CN108549366A - Intelligent automobile road driving mapping experiment method parallel with virtual test - Google Patents
Intelligent automobile road driving mapping experiment method parallel with virtual test Download PDFInfo
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Abstract
The present invention relates to a kind of intelligent automobile road driving mapping experiment methods parallel with virtual test, it is intended to improve efficiency and the safety of intelligent automobile test.The track data of driver's driving vehicle, the perception data and planned trajectory data of automated driving system are acquired first（Notice that intelligent automobile is not carried out the planned trajectory at this time）；Then actual path and planned trajectory are compared, there are the scenes of significant difference for two tracks of search；Later by the perception information of intelligent automobile, these scenes are reconstructed, and carry out scene playback and simulation and prediction, calculate separately the safety of two tracks, and assess it.This method controls vehicle without using automated driving system, as long as intelligent vehicle is in road driving（I.e. human driver is in manipulation automobile）, so that it may it is tested and is tested.Testing efficiency both can be improved in this method, ensures that the safety of test.
Description
Technical Field
The invention belongs to the technical field of intelligent automobile testing, and particularly relates to a system for searching a scene with a difference between an intelligent automobile planned path and a driver driving path in the process that a driver drives an intelligent automobile to autonomously run on an actual road, constructing the same scene in a virtual environment, and performing scene playback and simulation testing to realize testing and evaluation on the running safety of the intelligent automobile.
Background
With the development of scientific technology, the intelligent automobile based on the automatic driving technology shows huge development potential in the aspects of the improvement of traffic safety, the prevention and the treatment of traffic jam and the like, and is the direction of the development of the automobile industry and traffic operation.
The automatic driving system of the intelligent automobile consists of three modules, namely environment perception module, planning decision module and vehicle control module. The environment perception module perceives the traffic parameters of traffic participants around the vehicle and identifies traffic environment information such as mark lines, signal control, weather conditions and the like through a sensor arranged on the intelligent vehicle. The planning decision module plans the driving path of the vehicle according to the information obtained by the environment sensing module, and simultaneously makes decisions on behaviors of acceleration, deceleration and the like of the vehicle. And the vehicle control module controls the driving direction and the speed of the vehicle according to the path planning and behavior decision of the planning decision module. The three modules are mutually connected, and progress is carried out layer by layer, and finally automatic driving is realized. The invention concerns the testing of intelligent vehicle planning decision modules.
When the intelligent automobile runs on a common road, the intelligent automobile needs to cope with various complex traffic environments and weather conditions, such as mixed traffic flow environments, heavy snow and haze weather. Therefore, the intelligent vehicle must undergo a comprehensive and rigorous test before getting on the road, otherwise there will be a safety risk. Currently, testing of driving levels of smart vehicles is mainly performed by open road testing, test site testing and virtual testing. The public road test has the most real test environment, but the test scene is uncontrollable, an extremely long test period is required, and potential safety hazards exist. The reports of landes indicate that because the scenario that smart cars are to face is infinite and traffic accidents are a very rare event, if the safety of smart cars is to be proven statistically significantly higher than human driving, about 100 cars are required, 24 hours a day, and 225 years of a year round without rest test at a speed of 25 miles per hour. In addition, the public road test has higher traffic safety risk, is subject to factors such as legal policies and the like, and is difficult to develop on a large scale. The test site test is often a function test under a simple traffic scene aiming at a single vehicle under a fixed scene, and is difficult to reproduce comprehensively and truly the complex environment which is possibly met by the intelligent vehicle on the actual road. The virtual test can improve the efficiency of the test, can guarantee the security of the test at the same time, but can not guarantee the authenticity of the test scene.
The method integrates the open road test and the virtual test together, and tests and evaluates the automatic driving system of the intelligent vehicle through the parallel mapping experiment. The method provided by the invention solves the problems of potential safety hazard and low testing efficiency in public road testing, and can be used for testing the intelligent vehicle through parallel experiments as long as the intelligent vehicle provided with the environment perception and planning decision system runs on the road.
Disclosure of Invention
The method solves the technical problem that the key scenes in the driving of the intelligent vehicle are identified by comparing the actual trajectory output of the intelligent vehicle on the public road with the virtual trajectory calculated according to the planning decision of the intelligent vehicle under the specific scenes, and the automatic driving system of the intelligent vehicle is evaluated through the key scenes. Firstly, acquiring track data of a vehicle driven by a driver, perception data of an automatic driving system and planning track data; then comparing the actual track with the planned track, and searching for a scene with significant difference between the two tracks; and finally, carrying out playback and simulation prediction on the scenes to finish the safety evaluation of the intelligent vehicle.
