode of the vehicle in a plurality of measuring units; wherein R B and R C are used to determine whether
- the automatic driving safety assessment method provided by the present application collects the risk data generated by the automatic driving mode based on the preset route, and also collects the automatic driving mode based on the real-time route.
- Risk data the real-time route is a route predicted according to the position and motion parameters of the vehicle in the manual driving mode. For example, when the driver controls the car to change the preset route, the vehicle safety evaluation device can re-plan the automatic driving route according to the driver's control route. And determine the safety of the automatic driving based on the re-planned automatic driving route, so that the safety of the automatic driving can be more realistically evaluated.
- the method further includes: calculating a sum of R B and R C ; determining that the safety of the vehicle's automatic driving mode is consistent when the sum of R B and R C is less than or equal to an automatic driving risk threshold Claim.
- the above embodiment can be implemented by means for determining R B and R C without transmitting R B and R C to other devices and evaluating the safety of the vehicle's automatic driving mode by other devices, thereby improving the real-time safety assessment of the automatic driving mode. Sex.
- the automatic driving risk threshold is a(R A +R′ A ), where a is a risk tolerance coefficient, and R A is a risk of a manual driving mode of the vehicle in multiple units of measurement.
- the value is that the road section that the vehicle passes in the plurality of measurement units is the first road section, and R' A is the risk value of the vehicle passing the first road section based on the manual driving mode outside the plurality of measurement units.
- the risk value of the automatic driving can be compared with the risk value of the manual driving, and the tolerance coefficient of the automatic driving risk value can be accepted, so that the safety of the automatic driving can be judged whether the safety is satisfied.
- determining R B according to r b and determining R C according to r c include: Determining R B or R C , where event represents a risk scenario, s event represents a coefficient of severity of the accident in the risk scenario, X represents the plurality of units of measure, and when r i represents r b , R i represents R B When r i represents r c , R i represents R C .
- the s event reflects the severity of the accident in different risk scenarios. For example, in a collision scene, the severity of the collision is much higher than the severity of the collision with other objects. Therefore, the severity of the collision can be set to 1, and the collision The severity of other objects can be selected, for example, from 0.1-0.9 depending on the nature of the object; r i reflects the probability of the risk being converted into an accident, ⁇ reflects the cumulative risk of multiple units of measure, and R i reflects the average The probability that a risk is converted into an accident within the unit of measure.
- the above formula can objectively reflect the risk of autonomous driving.
- obtaining r b and r c includes: determining r b and r c according to at least one of the following parameters when the risk scene is a collision scenario: a speed ⁇ v of the vehicle relative to the collision object, based on the collision
- the trajectory of the object is estimated by the time to collision (TTC) of the collision of the vehicle with the collision object, the time to headway (THW) of the vehicle based on the distance between the vehicle and the collision object, and the distance between the vehicle and the colliding object, wherein, Delta] v and r b r c or positive correlation, the TTC or negative correlation with r b r c, THW or negative correlation with r b r c, the distance between the vehicle and the colliding object and r b or r c is negatively correlated.
- TTC time to collision occurrence
- THW time to collision occurrence
- vehicle and collision object The farther the distance is, the more likely the vehicle adjusts the travel route to avoid collisions, and the less likely the collision occurs, so the distance between the vehicle and the collision object is negatively correlated with r b or r c .
- r b and r c are determined according to at least one of the following parameters: speed ⁇ v of the vehicle relative to the collision object, time required for the collision of the vehicle and the collision object based on the motion trajectory of the collision object
- obtaining r b and r c includes: when the risk scene is a traffic signal scene, determining r b and r c according to the speed of the vehicle and the distance of the vehicle to the traffic signal, wherein the speed of the vehicle is r b or r c is positively correlated, and the distance from the vehicle to the traffic signal is negatively correlated with r b or r c .
- r b and r c is determined according to the vehicle speed and the distance between the vehicle and the traffic signal, comprising: determining r b and r c
- obtaining r b and r c includes: determining r b and r c according to the estimated vehicle exiting lane time when the risk scene is a road boundary scenario, wherein the estimated vehicle The exit time is negatively correlated with r b or r c .
- Lane time is negatively correlated with r b or r c .
- determining r b and r c according to the estimated vehicle exiting lane time including: Determining r b and r c , r SG3 is r b or r c in the scene of the road exiting the road, where the TLC threshold indicates the critical value of the time required for the vehicle to rush out of the road, e is a natural constant, t lc is the estimated vehicle rushing out of the lane time, y is the lateral offset of the vehicle at the road boundary, W road is the width of the road, v long is the longitudinal speed of the vehicle, and v lat is the lateral direction of the vehicle The speed, ⁇ - ⁇ d represents the angle between the heading of the vehicle and the tangential direction of the road, and r 4 is the normalized parameter.
- obtaining r b and r c includes: determining r b and r c according to at least one of the following parameters when the risk scene is an unconventional driving scene: longitudinal acceleration of the vehicle, lateral acceleration of the vehicle And the speed of the vehicle, wherein the longitudinal acceleration of the vehicle is positively correlated with r b or r c , the lateral acceleration of the vehicle is positively correlated with r b or r c , and the speed of the vehicle is positively correlated with r b or r c .
- An unconventional form of scene is, for example, a driving scene in which the vehicle has lateral acceleration.
- the longitudinal acceleration of the vehicle, a lat represents the lateral acceleration of the vehicle, a threshold1 represents the threshold of the lateral acceleration in the safe driving state, a threshold2 represents the threshold of the longitudinal acceleration in the safe driving state, and v represents the speed of the vehicle, v safe speed threshold value indicates the safe driving condition, r 5 is a normalization parameter.
- obtaining r b and r c includes: determining a driving mode and a risk scene of the vehicle in a time period, where the starting point of the time period is a first moment, and the ending point of the time period is a second moment
- the driving mode includes an automatic driving mode and/or a manual driving mode; determining, according to a risk identification manner corresponding to the risk scene, a risk value of the driving mode in the time period, wherein the current time is after the first time, and The current moment is before the second moment.
- the above time period may be a unit of measurement, and the risk values of the driving mode during the time period are, for example, r b and r c .
- the vehicle selects a time period among a plurality of time periods required to travel a certain distance, and determines a corresponding time in the time period by using the known driving information and the predicted driving information at a time (ie, the current time) within the time period.
- the risk value of the driving mode for example, the determined time period is 1 second, and the current time is 0.4 seconds.
- the vehicle can determine the driving mode in the time period is very
- the driving risk value is 1, that is, the probability of overspeed is 100%; if there is no overspeed in the past 0.4 seconds, and the vehicle determines that the speed will remain in the legal 0.6 seconds in the future based on the current vehicle speed and acceleration Within the driving speed, the vehicle can determine that the unconventional driving risk value in the time period is a value less than 1, that is, the probability of occurrence of overspeed is less than 100%.
- the time period required for traveling for a certain distance is divided into a plurality of time periods, and the risk value of the driving mode in each time period is determined according to the traffic context in each time period, so that the risk can be more objectively identified.
- the method before determining the risk value of the driving mode in the time period according to the risk identification manner corresponding to the risk scenario, the method further includes: determining a position and a motion parameter of the vehicle at the first moment, Wherein, the vehicle travels based on the manual driving mode at the first moment; predicts the position of the vehicle at the third moment according to the position and the motion parameter of the vehicle at the first moment, the third moment is later than the first moment; according to the position and the first moment The position of the three moments predicts the shadow driving mode of the vehicle from the position of the first moment to the position of the third moment, and the shadow driving mode is an automatic driving mode of the vehicle based on the position and the motion parameter predicted by the vehicle in the manual driving mode; Determining the risk value of the driving mode in the time period according to the risk identification manner corresponding to the risk scenario, including: determining r a , r b , and r c according to the risk identification manner corresponding to the risk scenario at the current time, where r a is artificial risk
- the vehicle may predict the position of the vehicle after a period of time according to the position and the motion parameter of the vehicle at the first moment, that is, predict the position of the vehicle at the third moment, wherein the vehicle is in the manual driving state at the first moment, and then, the vehicle prediction The shadow driving mode of the vehicle from the position at the first moment to the position at the third moment.
- the above embodiment continuously corrects the route of the automatic driving according to the position and the motion parameter in the manual driving mode, so that the route of the automatic driving mode is closer to the route of the manual driving mode, so that more objective automatic driving risk data can be acquired.
- the present application also provides a method for determining driving risk of a vehicle, the method being applied to a vehicle having an automatic driving mode and a manual driving mode, the method comprising: determining a driving mode and a risk scene of the vehicle within a time period
- the starting point of the time period is a first time
- the end point of the time period is a second time
- the driving mode includes an automatic driving mode and/or a manual driving mode
- the current time is determined according to a risk identification manner corresponding to the risk scene.
- the risk value of the driving mode in the time period wherein the current time is after the first time, and the current time is before the second time.
- the vehicle selects a time period among a plurality of time periods required to travel a certain distance, and determines a corresponding time in the time period by using the known driving information and the predicted driving information at a time (ie, the current time) within the time period.
- the risk value of the driving mode for example, the determined time period is 1 second, and the current time is 0.4 seconds.
- the vehicle can determine the driving mode in the time period is very
- the driving risk value is 1, that is, the probability of overspeed is 100%; if there is no overspeed in the past 0.4 seconds, and the vehicle determines that the speed will remain in the legal 0.6 seconds in the future based on the current vehicle speed and acceleration Within the driving speed, the vehicle can determine that the unconventional driving risk value in the time period is a value less than 1, that is, the probability of occurrence of overspeed is less than 100%.
