CN114802260A - Simulation scene construction method based on roadside device natural driving data - Google Patents
Simulation scene construction method based on roadside device natural driving data Download PDFInfo
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- 238000004088 simulation Methods 0.000 title claims abstract description 35
- 238000010276 construction Methods 0.000 title claims abstract description 10
- 238000000034 method Methods 0.000 claims abstract description 14
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- 238000012360 testing method Methods 0.000 claims abstract description 10
- 238000007405 data analysis Methods 0.000 claims abstract description 4
- 238000013075 data extraction Methods 0.000 claims abstract description 4
- 230000000903 blocking effect Effects 0.000 claims description 10
- 238000004458 analytical method Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
- B60W40/04—Traffic conditions
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
- B60W40/06—Road conditions
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2420/00—Indexing codes relating to the type of sensors based on the principle of their operation
- B60W2420/40—Photo, light or radio wave sensitive means, e.g. infrared sensors
- B60W2420/403—Image sensing, e.g. optical camera
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2420/00—Indexing codes relating to the type of sensors based on the principle of their operation
- B60W2420/40—Photo, light or radio wave sensitive means, e.g. infrared sensors
- B60W2420/408—Radar; Laser, e.g. lidar
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2556/00—Input parameters relating to data
- B60W2556/40—High definition maps
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- Engineering & Computer Science (AREA)
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- Automation & Control Theory (AREA)
- Mathematical Physics (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention relates to a simulation scene construction method based on roadside device natural driving data, which comprises the following steps: step 1: acquiring traffic data acquired by road side equipment within a time period T; step 2: data extraction is carried out, and road information, vehicle state information, traffic light information and pedestrian information in a time period T are obtained; and step 3: carrying out data analysis and statistics, counting the traffic flow, the average speed of the vehicles, the blockage degree and the acceleration and deceleration frequency of the vehicles in the time period T, and analyzing the lane-changing space-time distribution, the blockage space-time distribution and the pedestrian space-time distribution of the vehicles; and 4, step 4: executing the steps 1-3, and constructing a traffic flow model of the road section on the space-time; and 5: when simulation test is carried out, the traffic flow model is called to obtain the distribution of different areas of the road according to different time, and then a simulation scene which accords with the traffic flow characteristics of the road is generated. Compared with the prior art, the method has the advantages of improving the authenticity and the effectiveness of simulation and the like.
Description
Technical Field
The invention relates to the technical field of simulation scene construction, in particular to a simulation scene construction method based on roadside device natural driving data.
Background
At the present stage, the automatic driving technology is rapidly developed, a large number of road tests are required to be developed to verify that various functions and performances of the system meet the requirements of design and regulations before the automatic driving automobile is commercially applied, and how to develop a test service with high efficiency and low cost is a challenge in the automatic driving neighborhood at present. At present, a generally accepted solution path is to develop a test service in a simulation mode, and this mode can realize high-efficiency and low-cost development of related test work, and is helpful for solving the problems of cost, risk and the like in road tests. However, it is important to develop a test service in a simulation manner to ensure the authenticity and validity of a simulation scene, and therefore, a method for constructing a simulation scene based on real traffic data needs to be provided to improve the authenticity and validity of the simulation scene.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a simulation scene construction method based on roadside device natural driving data.
The purpose of the invention can be realized by the following technical scheme:
a simulation scene construction method based on roadside device natural driving data comprises the following steps:
step 1: acquiring traffic data acquired by road side equipment in a time period T, and recording a data source;
step 2: data extraction is carried out, and road information, vehicle state information, traffic light information and pedestrian information in a time period T are obtained;
and step 3: carrying out data analysis and statistics, counting the traffic flow, the average speed of the vehicles, the blockage degree and the acceleration/deceleration frequency of the vehicles in the time period T, and analyzing the lane-changing space-time distribution, the blockage space-time distribution and the pedestrian space-time distribution of the vehicles;
and 4, step 4: executing the steps 1-3, obtaining relevant parameters and distribution at different time periods in one day or obtaining and integrating relevant parameters and distribution at different dates, further obtaining different parameters and distribution in one year based on days, weeks, months or even quarters, and constructing traffic flow models of a certain road at different time scales in space and time;
and 5: and when the simulation test is carried out, calling a traffic flow model, obtaining the distribution in different areas of the road according to different time, and further generating a simulation scene according with the traffic flow characteristics of the road.
In the step 1, the road side equipment comprises a camera, a laser radar and a millimeter wave radar.
In the step 2, the road information includes lane line type, lane width, road temporary event, road type and lane type, and the road type includes straight road, curve, crossroad, T-shaped intersection and roundabout.
In the step 2, the vehicle state information includes vehicle speed, acceleration, vehicle head orientation, surrounding vehicle information, and lateral distance and longitudinal distance from the surrounding vehicle at time t, where the surrounding vehicle specifically is:
a vehicle having a radius within 20m with the target vehicle as a center.
In step 3, the traffic flow is specifically the number of vehicles entering the road segment minus the number of vehicles leaving the road segment in the time period T.
In step 3, the average speed of the vehicle is specifically the average speed of each vehicle in the time period T.
In the step 3, the congestion degree is specifically the ratio of the vehicle lengths of all vehicles on each lane to the lane length within the time interval Δ t, and the larger the ratio is, the more serious the congestion condition is.