The automatic driving system of the intelligent automobile generally comprises three modules of environment perception, decision planning and control execution. The method has the basic idea that when the intelligent automobile is driven by people on an open road, the environment sensing and planning decision module still works, but the control execution module does not work, so that a short-term planning track of the planning decision can be obtained, and because the intelligent automobile is not executed by an actuator, the intelligent automobile does not need to run according to the track, so that the real world traffic is not influenced, and the test safety is ensured. Meanwhile, the three modules of environment perception, planning decision and control execution are mapped into the virtual world, the planning track is executed in the virtual world, the dynamic state of the virtual world is influenced, closed-loop operation is formed in the virtual world, and the intelligent automobile is tested. Therefore, the method is called as a road driving and virtual testing parallel mapping experimental method, and the core idea of the method is shown in figure 3.
The invention provides an intelligent automobile road driving and virtual test parallel mapping experimental method, which comprises the following specific steps:
(1) collecting a driving track, perception information and a planning driving track given by an automatic driving system of an intelligent automobile:
acquiring a driving track given by an automatic driving system when a driver drives on an open road, wherein the driving track given by the automatic driving system comprises information such as vehicle position coordinates with time stamps, speed, steering angle and the like; acquiring perception information of an intelligent automobile, wherein the perception information comprises surrounding traffic information, environmental information and the like; acquiring a planned driving track given by an intelligent automobile through a sensing module and a planning decision module, wherein the planned driving track comprises short-term planned information such as vehicle position coordinates, speed, steering angle and the like;
(2) comparing a driving track given by an automatic driving system of the intelligent automobile with a planned driving track, and searching for a scene with a significant difference between the two tracks:
comparing and analyzing the acquired planned driving track information of the intelligent automobile and the driving track given by the automatic driving system, and screening out a scene with an obvious difference between the two tracks; at a certain momenttTwo traces are considered to be significantly different when any of the following is present:
a) longitudinal distance of two-track vehicledGreater than a threshold valueI.e. by;
b) Lateral distance of two-track vehiclesGreater than a threshold valueI.e. by;
c) Speed difference of two-track vehicleGreater than a threshold valueI.e. by;
(3) Scene playback and simulation prediction:
carrying out scene reconstruction on the scene in which the planned driving track information of the intelligent automobile in the step (2) and the driving track given by the automatic driving system have obvious difference; namely, because the environment sensing module of the intelligent automobile has a certain sensing range and a sensing blind area possibly exists, the reconstructed scene is kept consistent with the real scene as far as possibletScenes with significant differences in time are perceived by the vehicle's perception module over a period of timeToReconstructing the scene around the vehicle by the sensed environmental information;
after the scene reconstruction is completed, the method is carried outIs timed totIn the time period of the moment, the actual running track and the planned track of the intelligent automobile are played back, whether the two tracks have collision events with the obstacle or not is detected, the running safety index of the intelligent automobile is calculated, and meanwhile, the intelligent automobile is subjected to collision with the obstacle through traffic simulation softwaretToIn time intervals, simulating the behaviors of the intelligent automobile and other traffic participants; generating a planned driving track through an automatic driving system of the intelligent automobile based on the traffic environment information generated by the traffic simulation software, and forming real-time closed-loop interaction between the track planned by the automatic driving system and the traffic flow in the traffic simulation software; then, calculatetToAnd in a time period, the safety indexes of the actual running track and the planned running track of the intelligent automobile are obtained. Wherein,，；
(4) safety assessment of an automatic driving system of the intelligent automobile:
based on obvious difference between actual running track and planned track of intelligent automobileThe safety evaluation of the intelligent automobile automatic driving system is divided into two parts: one is comparative analysis of the playback track, i.e.TotSafety analysis within a time period; secondly, after the difference appears, the track of the vehicle is continuously driven for a period of time to compare and analyze, namelytToSafety analysis over a period of time. The safety analysis not only analyzes the safety of the own vehicle, but also analyzes the influence on the following fleet.
In the invention, the safety index of the intelligent automobile in driving is TCC.
In the invention, the traffic simulation software is any one of VISSIM or TESS NG.
Compared with the prior art, the technical scheme of the invention has the following beneficial effects:
1. the intelligent automobile road driving and virtual test parallel mapping experiment method provided by the invention integrates two test modes of open road test and virtual test, thereby improving the test efficiency and ensuring the test safety. In the experimental method, when the intelligent automobile runs on an actual road, the intelligent automobile is controlled by a driver instead of an automatic driving system, so that the safety of the test is guaranteed, and the problem of violation of laws and regulations does not exist. Meanwhile, as long as the intelligent automobile provided with the automatic driving system runs on an actual road, data can be continuously provided for the experiment, and compared with public road tests limited by time, place and laws and regulations, the test efficiency is greatly improved.