- the time period required for traveling for a certain distance is divided into a plurality of time periods, and the risk value of the driving mode in each time period is determined according to the traffic context in each time period, so that the risk can be more objectively identified.
- the method before determining the risk value of the driving mode in the time period according to the risk identification manner corresponding to the risk scenario, the method further includes: determining a position and a motion parameter of the vehicle at the first moment, Wherein, the vehicle travels based on the manual driving mode at the first moment; predicts the position of the vehicle at the third moment according to the position and the motion parameter of the vehicle at the first moment, the third moment is later than the first moment; according to the position and the first moment The position of the three moments predicts the shadow driving mode of the vehicle from the position of the first moment to the position of the third moment, and the shadow driving mode is an automatic driving mode of the vehicle based on the position and the motion parameter predicted by the vehicle in the manual driving mode; Determining the risk value of the driving mode in the time period according to the risk identification manner corresponding to the risk scenario, including: determining r a , r b , and r c according to the risk identification manner corresponding to the risk scenario at the current time, where r a is artificial risk
- the vehicle may predict the position of the vehicle after a period of time according to the position and the motion parameter of the vehicle at the first moment, that is, predict the position of the vehicle at the third moment, wherein the vehicle is in the manual driving state at the first moment, and then, the vehicle prediction The shadow driving mode of the vehicle from the position at the first moment to the position at the third moment.
- the above embodiment continuously corrects the route of the automatic driving according to the position and the motion parameter in the manual driving mode, so that the route of the automatic driving mode is closer to the route of the manual driving mode, so that more objective automatic driving risk data can be acquired.
- determining r a , r b , and r c according to the risk identification manner corresponding to the risk scenario at the current moment includes: determining, when the risk scenario is a collision scenario, r a according to at least one of the following parameters, r b and r c : the speed ⁇ v of the vehicle with respect to the collision object, the time TTC required for the collision of the vehicle with the collision object based on the motion trajectory of the collision object, the vehicle estimated based on the distance between the vehicle and the collision object brake THW is the time, and the distance between the vehicle and the colliding object, wherein, Delta] v and r a, r b r c or positive correlation, with the TTC r a, r b r c or negative correlation, the THW is r a, r b, or r c is negatively correlated, and the distance between the vehicle and the collision object is negatively correlated with r a , r b or
- TTC is negatively correlated with r a , r b or r c ; the longer the brake is available, the smaller the probability of collision, so THW and r a , r b or r c is negatively correlated; the farther the vehicle is from the collision object, the greater the possibility that the vehicle adjusts the driving route to avoid collision, and the smaller the probability of collision occurrence, therefore, the distance between the vehicle and the collision object is r a , r b or r c negative correlation.
- r a , r b and r c are determined according to at least one of the following parameters: a speed ⁇ v of the vehicle relative to the collision object, a vehicle estimated based on the motion trajectory of the collision object, and the collision object occurrence
- determining r a , r b , and r c according to the risk identification manner corresponding to the risk scene at the current moment including: when the risk scene is a traffic signal scene, according to the speed of the vehicle and the vehicle to the traffic signal determining the distance r a, r b and r C, wherein r and the vehicle speed a, r b C r or positive correlation, vehicle traffic lights to a distance r a, r b C r or negative.
- the acceleration constant, s(t) represents the distance of the vehicle to the traffic signal at time t.
- determining r a , r b , and r c according to the risk identification manner corresponding to the risk scenario at the current moment including: when the risk scenario is a road boundary scenario, the predicted vehicle exits the lane The time determines r a , r b and r c , wherein the predicted vehicle exit lane time is inversely related to r a , r b or r c .
- Lane time is negatively correlated with r b or r c .
- determining, according to the estimated vehicle exiting lane time, r a , r b , and r c including: according to Determining r a , r b and r c , r SG3 is r a , r b or r c in the scene of the road exit , wherein the TLC threshold indicates a critical value of the time required for the vehicle to rush out of the road, e is a natural constant, t lc is the estimated vehicle exit time, y is the lateral offset of the vehicle at the road boundary, W road is the width of the road, and v long is the longitudinal direction of the vehicle The speed, v lat is the lateral speed of the vehicle, ⁇ - ⁇ d represents the angle between the heading of the vehicle and the tangential direction of the road, and r 4 is a normalized parameter.
- determining r a , r b , and r c according to the risk identification manner corresponding to the risk scenario at the current moment includes: determining, when the risk scenario is an unconventional driving scenario, determining the r according to at least one of the following parameters: a , r b and r c : the longitudinal acceleration of the vehicle, the lateral acceleration of the vehicle and the speed of the vehicle, wherein the longitudinal acceleration of the vehicle is positively correlated with r a , r b or r c , the lateral acceleration of the vehicle and r a , r b Or r c is positively correlated and the speed of the vehicle is positively correlated with r a , r b or r c .
- the longitudinal acceleration of the vehicle is positively correlated with r a , r b or r c ; the greater the lateral acceleration, the greater the probability that the vehicle will make a sharp turn, therefore, the vehicle
- the lateral acceleration is positively correlated with r a , r b or r c ; the greater the speed of the vehicle, the greater the probability of unconventional driving such as overspeed, and therefore the speed of the vehicle is positively correlated with r a , r b or r c .
- the method further includes: transmitting r a , r b , and r c , r a , r b , and r c to the security center for evaluating the security of the artificial driving mode and the shadow driving mode.
- the vehicle may send the above risk data or the original data corresponding to the risk data (for example, the measured speed value of the vehicle) to the safety center for the safety center to evaluate the manual driving mode and the shadow.
- the safety of the driving mode reduces the workload of the onboard processor.
- the method further includes: receiving security scenario information sent by the security center, the security scenario information is used to indicate a safe driving traffic scene set of the automatic driving mode; and determining the automatic driving mode according to the security scenario information.
- a safe driving traffic scene set if the current traffic scene does not belong to a safe driving traffic scene set, the vehicle plays and/or displays a prompt message prompting the driver to perform manual driving, and/or the vehicle performs a safe docking process.
- the security center may identify the risk scenario according to the risk data reported by the vehicle, and update the risk identification mechanism and the risk control strategy of the vehicle by transmitting the security scenario information to the vehicle.
- the security scenario information may be Instructing the vehicle to exclude the risk scene from the safe driving traffic scene set, and updating the safe driving traffic scene set after receiving the safety scene information, and if the current traffic scene does not belong to the updated safe driving traffic scene set, the vehicle adopts safety measures. For example, playing and/or displaying a prompt message prompting the driver to perform manual driving, and/or performing a safe docking process.
- an automatic driving safety evaluation device which can implement the functions described in the method related to the first aspect, and the functions can be implemented by hardware or by executing corresponding software by hardware.
- the hardware or software includes units or modules corresponding to one or more of the functions described above.
- the above apparatus includes a transceiver and a processor configured to support the apparatus to perform the functions recited in the method of the first aspect described above.
- the transceiver is used to support communication between the device and other devices.
- the apparatus can also include a memory for coupling with the processor that retains the program instructions and data necessary for the apparatus.
- a computer readable storage medium the computer program code storing computer program code, when executed by a processing unit or a processor, causing an automatic driving safety assessment device to perform the first aspect Said method.
- a chip in which instructions are stored that, when run on an automated driving safety assessment device, cause the chip to perform the method of the first aspect.
- a computer program product comprising: computer program code, when the computer program code is run by a communication unit or transceiver of an automated driving safety assessment device, and a processing unit or processor
- the automatic driving safety evaluation device performs the method of the above first aspect.
- a device for determining a driving risk of a vehicle may implement the functions involved in the method of the second aspect, and the foregoing functions may be implemented by hardware or by executing corresponding software by hardware.
- the hardware or software includes units or modules corresponding to one or more of the functions described above.
- the above apparatus includes a transceiver and a processor configured to support the apparatus to perform the functions recited in the method of the first aspect described above.
- the transceiver is used to support communication between the device and other devices.
- the apparatus can also include a memory for coupling with the processor that retains the program instructions and data necessary for the apparatus.
- a computer readable storage medium storing computer program code for executing a device for determining a driving risk of a vehicle when the computer program code is executed by a processing unit or a processor The method described in the two aspects.
- a chip in which instructions are stored that, when run on a device that determines the driving risk of the vehicle, cause the chip to perform the method of the second aspect.
- a computer program product comprising: computer program code, when the computer program code is determined to be a communication unit or transceiver of the device driving the vehicle, and the processing unit or processor is running
- the apparatus for determining the driving risk of the vehicle performs the method of the second aspect described above.
- an automatic driving safety assessment system comprising a risk control module, a risk policy module and a safety assessment module, wherein the risk control module is configured to perform the method in any of the optional embodiments of the first aspect To determine the risk of the automatic driving mode; the risk strategy module is configured to determine a risk control strategy according to the risk of the automatic driving mode determined by the risk control module, and the risk control strategy is used to reduce or eliminate the risk of the automatic driving mode; the safety evaluation strategy And determining a security of the automatic driving mode according to a risk of the automatic driving mode determined by the risk control module.
- an automatic driving safety evaluation system comprising a risk control module, a risk policy module and a safety evaluation module, wherein the risk control module is configured to perform the method in any of the alternative embodiments of the second aspect To determine the risk of the automatic driving mode; the risk strategy module is configured to determine a risk control strategy according to the risk of the automatic driving mode determined by the risk control module, and the risk control strategy is used to reduce or eliminate the risk of the automatic driving mode; the safety evaluation strategy And determining a security of the automatic driving mode according to a risk of the automatic driving mode determined by the risk control module.