2. According to the intelligent automobile road driving and virtual test parallel mapping experimental method provided by the invention, for a key scene with a significant difference between an actual track and a planned track, the scene is replayed through intelligent automobile sensing data, the safety of the actual path and the intelligent automobile planned path are compared, the evolution of the intelligent automobile and surrounding traffic flow within a period of time after the difference track appears is deduced through simulation software, the safety evolution situation of the actual track and the planned track in a certain period of time in the future is analyzed, and a safety evaluation method based on scene replay and future state deduction is established, so that the safety evaluation of an intelligent automobile automatic driving system is more comprehensive and reliable.
Drawings
FIG. 1 is a flow chart of an intelligent vehicle road driving and virtual test parallel mapping experiment method provided by the invention;
FIG. 2 is a schematic diagram of each judgment index in step 2 of the present invention;
FIG. 3 is a diagram of the basic idea of a parallel mapping experiment;
fig. 4 is an interface for simulation prediction of a scene using VISSIM in embodiment 1;
fig. 5 is a typical scenario example in embodiment 1.
Detailed Description
The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and examples. The present embodiment is implemented on the premise of the technical solution of the present invention, but the scope of the present invention is not limited to the following embodiments.
Example 1
The implementation place of the embodiment is Shanghai city, data obtained by driving the intelligent automobile with automatic driving on the actual road of the Shanghai city by a plurality of drivers are compared with the actual track and the planned track, the scene with obvious difference in track is searched, and the safety of the intelligent automobile is evaluated by performing a parallel mapping experiment, and the method comprises the following detailed steps:
(1) collecting the running track, perception information and planning the running track of the vehicle:
the method comprises the steps of collecting a driving track of a driver driving the intelligent automobile on an actual road in Shanghai city, wherein the driving track comprises information such as vehicle position coordinates with time stamps, speed and steering angle. The method comprises the steps of collecting perception information of the intelligent automobile, wherein the perception information comprises surrounding obstacle position information, lane information and the like. The method comprises the steps of collecting planned driving tracks of the intelligent automobile, including planned information such as position coordinates, speed and steering angles of the intelligent automobile, which are given by a sensing module and a planning decision module.
(2) Comparing the vehicle running track with the planned track, and searching two scenes with obvious difference in track:
and comparing and analyzing the acquired vehicle running track information and a planned running track given by an automatic driving system, and screening out a scene with obvious difference between the two tracks. At a certain momenttThe two traces are considered to be significantly different when either:
a) longitudinal distance of two-track vehicledGreater than a threshold valueI.e. by;
b) Lateral distance of two-track vehiclesGreater than a threshold valueI.e. by;
c) Speed difference of two-track vehicleGreater than a threshold valueI.e. by;
(3) Scene playback and simulation prediction:
and reconstructing the scenes of all the searched scenes with obvious difference between the actual trajectories and the planned trajectories. To be attScenes with significant differences in time are perceived by the vehicle's perception module over a period of timeToAnd reconstructing the scene around the vehicle by the sensed environmental information to obtain the position and speed information of each vehicle and the position information of the fixed obstacle in the scene at each moment.
And after the scene reconstruction is completed, carrying out scene playback. To pairIs timed totAnd playing back the actual running track and the planned track of the vehicle in the time period of the moment. In the playback, whether the two tracks have collision events with other vehicles or obstacles is firstly checked; then, the TTC index of each moment in the running process of the intelligent vehicle with the two tracks is calculated to obtain the minimum TTC index in the playback stage. In the event of a collision event,。
the evolution of the scene is then predicted by traffic simulation software. Using traffic simulation software VISSIM, the time when the two tracks have significant differencetTime of dayThe state of the scene as the initial state, pairtToAnd (4) performing simulation prediction on the behaviors of the intelligent automobile and other traffic participants in a time period. ComputingtToIn a time interval, obtaining the minimum TTC index of the prediction stage by the TTC index of each moment of the actual running track and the planned running track of the vehicle. In the event of a collision event,. In addition, the influence of the differential behavior on the whole traffic flow is calculated, so that the TTC of each vehicle in the whole fleet at each moment is calculated, and the minimum value is taken as the minimum TTC index of the fleet。
(4) Safety assessment of an automatic driving system of the intelligent automobile:
the safety performance of the automatic driving system of the intelligent automobile is evaluated from two aspects of playback and prediction through safety indexes obtained by scene playback and simulation prediction:
a) if the minimum TTC of the actual track is greater than the minimum TTC of the planned track during the playback phase, that isThe safety of the automatic driving system in the scene is lower than that of human driving; on the contrary, ifThe safety of the automatic driving system in the scene is higher than that of human driving;
b) if it returnsThe safety difference between the two tracks in the playing scene is not large, and the minimum TTC of the actual track in the simulation prediction stage is larger than the minimum TTC of the planned track, namelyThe safety of the automatic driving system in the scene is lower than that of human driving; on the contrary, ifThe safety of the automatic driving system in the scene is higher than that of human driving;
c) if the safety difference between the two tracks of a single vehicle is not large, and the minimum TTC of the fleet of the actual tracks in the simulation prediction stage is larger than the minimum TTC of the fleet of the planned tracks, namelyThe safety of the automatic driving system in the scene is lower than that of human driving; on the contrary, ifThe safety of the automatic driving system in the scene is higher than that of human driving.