- Figure 1 is a flow chart of a conventional automobile product release
- Figure 2 is a schematic diagram of various risks of manual driving and the risk of autonomous driving
- Figure 3 is a schematic diagram of a mass production route of an autonomous vehicle
- FIG. 4 is a schematic diagram of an automatic driving safety assessment system provided by the present application.
- FIG. 5 is a schematic flowchart of a method for evaluating automatic driving safety provided by the present application.
- FIG. 6 is a schematic diagram of an automatic driving safety evaluation method provided by the present application.
- FIG. 7 is a schematic diagram of risks in a collision scenario provided by the present application.
- FIG. 8 is a schematic diagram of risks in a traffic signal scene provided by the present application.
- FIG. 9 is a schematic diagram of a method for determining driving risk of a vehicle provided by the present application.
- FIG. 10 is a schematic diagram of an automatic driving control vehicle trajectory provided by the present application.
- FIG. 11 is a schematic flow chart of a shadow driving mode provided by the present application.
- FIG. 12 is a schematic flowchart of a method for identifying a driver's driving intention provided by the present application
- FIG. 13 is a schematic diagram of predicting a shadow trajectory based on a shadow driving mode provided by the present application.
- FIG. 14 is a schematic diagram of comparison between a shadow driving mode risk and a manual driving mode risk provided by the present application.
- 16 is a schematic diagram of a safe driving scene fence provided by the present application.
- 17 is a schematic view of a vehicle end device provided by the present application.
- Figure 18 is a schematic view of another vehicle end device provided by the present application.
- FIG. 19 is a schematic diagram of a cloud device provided by the present application.
- 20 is a schematic diagram of another cloud device provided by the present application.
- the traditional automotive product release process is shown in Figure 1, including: product planning, development, verification, release, exposure to defects, and termination (ie, discontinuation).
- the safety level requirements of the autopilot system are similar to those in the traditional automotive electronics field, but due to the unknown hidden dangers introduced by autonomous driving and the cumulative road test mileage (million-kilometer class) introduced by the complexity of the driving scene, it is difficult to implement as shown in Figure 1.
- the traditional release process is similar to those in the traditional automotive electronics field, but due to the unknown hidden dangers introduced by autonomous driving and the cumulative road test mileage (million-kilometer class) introduced by the complexity of the driving scene, it is difficult to implement as shown in Figure 1.
- the traditional release process is shown in Figure 1, including: product planning, development, verification, release, exposure to defects, and termination (ie, discontinuation).
- Figure 2 shows the unknown hazard introduced by autonomous driving.
- the outermost circle represents all driving scenarios, where the insignificant scene (A zone) occupies the majority of the driving scene, and the existing knowledge can only pass through the traffic that has occurred.
- C zone identifying the risk scenario of the artificial driving
- E zone the risk of self-driving and protected manual driving
- F zone new unknown hazards
- the self-driving car mass production route includes two routes.
- Route 1 Small-scale production and installation of auto-driving cars, safety evaluation methods to verify their safety, verify the safety of the autopilot system, when the safety performance of the autopilot system meets the requirements, determine whether it can enter the mass production phase.
- Route 2 Mass production of vehicles with advanced driver assistance systems, safety assessment methods to assess their safety, when the advanced driver assistance system achieves a reliable level of safety, cars with advanced driver assistance systems can be upgraded to autonomous driving car.
- Route 1 and Route 2 represent two routes for mass production of autonomous vehicles.
- the vehicle identifies the risk caused by the defect of the automatic driving system through the risk identification method, updates the risk control strategy in time, and adopts risk control measures before the risk has been converted into an accident to reduce the probability of accident occurrence.
- FIG. 4 shows an automatic driving safety assessment system provided by the present application.
- the system 400 includes a vehicle end device 410 and a cloud device 420.
- the vehicle end device 410 includes a sensor 411, a real-time environment sensing module 412, an autopilot module 413, a shadow driving module 414, a risk identification module 415, and a risk control module 416.
- the cloud device 420 includes a risk policy module 421 and a security assessment module 422.
- the sensor 411 is used to collect environmental information, and the sensor 411 can be, for example, a camera and a radar.
- the real-time environment awareness module 412 is configured to process the environment information collected by the sensor 411 for use by subsequent modules. For example, the camera detects that there is an obstacle in front of the vehicle, and the radar also detects that there is an obstacle in front of the vehicle. In fact, the obstacle detected by the camera and the radar may be the same obstacle, and the real-time environment sensing module 412 needs to be based on The information gathered by the camera and radar determines that there are actually several obstacles in front.
- the autopilot module 413 is configured to control the vehicle travel according to the data (ie, the perception result) output by the real-time environment sensing module 412, and output the autopilot control trajectory.
- the shadow driving module 414 is configured to simulate the automatic driving according to the data output by the real-time environment sensing module 412 when the vehicle is manually driven, and generate a shadow trajectory.
- the workflow of the shadow driving module 414 will be described in detail below.
- the risk identification module 415 is configured to identify the risk scenario of the manual driving, the automatic driving, and the shadow driving, and generate the risk data.
- the risk identification module 415 identifies the risk of the manual driving according to the sensing result output by the factual environment sensing module 412, according to the automatic driving module 413.
- the output of the autopilot track trajectory identifies the risk of automatic driving, and the risk of shadow driving is identified based on the shadow trajectory output by the shadow driving module 414.
- the risk policy module 421 is configured to formulate a risk control strategy for the potential risk of the identified automatic driving system, and the risk control module 416 is configured to take measures to reduce or eliminate the risk according to the risk control strategy issued by the cloud.
- the safety assessment module 422 is for evaluating the safety of the automated driving system using the accumulated risk data.
- FIG. 4 is a schematic description of the system 400 from the perspective of function division.
- the functions of each module may be implemented in software form, or may be implemented in hardware form, or may be implemented in a combination of software and hardware.
- the function of the various modules of the system 400 can be determined according to the actual situation, which is not limited in this application.
- FIG. 4 divides the system 400 into a vehicle end device and a cloud device, but the architecture of the system 400 is not limited thereto.
- the risk identification module 415 can be deployed in the cloud device 420, and the vehicle end device 410 does not need to perform risk identification. Processing, this can reduce the burden on the processor of the vehicle end device 410; for example, the vehicle end device 410 can deploy the risk policy module and the security evaluation module, so that even if the vehicle end device 410 is in an offline state, the vehicle end device 410 can be based on Identify the risks associated with automated driving systems to develop risk strategies and assess the safety of automated driving systems.
- FIG. 5 illustrates a schematic flow diagram of a method for assessing the safety of automated driving.
- S501 can be performed by sensor 411, which collects real-time information, reconstructs static and dynamic environmental data, and trajectory data of the vehicle (also referred to as "self-driving") in which sensor 411 is located.
- S502 may be executed by the risk identification module 415 to identify risks in different vehicle control modes, wherein if the vehicle is in the automatic driving mode, the risk identification module 415 calculates the risk characteristics of the automatic driving according to the automatic driving control track; if the vehicle is in manual In the driving mode, the risk identification module 415 calculates the risk characteristics of the artificial driving according to the driver's controlled vehicle trajectory, and calculates the risk characteristics of the shadow driving according to the shadow trajectory.
- the risk control module 416 identifies the potential defects of the automated driving system based on the risk characteristics of the automatic driving mode calculated by the risk identification module 415 and the risk characteristics of the shadow driving mode, and updates the risk control strategy.
- S504 may be performed by a security assessment module 422 that evaluates the security of the automated driving mode based on the accumulated risk characteristics of the various driving modes, the results of which are used as a basis for security verification and mass production release.
- FIG. 6 shows an automatic driving safety assessment method provided by the present application.
- the method 600 includes:
- r b is a risk value of a shadow driving mode of the vehicle in the first measurement unit
- r c is a risk value of the automatic driving mode of the vehicle based on the preset route in the first measurement unit
- the driving mode is a real-time route-based automatic driving mode of the vehicle
- the real-time route is a route predicted according to the position and the motion parameter of the vehicle in the manual driving mode
- the first unit of measurement is a time period or a distance.
- R C is a risk value of the automatic driving mode based on the preset route of the vehicle in multiple measuring units; wherein, R B and R C are used to determine the safety of the automatic driving mode of the vehicle does it reach the requirement.
- Method 600 can be performed by cloud device 420 or by a vehicle-end device configured with a security assessment module.
- r b and r c reflect the probability that the risk is converted into an accident, which may be the probability value of the risk being converted into an accident, or other types of values that can quantify the risk of automatic driving.
- the unit of measurement of r b and r c may be a time period or a distance.
- Risk behavior can be defined as explicit or implicit driving behaviors that violate safety objectives (including manual driving, automatic driving, and shadow driving), where explicit expression of driving behavior has turned into an accident, implicitly indicating that driving behavior may be transformed For the accident. Therefore, the identification of risk behaviors includes:
- the identification of hidden risks does not reserve sufficient safety distance with the preceding vehicle, the maximum speed of the safe driving speed is still accelerated, and the intersection does not decelerate in time; the hidden risk also includes abnormal driving behavior, these driving The behavior may cause his car to fail to react in time to cause a passive accident, such as abnormal acceleration and deceleration, and not traveling in the direction of the road.
- the above safety goal includes but is not limited to the following definitions:
- SG4 Avoid unconventional driving (including overspeed, abnormal acceleration and deceleration and steering)
- the risk value when there is a dominant risk, can be defined as 1; when there is no risk, the risk value can be defined as 0; and the risk value of the recessive risk is greater than 0 and less than 1.
- the method of calculating the risk value in each scenario is described in detail below.
- the first unit of measurement is any unit of measurement.
- the automatic driving mode based on the preset route is the automatic driving mode shown in FIG. 5, which controls the driving mode of the vehicle for the automatic driving module 413, and the automatic driving module 413 acquires the starting point and the ending point (ie, the preset route) of a certain distance. Control the vehicle to travel the section, the driver does not participate in driving.
- the driving mode based on the real-time route is an automatic driving mode simulated by the shadow driving module 414 during the driver's control of the running of the vehicle, and the shadow driving module 414 continuously corrects the shadow trajectory according to the position and the motion parameter of the vehicle when the driver controls the vehicle, thereby avoiding the driver's control of the vehicle.
- the route passes through the A street, and the automatic driving route passes through the B street, making the safety assessment of the automatic driving more objective.
- R B may be, for example, an average value obtained by dividing the sum of r b of a plurality of measurement units by a plurality of measurement units, or may be a sum after each measurement unit of r b multiplied by a weighting coefficient.
- the average value obtained by dividing by a plurality of units of measurement; for the same reason, R C may be, for example, an average value obtained by dividing the sum of r c of a plurality of units of measurement by a plurality of units of measurement, or may be an amount of each unit of measure. c is multiplied by the sum of the weighting coefficients and then divided by the average of the plurality of units of measure.
- R B and R C may also be values calculated by other methods, and the present application does not limit the method of calculating R B and R C .
- the automatic driving safety assessment method collects risk data generated by an automatic driving mode based on a preset route, in addition to collecting risk data generated by an automatic driving mode based on a preset route, and the real-time route is based on manual driving.
- the mode of the vehicle's position and motion parameters predicted autopilot route ie, shadow trajectory
- the shadow driving module 414 may re-plan the autopilot route based on the driver's control route and based on re-planning
- the automatic driving route determines the safety of the automatic driving, so that the safety of the automatic driving can be more realistically evaluated.
- the method 600 further includes:
- the above embodiment can be implemented by means for determining R B and R C without transmitting R B and R C to other devices and evaluating the safety of the vehicle's automatic driving mode by other devices, thereby improving the real-time safety assessment of the automatic driving mode. Sex.
- the automatic driving risk threshold indicates a threshold value of the risk value of the automatic driving mode in each unit of measurement.
- the automatic driving risk threshold may be a value set according to the statistical result, for example, the maximum value of the average driving risk per kilometer in the mileage of one million kilometers traveled by the vehicle, if the vehicle travels one million kilometers after the R B +R If the value of C is less than or equal to the maximum value, the auto-driving function of the vehicle meets the safety requirements and can be mass-produced. If the value of R B +R C is greater than the maximum value after the vehicle travels one million kilometers, the vehicle automatically The driving function does not meet the safety requirements and cannot be mass-produced and needs to be improved.
- R B R C + R B and R C are respectively corresponding to the mileage, for example, it may be set per million kilometers R B + R C R C corresponding to the mileage in less than 10 Ten thousand kilometers, so that the safety of autonomous driving can be evaluated more objectively and realistically.
- the automatic driving risk threshold is a(R A +R′ A ), where a is a risk tolerance coefficient, and R A is, for example, a mode of manual driving of the vehicle in multiple units of measurement.
- the average value of the risk value, the road segment through which the vehicle passes in a plurality of measurement units is the first road segment, and R' A is, for example, an average value of the risk values of the vehicle passing through the first road segment based on the manual driving mode outside the plurality of measurement units.
- the risk value of the automatic driving can be compared with the risk value of the manual driving, and the tolerance coefficient of the automatic driving risk value can be accepted, so that the safety of the automatic driving can be judged whether the safety is satisfied.
- R A is the risk value obtained after the vehicle is driven for 500,000 kilometers in the manual driving mode.
- the road section through which the 500,000 kilometers pass is the first road section, and R' A is the vehicle in the manual driving mode after passing the first road section again.
- the obtained risk value; R B + R C is the risk value obtained after driving one million kilometers of self-driving, and a means that the risk value of the automatic driving needs to be smaller than the risk value of the manual driving and a in the case of traveling the same length.
- the product can be used to determine that the safety of the automatic driving mode meets the requirements, and a is usually a constant greater than 0 and less than 1.
- determining R B from r b and determining R C from r c includes:
- the s event reflects the severity of the accident in different risk scenarios, r i reflects the probability that the risk is converted into an accident, and ⁇ reflects the cumulative risk of multiple units of measure.
- the s event of the collision can be set to 1, and the s event of the other object can be from 0.1- according to the nature of the object. Choose from 0.9.
- s event can be defined based on accident statistics. When any of the above behaviors occurs, the corresponding s event is equal to 0.01.
- X may be the mileage or time traveled by the vehicle corresponding to R i , and R i reflects the probability that the risk is converted into an accident per average unit of measure (eg, per kilometer or hour).
- the above formula can objectively reflect the risk of autonomous driving.
- obtaining r b and r c includes:
- determining r b and r c according to at least one of the following parameters: a speed ⁇ v of the vehicle relative to the collision object, and a collision of the vehicle and the collision object estimated based on the motion trajectory of the collision object Time TTC, the braking time THW of the vehicle based on the distance between the vehicle and the collision object, and the distance between the vehicle and the collision object, where ⁇ v is positively correlated with r b or r c , TTC and r b or r c is negatively correlated, THW is negatively correlated with r b or r c , and the distance between the vehicle and the collision object is negatively correlated with r b or r c .
- TTC time to collision occurrence
- THW time to collision occurrence
- vehicle and collision object The farther the distance is, the more likely the vehicle adjusts the travel route to avoid collisions, and the less likely the collision occurs, so the distance between the vehicle and the collision object is negatively correlated with r b or r c .
- FIG. 7 is a schematic diagram showing the risk in a collision scenario provided by the present application.
- the trajectory of the self-vehicle in Fig. 7 is the line segment from the state (t2, s2, v2, a2) to the state (t0, s0, v0, a0), and the trajectory of the car is from the state (t1, s1, v1, a1). )
- the two cars face each other and collide at the intersection of the two lines shown in the figure.
- the above two trajectories are all trajectories predicted by the vehicle, and the self-vehicle recognizes the object that intersects with the self-vehicle trajectory in a certain period of time (for example, 5 seconds) according to the sensing result of the sensor, and the collision risk value in the future period is taken from the vehicle and The maximum value of the collision risk value between the above objects.
- TTC(t) represents the TTC at time t
- THW(t) represents the THW at time t.
- r 0 , r 1 and r 2 are normalized parameters respectively, v is the speed of the vehicle at time t, ⁇ v(t) is ⁇ v at time t, exp is an index based on the natural constant, and s is the vehicle and the collision object. The distance between them.
- h(t event , obj) represents the collision risk event (obj) for a period of time (t Event ) raises a potential conflict event
- h(t event , obj) indicates that the occurrence of a collision risk event (obj) leads to an increase in the probability of collision within a period of time (t event )
- h(t event , obj) 0 indicates that no collision risk event has occurred.
- each normalization parameter is used to represent the weight of each parameter. For example, each normalization parameter may take a value greater than or equal to 0 and less than or equal to 1.
- TTC less than 1s and “THW less than 1s” are collision risk events (obj), which can predict the time of collision and select a period of time near the predicted collision time (t event) Calculate the collision risk value (the probability of collision) in the period of time, and take the largest value of the multiple collision risk values (ie, f(t) max ) in the period as the collision risk value of the period.
- obtaining r b and r c includes:
- FIG. 8 is a schematic diagram showing the risk in a traffic light scene provided by the present application.
- the risk of the traffic signal scene is mainly in violation of the rules indicated by the traffic lights, and the corresponding risk value can be obtained according to the speed of the vehicle.
- the vehicle distance traffic signal 50m, the distance s of the vehicle from the intersection stop line, the speed v of the vehicle, and the acceleration a of the vehicle constitute the current vehicle state.
- the picture on the right shows the distance-speed curve of the vehicle.
- the traffic signal is forbidden, the vehicle needs to decelerate and reduce the speed to 0 before stopping the line. Different accelerations form different speed-distance curves.
- the normal acceleration refers to an acceleration whose absolute value is smaller than the absolute value of the preset acceleration, for example, The preset acceleration value is 0.4g. If the vehicle adopts an acceleration of -0.3g to stop before stopping the line, the r b or r c of the vehicle is equal to 0.
- the self-vehicle When the data-distance curve of the self-vehicle is located between the curve 1 and the curve 2 shown in Fig. 8, the self-vehicle has a hidden risk of violating the traffic signal. At this time, the self-vehicle needs to reduce the speed to 0 before stopping the line. The larger the absolute value of the acceleration, the larger the value of r b or r c .
- h(t event ,Light) indicates the occurrence of traffic light risk event (Light).
- the normalized parameter is used to represent the weight of the parameter. For example, each normalized parameter may take a value greater than or equal to 0 and less than or equal to 1.
- the traffic signal at the intersection shows the forbidden signal.
- the speed of the vehicle is 100km/h, there is a great probability of illegal traffic lights (such as red light).
- the above-mentioned vehicle is 50m away from the intersection.
- the traffic light at the intersection shows the forbidden signal.
- the speed of the vehicle is 100km/h, which is the traffic light risk event (Light). It can predict the red light moment and select the time period (t event ) near the predicted red light time.
- the red light risk value (the probability of a red light occurrence) during the period of time, taking the largest value among the multiple red light risk values (ie, f(t) max ) during that period as the red light risk value for the period of time.
- obtaining r b and r c includes:
- r b and r c are determined according to the estimated vehicle exit time, wherein the estimated vehicle exit time is negatively correlated with r b or r c .