(5) A typical scenario example:
as shown in the scenario of FIG. 5, the actual trajectory of the test vehicle is the same as the planned trajectorytSignificant differences in time of day occur, so the scene is first reconstructed and then played back and predictive evaluated.
The lane change operation is performed because the driver of the test vehicle predicts the upcoming lane change behavior according to the behavior of the right front vehicle, and the automatic driving system does not predict the insertion behavior, so that the planning is made to continue running on the own lane. TTC index of actual track and planned track can be calculated by reviewing, and the stageThe safety of automatic driving at this stage is lower than that of human driving.
According to the predicted track, after the two tracks are obviously different, the actual track keeps safe running, in the predicted track, a test vehicle firstly carries out emergency braking operation due to the insertion of a front vehicle and carries out rear-end collision with the front vehicle at a different point, and the TTC is smaller than a safety threshold value; and then, the test vehicle is subjected to rear-end collision due to emergency braking operation, so that an accident occurs. This stageAnd isThe safety of automatic driving is lower than that of human driving.
Claims (3)
1. An intelligent automobile road driving and virtual test parallel mapping experimental method is characterized by comprising the following specific steps:
(1) collecting a driving track, perception information and a planning driving track given by an automatic driving system of an intelligent automobile:
acquiring a driving track given by an automatic driving system when a driver drives on an open road, wherein the driving track given by the automatic driving system comprises information such as vehicle position coordinates with time stamps, speed, steering angle and the like; acquiring perception information of an intelligent automobile, wherein the perception information comprises surrounding traffic information, environmental information and the like; acquiring a planned driving track given by an intelligent automobile through a sensing module and a planning decision module, wherein the planned driving track comprises short-term planned information such as vehicle position coordinates, speed, steering angle and the like;
(2) comparing a driving track given by an automatic driving system of the intelligent automobile with a planned driving track, and searching for a scene with a significant difference between the two tracks:
comparing and analyzing the acquired planned driving track information of the intelligent automobile and the driving track given by the automatic driving system, and screening out a scene with an obvious difference between the two tracks; at a certain momenttTwo traces are considered to be significantly different when any of the following is present:
longitudinal distance of two-track vehicledGreater than a threshold valueI.e. by;
Lateral distance of two-track vehiclesGreater than a threshold valueI.e. by;
Speed difference of two-track vehicleGreater than a threshold valueI.e. by;
(3) Scene playback and simulation prediction:
carrying out scene reconstruction on the scene in which the planned driving track information of the intelligent automobile in the step (2) and the driving track given by the automatic driving system have obvious difference; namely, because the environment sensing module of the intelligent automobile has a certain sensing range and a sensing blind area possibly exists, the reconstructed scene is kept consistent with the real scene as far as possibletScenes with significant differences in time are perceived by the vehicle's perception module over a period of timeToReconstructing the scene around the vehicle by the sensed environmental information;
after the scene reconstruction is completed, the method is carried outIs timed totIn the time period of the moment, the actual running track and the planned track of the intelligent automobile are played back, whether the two tracks have collision events with the obstacle or not is detected, the running safety index of the intelligent automobile is calculated, and meanwhile, the intelligent automobile is subjected to collision with the obstacle through traffic simulation softwaretToIn time intervals, simulating the behaviors of the intelligent automobile and other traffic participants; generating a planned driving track through an automatic driving system of the intelligent automobile based on the traffic environment information generated by the traffic simulation software, and forming real-time closed-loop interaction between the track planned by the automatic driving system and the traffic flow in the traffic simulation software; then, calculatetToIn a time period, safety indexes of an actual driving track and a planned driving track of the intelligent automobile are obtained;
wherein,，；
(4) safety assessment of an automatic driving system of the intelligent automobile:
based on the scene that the actual running track and the planned track of the intelligent automobile have obvious difference, the safety evaluation of the automatic driving system of the intelligent automobile is divided into two parts: one is comparative analysis of the playback track, i.e.TotSafety analysis within a time period; secondly, after the difference appears, the track of the vehicle is continuously driven for a period of time to compare and analyze, namelytToSafety analysis within a time period; the safety analysis not only analyzes the safety of the own vehicle, but also analyzes the influence on the following fleet.
2. The method as claimed in claim 1, wherein the safety index of the intelligent vehicle is TCC.
3. The intelligent automobile road driving and virtual test parallel mapping experiment method as claimed in claim 1, wherein the traffic simulation software is any one of VISSIM or TESS NG.
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