- Lane time is negatively correlated with r b or r c .
- r SG3 is r b or r c in the scene of the road exiting the road
- the TLC threshold indicates the critical value of the time required for the vehicle to rush out of the road
- e is a natural constant
- t lc is the predicted vehicle rushing out of the lane time
- y is the lateral offset of the vehicle at the road boundary
- W road is the width of the road
- v long is the longitudinal speed of the vehicle
- v lat is the lateral direction of the vehicle Speed
- ⁇ - ⁇ d represents the angle between the heading of the vehicle and the tangential direction of the road
- r 4 is the normalized parameter
- the normalized parameter is used to represent the weight of the parameter.
- obtaining r b and r c includes:
- determining r b and r c according to at least one of the following parameters: longitudinal acceleration of the vehicle, lateral acceleration of the vehicle, and vehicle Speed, where the longitudinal acceleration of the vehicle is positively correlated with r b or r c , the lateral acceleration of the vehicle is positively correlated with r b or r c , and the speed of the vehicle is positively correlated with r b or r c .
- the above longitudinal acceleration refers to an acceleration in which the acceleration direction is the same or opposite to the speed direction (for example, an acceleration obtained by stepping on a throttle or braking on a straight line), and the lateral acceleration refers to an angle in which the acceleration direction and the speed direction have an angle other than zero. Acceleration (for example, acceleration that causes the vehicle to drift when cornering).
- the longitudinal acceleration of the vehicle is positively correlated with r b or r c ; the greater the lateral acceleration, the greater the probability of a sharp turn of the vehicle. Therefore, the lateral acceleration of the vehicle is r b or r c is positively correlated; the greater the speed of the vehicle, the greater the probability of unconventional driving such as overspeed, and therefore, the speed of the vehicle is positively correlated with r b or r c .
- the threshold of acceleration may be, for example, 0.5 g
- a threshold 2 represents a threshold of longitudinal acceleration in a safe driving state, for example, may be 0.7 g
- v represents the speed of the vehicle
- v safe represents a speed threshold in a safe driving state, and may refer to the road.
- the speed limit is determined and r 5 is the normalized parameter.
- the automatic driving safety evaluation method provided by the present application is described in detail above. If the information received by the cloud device 420 from the vehicle end device 410 is r b or r c , the cloud device 420 does not need to perform S611 to S614 again, if the cloud device 420 The information received by the vehicle end device 410 is sensor-aware data, and the cloud device 420 also needs to execute S611 to S614 to acquire r b and r c .
- FIG. 9 illustrates a method for determining driving risk of a vehicle provided by the present application.
- the method 900 is applied to a vehicle having an automatic driving mode and a manual driving mode, and the method 900 includes:
- S910 Determine a driving mode and a risk scene of the vehicle in a time period, where the starting point of the time period is a first time, and the end of the time period is a second time, the driving mode includes an automatic driving mode and/or a manual driving mode. .
- S920 Determine, according to the risk identification manner corresponding to the risk scenario, the risk value of the driving mode in the time period, where the current time is after the first time, and the current time is before the second time.
- the method 900 can be performed, for example, by the vehicle end device 410, which selects a time period among a plurality of time periods required for the vehicle to travel a certain distance, and utilizes a time (ie, the current time) within the time period.
- the known driving information and the predicted driving information determine the risk value of the corresponding driving mode in the time period, for example, the determined time period is 1 second, the current time is 0.4 seconds, and if the speeding has occurred within 0.4 seconds has elapsed
- the vehicle can determine that the unconventional driving risk value of the driving mode in the time period is 1, that is, the probability of occurrence of overspeed is 100%; if the overspeeding situation does not occur within 0.4 seconds, and the vehicle is based on the current
- the speed and acceleration of the vehicle determine that the vehicle speed will remain within the legal travel speed in the next 0.6 seconds, and the vehicle can determine that the unconventional driving risk value for the time period is a value less than one, that is, the probability of overspeed occurring is less than 100%.
- the embodiment divides the time period required to travel a certain distance into a plurality of time segments, and determines according to the traffic context in each time period (ie, information related to the traffic environment before the current time and after the current time) The risk value of the driving mode in each time period, so that the risk can be more objectively identified.
- the manner of determining a time period is merely an example.
- the vehicle end device 410 can also determine a time period by other methods, for example, determining the time required to travel a distance according to the distance and speed, which is described in S910. "One time period"; for example, the first time and the second time are determined at the current time, thereby determining "one time period" described in S910.
- the above embodiment may determine the risk value of the automatic driving mode and/or the manual driving mode, wherein the automatic driving mode includes a shadow driving mode, and the safety of the automatic driving system based on the risk value of the shadow driving mode is the automatic driving safety provided by the present application.
- the evaluation method can achieve an important factor in better technical effects than the prior art.
- the purpose of the shadow driving mode is to generate the simulated autopilot track by software and hardware running in the background in the manual driving mode to identify the potential risks of the autopilot system, thereby further identifying the hidden dangers of the autopilot system and accumulating safety assessments. Required data.
- the automatic driving control trajectory generated directly based on the sensor's sensory data cannot represent the true risk level of the automatic driving system, and may cause distortion of the automatic driving safety assessment result.
- Figure 10 shows a schematic diagram of a self-driving control vehicle trajectory.
- the dotted arrow indicates the actual trajectory of the vehicle in the manual driving mode
- the solid arrow indicates the automatic driving trajectory simulated according to the sensor's sensory data. Since the sensing data indicates that there is a car in the left lane, the automatic driving system will simulate continuing along the original lane. The trajectory, in fact, the vehicle does not travel along the original lane, but has changed lanes.
- the above-mentioned automatic driving trajectory simulated according to the sensor's sensory data cannot reflect the real driving situation, resulting in distortion of the trajectory-based automatic driving safety assessment result.
- the method 900 further includes:
- S901 Determine a position and a motion parameter of the vehicle at the first moment, wherein the vehicle travels based on the manual driving mode at the first moment.
- S903 predict a shadow driving mode of the vehicle from a position of the first moment to a position of the third moment according to the position of the first moment and the position of the third moment, where the shadow driving mode is based on the position and motion parameter prediction of the vehicle in the manual driving mode.
- the automatic driving mode of the vehicle is based on the position and motion parameter prediction of the vehicle in the manual driving mode.
- S920 includes:
- S921 Determine r a , r b , and r c according to a risk identification manner corresponding to the risk scene at a current moment, where r a is a risk of the artificial driving mode in the time period, and r b is a shadow driving mode in the time period
- the risk, r c is the risk of the automatic driving mode based on the preset route during the time period.
- the vehicle may predict the position of the vehicle after a period of time according to the position and the motion parameter of the vehicle at the first moment, that is, predict the position of the vehicle at the third moment, wherein the vehicle is in the manual driving state at the first moment, and then, the vehicle prediction The shadow driving mode of the vehicle from the position at the first moment to the position at the third moment.
- the above embodiment continuously corrects the route of the automatic driving according to the position and the motion parameter in the manual driving mode, so that the route of the automatic driving mode is closer to the route of the manual driving mode, so that more objective automatic driving risk data can be acquired.
- the third time may be the current time described in S920, or may be the second time, or may be a time after the second time.
- FIG. 11 shows a flow chart of a shadow driving mode provided by the present application.
- the shadow driving module 414 identifies the driver's driving intention, ie, the content described in S901 to S903, based on the vehicle's motion parameters (eg, speed and acceleration) at the first moment, and then identifies the driver's driving intention.
- a shadow trajectory is generated as input information of the shadow driving mode 414, thereby improving the degree of coincidence of the shadow trajectory with the driver's controlled vehicle trajectory.
- the driver's driving intentions described above include, but are not limited to, at least one of driving, turning, and changing lanes along the current lane.
- FIG. 12 shows a flow chart of a method for identifying a driver's intention provided by the present application.
- the above embodiment uses the current information to predict the information of the future time, and uses the predicted future time information to determine the driving intention of the current driver, thereby eliminating the deviation caused by the driver's intention based on the sensor's sensing data.
- FIG. 13 is a schematic diagram of predicting a shadow trajectory based on a shadow driving mode provided by the present application.
- the shadow trajectory shown in Figure 13 is roughly the same as the driver's trajectory, but slightly different. According to the driver's trajectory and shadow trajectory, different risk values are obtained. The difference between the risk values can be used to evaluate the artificial driving mode and the automatic driving mode. Which is more secure.
- the driver's driving intention is a risk intention (eg, an aggressive cut-in)
- a risk intention e.g, an aggressive cut-in
- a safer decision is selected (eg, , keep the lane in the lane) to generate a shadow track.
- FIG. 11 A schematic diagram of the shadow trajectory risk value calculated by the step of calculating the shadow trajectory shown in FIG. 11 is shown in FIG.
- the solid line indicates the risk value obtained by the driver-controlled vehicle trajectory calculated in accordance with S611 to S614, and the broken line corresponds to each time segment calculated according to S611 to S614 (for example, the time period from the first time to the third time).
- the risk value of the shadow track is the risk value obtained by the driver-controlled vehicle trajectory calculated in accordance with S611 to S614, and the broken line corresponds to each time segment calculated according to S611 to S614 (for example, the time period from the first time to the third time).
- Example 2 the driver's car track continues to fall into the risk zone, and the shadow track returns from the risk zone to the safe zone, indicating that the risk of driving is reduced by the system's analog car control.
- This data can be used as an autonomous driving system with better safety than the driver. An evidence of car control.
- Example 3 both the driver's control track and the shadow-controlled track continue to fall into the risk zone, but the risk value of the shadow track is less than the driver's control, indicating that the driving control is reduced by the analog control of the automatic driving system.
- the driving system safety performance is superior to the driver's control of the car.
- the data in the three examples above can be used as data for the automated driving safety assessment of method 600.
- the shadow trajectory generation method is consistent with the automatic driving control algorithm; the risk calculation method of the shadow trajectory is consistent with the automatic driving/driver driving trajectory risk calculation method, thereby ensuring the risk of the shadow trajectory.
- the method 900 further includes:
- the vehicle can send the above risk data to the safety center for the safety center to evaluate the safety of the manual driving mode and the shadow driving mode, and reduce the workload of the vehicle processor.
- the above security center is, for example, a cloud device 420.
- the risk control module 416 reduces or eliminates the risk.
- FIG. 15 is a schematic flowchart of a method for risk control provided by the present application.
- the risk scene data includes, but is not limited to, at least one of the sensor data acquired by the sensor 411, the trajectory generated by the autopilot module 413, and the trajectory generated by the shadow driving module 414, and the risk identification module 415 confirms according to the risk scene data described above. Whether the current scenario is a risk scenario. If the risk identification module 415 confirms that the current scenario is a non-risk scenario and discovers that the current scenario is actually a risk scenario, the risk identification mechanism may be adjusted, for example, adjusting the risk identification threshold parameter and modifying the risk identification algorithm. Wait.
- the risk identification module 415 confirms that the current scenario is a risk scenario, and determines whether the risk scenario is caused by a hidden danger of the automatic driving system, the risk identification module 415 reports the risk scenario to the risk policy module 421, and the risk policy module 421 sends the risk policy module 421 to the risk control module 416.
- the policy update information updates the risk control strategy of the vehicle end device 410 to exclude the risk scene from the security scene fence.
- the method 900 further includes:
- S940 Receive security scene information sent by the security center, where the security scenario information is used to indicate a safe driving traffic scene set of the automatic driving mode.
- S950 Determine a safe driving traffic scene set of the automatic driving mode according to the safety scenario information.
- S960 If the current traffic scene does not belong to the safe driving traffic scene set, the vehicle plays and/or displays prompt information prompting the driver to perform manual driving, and/or the vehicle performs safe docking processing.
- the security center may identify the risk scenario according to the risk data reported by the vehicle, and update the risk identification mechanism and the risk control strategy of the vehicle by transmitting the security scenario information to the vehicle.
- the security scenario information may be Instructing the vehicle to exclude the risk scene from the safe driving traffic scene set, and updating the safe driving traffic scene set after receiving the safety scene information, and if the current traffic scene does not belong to the updated safe driving traffic scene set, the vehicle adopts safety measures. For example, playing and/or displaying a prompt message prompting the driver to perform manual driving, and/or performing a safe docking process.
- the above-mentioned safe driving traffic scene set may also be referred to as a safe driving scene fence, which is composed of an environment, a static scene, a dynamic scene, and a self-driving behavior.
- a safe driving scene fence which is composed of an environment, a static scene, a dynamic scene, and a self-driving behavior.
- the definition of the scene fence is as shown in FIG. 16.
- the contents in the parentheses are the contents of the safe driving scene fence of the automatic driving system.
- the cloud device 420 timely transmits the security scene information according to the identified hidden danger of the automatic driving system, and updates the content of the safe driving scene fence.
- the risk control module 416 obtains the recognition result of each element of the scene fence according to the map input and the real-time perception result, and updates the surrounding content of the safe driving scene fence according to the security scene information sent by the cloud device 420, in the automatic driving module 413.
- the risk control module 416 determines that the current scene exceeds the fence of the safe driving scene, the driver is reminded to take over, or the vehicle is controlled to be safely parked.
- a risk event is due to the intersection width greater than 20m, resulting in the system not being able to accurately identify a certain type of traffic light, causing potential risks in the updated safe driving scene fence
- the width of the intersection is less than 20m.
- a risk event is due to the fact that the autopilot system is unable to measure the speed of the low speed vehicle in time, causing a potential risk, then in the updated safe driving scene fence, the dynamic scene
- the traffic speed is applicable to 10 to 50 kilometers per hour (kph).
- the automatic driving system detects that the current traffic is less than 10kph, the automatic driving system prompts the driver to take over the car, or gently pulls to the side to stop.
- driver's ring strategy can be adjusted, such as adjusting the reminder interval.
- vehicle end device and the cloud device include corresponding hardware structures and/or software modules for performing various functions in order to implement the above functions.
- the present application can be implemented in a combination of hardware or hardware and computer software in combination with the elements and algorithm steps of the various examples described in the embodiments disclosed herein. Whether a function is implemented in hardware or computer software to drive hardware depends on the specific application and design constraints of the solution. A person skilled in the art can use different methods to implement the described functions for each particular application, but such implementation should not be considered to be beyond the scope of the present application.
- the present application may divide the functional units of the vehicle end device and the cloud device according to the above method example.
- each functional unit may be divided according to each function, or two or more functions may be integrated into one processing unit.
- the above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. It should be noted that the division of the unit in the present application is schematic, and is only a logical function division, and the actual implementation may have another division manner.
- Fig. 17 shows a possible structural diagram of the vehicle end device involved in the above embodiment.
- the vehicle end device 1700 includes a processing unit 1702 and a communication unit 1703.
- the processing unit 1702 is for controlling management of the actions of the vehicle end device 1700, for example, the processing unit 1702 is configured to support the vehicle end device 1700 to perform S620 and/or other processes for the techniques described herein.
- the communication unit 1703 is configured to support communication between the vehicle end device 1700 and the cloud device.
- the vehicle end device 1700 can also include a storage unit 1701 for storing program codes and data of the vehicle end device 1700.
- the processing unit 1702 is configured to execute S610-S630, wherein when the processing unit 1702 executes S610, the processing unit 1702 may also be referred to as the obtaining unit 1702.
- the storage unit 1701 needs to store the intermediate data generated by the processing unit 1702 in the process of performing the above steps, and the intermediate data is, for example, r b and r c , so that the processing unit 1702 is based on the risk value of the shadow driving mode in the plurality of units of measurement and based on The risk value of the automatic driving mode of the preset route determines whether the safety of the vehicle's automatic driving mode meets the requirements.
- the processing unit 1702 is configured to execute S610, and control the communication unit 1703 to send r b and r c to the cloud device, so that the cloud device performs S620-S630.
- the processing unit 1702 is configured to control the communication unit 1703 to send original data (for example, sensor data) for calculating r b and r c to the cloud device, so that the cloud device performs S610-S630. .
- the processing unit 1702 is configured to perform S910 and S920.
- the processing unit 1702 is configured to control the communication unit 1703 to execute S930.
- the processing unit 1702 may be a processor or a controller, such as a central processing unit (CPU), a general purpose processor, a digital signal processor (DSP), and an application-specific integrated circuit. , ASIC), field programmable gate array (FPGA) or other programmable logic device, transistor logic device, hardware component, or any combination thereof. It is possible to implement or carry out the various illustrative logical blocks, modules and circuits described in connection with the present disclosure.
- the processor may also be a combination of computing functions, for example, including one or more microprocessor combinations, a combination of a DSP and a microprocessor, and the like.
- the communication unit 1703 may be a transceiver or the like.
- the storage unit 1701 may be a memory.
- the vehicle end device referred to in the present application may be the vehicle end device 1800 shown in FIG.
- the vehicle end device 1800 includes a processor 1802, a transceiver 1803, and a memory 1801.
- the transceiver 1803, the processor 1802, and the memory 1801 can communicate with each other through an internal connection path to transfer control and/or data signals.
- FIG. 19 shows a possible structural diagram of the cloud device involved in the above embodiment.
- the cloud device 1900 includes a processing unit 1902 and a communication unit 1903.
- the processing unit 1902 is configured to control and manage the actions of the cloud device 1900.
- the processing unit 1902 is configured to support the cloud device 1900 to execute S620 and/or other processes for the techniques described herein.
- the communication unit 1903 is configured to support communication between the cloud device 1900 and the vehicle end device.
- the cloud device 1900 may further include a storage unit 1901 for storing program codes and data of the cloud device 1900.
- the processing unit 1902 is configured to control the communication unit 1903 to execute S610, that is, receive r b and r c from the vehicle end device.
- the communication unit 1903 may also be referred to as an obtaining unit 1903, and the processing unit 1902 is also used to execute S620 to S641.
- the storage unit 1901 needs to store intermediate data generated by the processing unit 1902 in the process of performing the above steps, the intermediate data being, for example, r b and r c , so that the processing unit 1902 is based on the risk value of the shadow driving mode in a plurality of units of measurement and based on The risk value of the automatic driving mode of the preset route determines whether the safety of the vehicle's automatic driving mode meets the requirements.
- the processing unit 1902 is configured to control the communication unit 1903 to receive raw data (eg, sensor data) for calculating r b and r c from the vehicle end device, and the processing unit 1902 is further configured to perform communication according to the communication.
- the raw data received by the unit 1903 is obtained by r b and r c .
- the processing unit 1902 may also be referred to as an obtaining unit 1902.
- the processing unit 1902 is further configured to execute S620-S641, and the storage unit 1901 needs to store the processing unit 1902 to perform the above steps.
- the intermediate data generated in the process is, for example, r b and r c , so that the processing unit 1902 determines the vehicle based on the risk value of the shadow driving mode in the plurality of measurement units and the risk value of the automatic driving mode based on the preset route. Whether the safety of the automatic driving mode meets the requirements.
- the processing unit 1902 is configured to execute S910 and S920.
- the processing unit 1902 is configured to control the communication unit 1903 to execute S930.
- Processing unit 1902 can be a processor or controller, such as a CPU, general purpose processor, DSP, ASIC, FPGA or other programmable logic device, transistor logic device, hardware component, or any combination thereof. It is possible to implement or carry out the various illustrative logical blocks, modules and circuits described in connection with the present disclosure.
- the processor may also be a combination of computing functions, for example, including one or more microprocessor combinations, a combination of a DSP and a microprocessor, and the like.
- the communication unit 1903 may be a transceiver or the like.
- the storage unit 1901 may be a memory.
- the cloud device involved in the present application may be the cloud device 2000 shown in FIG.
- the cloud device 2000 includes a processor 2002, a transceiver 2003, and a memory 2001.
- the transceiver 2003, the processor 2002, and the memory 2001 can communicate with each other through an internal connection path to transfer control and/or data signals.
- the present application also provides a system for assessing the safety of automated driving, comprising one or more of the aforementioned vehicle end devices, and one or more cloud devices.
- the processor in the present application may be an integrated circuit chip having signal processing capabilities.
- each step of the foregoing method embodiment may be completed by an integrated logic circuit of hardware in a processor or an instruction in a form of software.
- the above processor may be a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or the like. Programming logic devices, discrete gates or transistor logic devices, discrete hardware components.
- the methods, steps, and logical block diagrams disclosed in the embodiments of the present application can be implemented or executed.
- the general purpose processor may be a microprocessor or the processor or any conventional processor or the like.
- the steps of the method disclosed in the embodiments of the present application may be directly implemented by the hardware decoding processor, or may be performed by a combination of hardware and software modules in the decoding processor.
- the software module can be located in a conventional storage medium such as random access memory, flash memory, read only memory, programmable read only memory or electrically erasable programmable memory, registers, and the like.
- the storage medium is located in the memory, and the processor reads the information in the memory and combines the hardware to complete the steps of the above method.
- the memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memory.
- the non-volatile memory may be a read-only memory (ROM), a programmable read only memory (ROMM), an erasable programmable read only memory (erasable PROM, EPROM), or an electrical Erase programmable EPROM (EEPROM) or flash memory.
- the volatile memory can be a random access memory (RAM) that acts as an external cache.
- RAM random access memory
- RAM random access memory
- many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (Synchronous DRAM).
- SDRAM double data rate synchronous DRAM
- DDR SDRAM double data rate synchronous DRAM
- ESDRAM enhanced synchronous dynamic random access memory
- SLDRAM synchronously connected dynamic random access memory
- DR RAM direct memory bus random access memory
- the present application also provides a computer readable medium having stored thereon a computer program that, when executed by a computer, implements the functions of any of the method embodiments described above.
- the application also provides a computer program product that, when executed by a computer, implements the functions of any of the method embodiments described above.
- the computer program product includes one or more computer instructions.
- the computer can be a general purpose computer, a special purpose computer, a computer network, or other programmable device.
- the computer instructions can be stored in a computer readable storage medium or transferred from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions can be from a website site, computer, server or data center Transmission to another website site, computer, server or data center via wired (eg coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (eg infrared, wireless, microwave, etc.).
- the computer readable storage medium can be any available media that can be accessed by a computer or a data storage device such as a server, data center, or the like that includes one or more available media.
- the usable medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a high-density digital video disc (DVD)), or a semiconductor medium (eg, a solid state disk, SSD)) and so on.
- a magnetic medium eg, a floppy disk, a hard disk, a magnetic tape
- an optical medium eg, a high-density digital video disc (DVD)
- DVD high-density digital video disc
- SSD solid state disk
- the present application also provides a processing apparatus including a processor and an interface, and the processor is configured to perform the steps described in any of the foregoing method embodiments.
- the foregoing processing device may be a chip, and the processor may be implemented by hardware or by software.
- the processor may be a logic circuit, an integrated circuit, etc.;
- the processor may be a general purpose processor implemented by reading software code stored in the memory.
- the memory may be integrated in the processor and may exist independently of the processor.
- system and “network” are used interchangeably herein.
- the term “and/or” in this context is merely an association describing the associated object, indicating that there may be three relationships, for example, A and / or B, which may indicate that A exists separately, and both A and B exist, respectively. B these three situations.
- the character "/" in this article generally indicates that the contextual object is an "or" relationship.
- B corresponding to A means that B is associated with A, and B can be determined according to A.
- determining B from A does not mean that B is only determined based on A, and that B can also be determined based on A and/or other information.
- the disclosed systems, devices, and methods may be implemented in other manners.
- the device embodiments described above are merely illustrative.
- the division of cells is only a logical function division.
- multiple units or components may be combined or integrated. Go to another system, or some features can be ignored or not executed.
- the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device or unit, or an electrical, mechanical or other form of connection.
- the units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the objectives of the embodiments of the present application.
- each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
- the above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.
- Computer readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one location to another.
- a storage medium may be any available media that can be accessed by a computer.
- computer readable media may comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, disk storage media or other magnetic storage device, or can be used for carrying or storing in the form of an instruction or data structure.
- Any connection may suitably be a computer readable medium.
- the software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave
- the coaxial cable , fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, wireless, and microwave are included in the fixing of the associated media.
- a disk and a disc include a compact disc (CD), a laser disc, a compact disc, a digital versatile disc (DVD), a floppy disc, and a Blu-ray disc, wherein the disc is usually magnetically copied, and the disc is The laser is used to optically replicate the data. Combinations of the above should also be included within the scope of the computer readable media.
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Abstract
Description
Claims (32)
- 一种自动驾驶安全评估的方法,其特征在于,包括:获取r b和r c,其中,r b为第一计量单位内车辆的影子驾驶模式的风险值,r c为所述第一计量单位内所述车辆基于预设路线的自动驾驶模式的风险值,所述影子驾驶模式为所述车辆基于实时路线的自动驾驶模式,所述实时路线为根据处于人工驾驶模式的所述车辆的位置和运动参数预测的路线,所述第一计量单位为时间段或路程;根据r b确定R B,R B为所述车辆在多个计量单位内的影子驾驶模式的风险值,所述多个计量单位包括所述第一计量单位;根据r c确定R C,R C为所述车辆在所述多个计量单位内的基于预设路线的自动驾驶模式的风险值;其中,R B和R C用于确定所述车辆的自动驾驶模式的安全性是否符合要求。
- 根据权利要求1所述的方法,其特征在于,所述方法还包括:计算R B与R C之和;当所述R B与R C之和小于或等于自动驾驶风险阈值时,确定所述车辆的自动驾驶模式的安全性符合要求。
- 根据权利要求2所述的方法,其特征在于,所述自动驾驶风险阈值为a(R A+R′ A),其中,a为风险容忍系数,R A为所述车辆在所述多个计量单位内的人工驾驶的模式的风险值,所述车辆在所述多个计量单位内通过的路段为第一路段,R′ A为所述车辆在所述多个计量单位之外基于人工驾驶模式通过所述第一路段的风险值。
- 根据权利要求1至4中任一项所述的方法,其特征在于,所述获取r b和r c,包括:当风险场景为碰撞场景时,根据下列参数中的至少一个确定r b和r c:所述车辆相对于碰撞物的速度Δv、基于所述碰撞物的运动轨迹预估的所述车辆与所述碰撞物发生碰撞所需的时间TTC、基于所述车辆与所述碰撞物之间的距离预估的所述车辆的刹车时间THW、以及所述车辆与所述碰撞物之间的距离,其中,Δv与r b或r c正相关,TTC与r b或r c负相关,THW与r b或r c负相关,所述车辆与所述碰撞物之间的距离与r b或r c负相关。
- 根据权利要求5所述的方法,其特征在于,所述根据下列参数中的至少一个确定r b和r c:所述车辆相对于碰撞物的速度Δv、基于所述碰撞物的运动轨迹预估的所述车辆与所述碰撞物发生碰撞所需的时间TTC、基于所述车辆与所述碰撞物之间的距离预估的所述车辆的刹车时间THW、以及所述车辆与所述碰撞物之间的距离,包括:
- 根据权利要求1至4中任一项所述的方法,其特征在于,所述获取r b和r c,包括:当风险场景为交通信号灯场景时,根据所述车辆的速度以及所述车辆到交通信号灯的距离确定r b和r c,其中,所述车辆的速度与r b或r c正相关,所述车辆到交通信号灯的距离与r b或r c负相关。
- 根据权利要求1至4中任一项所述的方法,其特征在于,所述获取r b和r c,包括:当风险场景为冲出道路边界场景时,根据预估的所述车辆冲出车道时间确定r b和r c,其中,所述预估的所述车辆冲出车道时间与r b或r c负相关。
- 根据权利要求1至4中任一项所述的方法,其特征在于,所述获取r b和r c,包括:当风险场景为横向加速度行驶场景时,根据下列参数中的至少一个确定r b和r c:所述车辆的纵向加速度、所述车辆的横向加速度以及所述车辆的速度,其中,所述车辆的纵向加速度与r b或r c正相关,所述车辆的横向加速度与r b或r c正相关,所述车辆的速度与r b或r c正相关。
- 根据权利要求11所述的方法,其特征在于,所述根据下列参数中的至少一个确定r b和r c:所述车辆的纵向加速度、所述车辆的横向加速度以及所述车辆的速度,包括:根据r SG4=r 5确定r b和r c,r SG4为非常规行驶场景中的r b或r c,其中,(|a long|>a threshold 1)||(|a lat|>a threshold 2)||(v≥v safe),a long表示所述车辆的纵向加速度,a lat表示所述车辆的横向加速度,a threshold 1表示安全行驶状态下横向加速度的阈值,a threshold 2表示安全行驶状态下纵向加速度的阈值,v表示所述车辆的速度,v safe表示安全行驶状态下的速度阈值,r 5为归一化参数。
- 一种自动驾驶安全评估的装置,其特征在于,包括处理单元和获取单元,所述获取单元用于获取r b和r c,其中,r b为第一计量单位内车辆的影子驾驶模式的风险值,r c为所述第一计量单位内所述车辆基于预设路线的自动驾驶模式的风险值,所述影子驾驶模式为所述车辆基于实时路线的自动驾驶模式,所述实时路线为根据处于人工驾驶模式的所述车辆的位置和运动参数预测的路线,所述第一计量单位为时间段或路程;所述处理单元用于根据所述获取单元获取的r b确定R B,R B为所述车辆在多个计量单位内的影子驾驶模式的风险值,所述多个计量单位包括所述第一计量单位;所述处理单元还用于根据所述获取单元获取的根据r c确定R C,R C为所述车辆在所述多个计量单位内的基于预设路线的自动驾驶模式的风险值;其中,R B和R C用于确定所述车辆的自动驾驶模式的安全性是否符合要求。
- 根据权利要求13所述的装置,其特征在于,所述处理单元还用于:计算R B与R C之和;当所述R B与R C之和小于或等于自动驾驶风险阈值时,确定所述车辆的自动驾驶模式的安全性符合要求。
- 根据权利要求14所述的装置,其特征在于,所述自动驾驶风险阈值为a(R A+R′ A),其中,a为风险容忍系数,R A为所述车辆在所述多个计量单位内的人工驾驶的模式的风险值,所述车辆在所述多个计量单位内通过的路段为第一路段,R′ A为所述车辆在所述多个计量单位之外基于人工驾驶模式通过所述第一路段的风险值。
- 根据权利要求13至16中任一项所述的装置,其特征在于,所述获取单元具体用于:当风险场景为碰撞场景时,根据下列参数中的至少一个确定r b和r c:所述车辆相对于碰撞物的速度Δv、基于所述碰撞物的运动轨迹预估的所述车辆与所述碰撞物发生碰撞所需的时间TTC、基于所述车辆与所述碰撞物之间的距离预估的所述车辆的刹车时间THW、以及所述车辆与所述碰撞物之间的距离,其中,Δv与r b或r c正相关,TTC与r b或r c负相关,THW与r b或r c负相关,所述车辆与所述碰撞物之间的距离与r b或r c负相关。
- 根据权利要求17所述的装置,其特征在于,所述获取单元具体还用于:
- 根据权利要求13至16中任一项所述的装置,其特征在于,所述获取单元具体用于:当风险场景为交通信号灯场景时,根据所述车辆的速度以及所述车辆到交通信号灯的距离确定r b和r c,其中,所述车辆的速度与r b或r c正相关,所述车辆到交通信号灯的距离与r b或r c负相关。
- 根据权利要求13至16中任一项所述的装置,其特征在于,所述获取单元具体用于:当风险场景为冲出道路边界场景时,根据预估的所述车辆冲出车道时间确定r b和r c,其中,所述预估的所述车辆冲出车道时间与r b或r c负相关。
- 根据权利要求13至16中任一项所述的装置,其特征在于,所述获取单元具体用于:当风险场景为横向加速度行驶场景时,根据下列参数中的至少一个确定r b和r c:所述车辆的纵向加速度、所述车辆的横向加速度以及所述车辆的速度,其中,所述车辆的纵向 加速度与r b或r c正相关,所述车辆的横向加速度与r b或r c正相关,所述车辆的速度与r b或r c正相关。
- 根据权利要求23所述的装置,其特征在于,所述获取单元具体还用于:根据r SG4=r 5确定r b和r c,r SG4为非常规行驶场景中的r b或r c,其中,(|a long|>a threshold 1)||(|a lat|>a threshold 2)||(v≥v safe),a long表示所述车辆的纵向加速度,a lat表示所述车辆的横向加速度,a threshold 1表示安全行驶状态下横向加速度的阈值,a threshold 2表示安全行驶状态下纵向加速度的阈值,v表示所述车辆的速度,v safe表示安全行驶状态下的速度阈值,r 5为归一化参数。
- 一种确定车辆驾驶风险的方法,其特征在于,所述方法包括:确定一个时间段内车辆的驾驶模式和风险场景,所述时间段的起始点为第一时刻,所述时间段的终点为第二时刻,所述驾驶模式包括自动驾驶模式和/或人工驾驶模式;在当前时刻根据风险场景对应的风险识别方式确定所述时间段内所述驾驶模式的风险值,其中,所述当前时刻位于所述第一时刻之后,且所述当前时刻位于所述第二时刻之前。
- 根据权利要求25所述的方法,其特征在于,所述在当前时刻根据风险场景对应的风险识别方式确定所述时间段内所述驾驶模式的风险值之前,所述方法还包括:确定所述车辆在所述第一时刻的位置和运动参数,其中,车辆在所述第一时刻基于人工驾驶模式行驶;根据所述车辆在第一时刻的位置和运动参数预测车辆在第三时刻的位置,所述第三时刻晚于所述第一时刻;根据所述第一时刻的位置和所述第三时刻的位置预测所述车辆从所述第一时刻的位置到所述第三时刻的位置的影子驾驶模式,所述影子驾驶模式为基于所述车辆处于人工驾驶模式下的位置和运动参数预测的所述车辆的自动驾驶模式;所述在当前时刻根据所述风险场景对应的风险识别方式确定所述时间段内所述驾驶模式的风险值,包括:在所述当前时刻根据所述风险场景对应的风险识别方式确定r a、r b和r c,其中,r a为所述时间段内所述人工驾驶模式的风险值,r b为所述时间段内所述影子驾驶模式的风险值,r c为所述时间段内基于预设路线的自动驾驶模式的风险值。
- 一种确定车辆驾驶风险的装置,其特征在于,所述装置包括处理单元和存储单元,所述存储单元存储了计算机程序代码,当所述计算机程序代码被所述处理单元或执行时,所述处理单元用于:确定一个时间段内车辆的驾驶模式和风险场景,所述时间段的起始点为第一时刻,所述时间段的终点为第二时刻,所述驾驶模式包括自动驾驶模式和/或人工驾驶模式;在当前时刻根据风险场景对应的风险识别方式确定所述时间段内所述驾驶模式的风险值,其中,所述当前时刻位于所述第一时刻之后,且所述当前时刻位于所述第二时刻之前。
- 根据权利要求27所述的装置,其特征在于,所述在当前时刻根据风险场景对应的风险识别方式确定所述时间段内所述驾驶模式的风险值之前,所述处理单元还用于:确定所述车辆在所述第一时刻的位置和运动参数,其中,车辆在所述第一时刻基于人工驾驶模式行驶;根据所述车辆在第一时刻的位置和运动参数预测车辆在第三时刻的位置,所述第三时刻晚于所述第一时刻;根据所述第一时刻的位置和所述第三时刻的位置预测所述车辆从所述第一时刻的位置到所述第三时刻的位置的影子驾驶模式,所述影子驾驶模式为基于所述车辆处于人工驾驶模式下的位置和运动参数预测的所述车辆的自动驾驶模式;所述处理单元具体用于:在所述当前时刻根据所述风险场景对应的风险识别方式确定r a、r b和r c,其中,r a为所述时间段内所述人工驾驶模式的风险值,r b为所述时间段内所述影子驾驶模式的风险值,r c为所述时间段内基于预设路线的自动驾驶模式的风险值。
- 一种自动驾驶安全评估系统,其特征在于,包括风险控制模块、风险策略模块和安全评估模块,所述风险控制模块用于执行权利要求1至12中任一项所述的方法,确定自动驾驶模式的风险;所述风险策略模块用于根据所述风险控制模块确定的所述自动驾驶模式的风险确定风险控制策略,所述风险控制策略用于减小或消除所述自动驾驶模式的风险;所述安全评估策略用于根据所述风险控制模块确定的所述自动驾驶模式的风险评估所述自动驾驶模式的安全性。
- 一种自动驾驶安全评估系统,其特征在于,包括风险控制模块、风险策略模块和安全评估模块,所述风险控制模块用于执行权利要求25或26所述的方法,确定自动驾驶模式的风险;所述风险策略模块用于根据所述风险控制模块确定的所述自动驾驶模式的风险确定风险控制策略,所述风险控制策略用于减小或消除所述自动驾驶模式的风险;所述安全评估策略用于根据所述风险控制模块确定的所述自动驾驶模式的风险评估所述自动驾驶模式的安全性。
- 一种可读存储介质,包括程序或指令,当所述程序或指令在计算机上运行时,如权利要求1至12中任意一项所述的方法被执行。
- 一种可读存储介质,包括程序或指令,当所述程序或指令在计算机上运行时,如权利要求25或26所述的方法被执行。
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Also Published As
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JP2021516816A (ja) | 2021-07-08 |
CN110276985A (zh) | 2019-09-24 |
KR102574105B1 (ko) | 2023-09-06 |
JP7227267B2 (ja) | 2023-02-21 |
EP3745378A1 (en) | 2020-12-02 |
KR20200118882A (ko) | 2020-10-16 |
EP3745378A4 (en) | 2021-03-24 |
US11872999B2 (en) | 2024-01-16 |
CN110276985B (zh) | 2020-12-15 |
EP3745378B1 (en) | 2022-06-01 |
US20200406911A1 (en) | 2020-12-31 |
BR112020017519A2 (pt) | 2020-12-22 |
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