In the step 3, the vehicle acceleration/deceleration frequency is specifically the acceleration/deceleration frequency occurring when each vehicle passes through the road section.
In the step 3, the lane change time-space distribution comprises lane change time distribution and lane change space distribution, specifically, the times of lane change in different areas of the road at each time T in the time period T;
the lane change time distribution is specifically the times of lane change on the road at each moment T in the time period T, and the lane change space distribution is specifically the times of lane change in different areas of the road in the time period T;
the blocking space-time distribution comprises blocking time distribution and blocking space distribution, and specifically is the blocking degree of different areas of the road at each moment T in a time period T;
the jam time distribution is specifically the jam degree of the road at each moment T in the time period T, and the jam space distribution is specifically the jam degree of different areas of the road in the time period T.
In the step 5, the distribution of different areas of the road section including the vehicle speed distribution, the behavior distribution and the density distribution is obtained according to different time.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the method, the traffic distribution models which accord with the real traffic data and are under different scales are obtained through analysis according to the real traffic data collected by the road side equipment, the simulation scene constructed by the traffic distribution models is called, the distribution situation of the actual traffic is better accorded, and the reality and the effectiveness of simulation are improved;
2. the invention can improve the probability of scenes with lower probability of occurrence in the real world in the simulation environment by matching the traffic distribution models with different scales, thereby improving the simulation efficiency.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
The invention provides a simulation scene construction method based on roadside device natural driving data, which comprises the following steps:
step 1: acquiring traffic data acquired by road side equipment in a time period T, and recording a data source;
the road side equipment comprises road side sensing equipment such as a camera, a laser radar, a millimeter wave radar and the like;
and 2, step: data extraction is carried out, and road information, vehicle state information, traffic light information and pedestrian information in a time period T are obtained;
the road information includes lane line type (solid line, broken line, etc.), lane width, lane type (left-turn lane, right-turn lane, and straight lane), road temporary event (such as temporary construction), and road type (straight lane, curve, crossroad, t-junction, roundabout, etc.);
the vehicle state information includes vehicle speed, acceleration, surrounding vehicle information at time t, and lateral and longitudinal distances from surrounding vehicles, including vehicles within a radius of 20m centered on the target vehicle;
and step 3: carrying out data analysis and statistics, counting the traffic flow, the average speed of the vehicles, the blockage degree and the acceleration and deceleration frequency of the vehicles in the time period T, and analyzing the lane-changing space-time distribution, the blockage space-time distribution and the pedestrian space-time distribution of the vehicles;
the traffic flow is specifically the number of vehicles entering the section of road minus the number of vehicles leaving the section of road in the time period T;
the vehicle average speed is specifically the average speed of each vehicle in the time period T;
the degree of congestion is described by the vehicle length of all vehicles on each lane and the ratio to the lane length over the time interval Δ t, and the greater the ratio, the more severe the congestion condition;
the vehicle acceleration/deceleration frequency is specifically the acceleration/deceleration frequency when each vehicle passes through the road section;
the lane change time-space distribution comprises lane change time distribution and lane change space distribution, and specifically is the times of lane change in different areas of the road at each time T in the time period T;
the lane change time distribution is specifically the times of lane change on the road section at each time T in the time period T;
the lane change spatial distribution is specifically the times of lane change in different areas of the road in the time period T;
the blockage space-time distribution comprises blockage time distribution and blockage space distribution, and specifically comprises blockage degrees of different areas of the road at each moment T in a time period T;
the jam time distribution is specifically the jam degree of the section of road at each moment T in the time period T;
the distribution of the blocking space is specifically the blocking degree of different areas of the road in a time period T;
and 4, step 4: constructing a traffic flow model, executing the steps 1-3, acquiring related parameters and distribution at different time periods in one day or acquiring and integrating related parameters and distribution at different dates, further acquiring different parameters and distribution in one year based on days, weeks, months or even quarters, and constructing the traffic flow model of a certain road at different time scales in space and time;
and 5: and when the simulation test is carried out, calling a traffic flow model, obtaining the distribution of vehicle speed, behavior, density and the like in different areas of the road according to different time, and generating a simulation scene which accords with the traffic flow characteristics of the road.
Small scale refers to traffic distribution models obtained over a relatively small time scale, such as hours or days, and large scale refers to traffic distribution models obtained over a larger time scale, such as months, quarters, or even years.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (10)
1. A simulation scene construction method based on roadside device natural driving data is characterized by comprising the following steps:
step 1: acquiring traffic data acquired by road side equipment in a time period T, and recording a data source;
step 2: data extraction is carried out, and road information, vehicle state information, traffic light information and pedestrian information in a time period T are obtained;
and step 3: carrying out data analysis and statistics, counting the traffic flow, the average speed of the vehicles, the blockage degree and the acceleration/deceleration frequency of the vehicles in the time period T, and analyzing the lane-changing space-time distribution, the blockage space-time distribution and the pedestrian space-time distribution of the vehicles;
and 4, step 4: executing the steps 1-3, obtaining relevant parameters and distribution at different time periods in one day or obtaining and integrating relevant parameters and distribution at different dates, further obtaining different parameters and distribution in one year based on days, weeks, months or even quarters, and constructing traffic flow models of a certain road at different time scales in space and time;
and 5: when the simulation test is carried out, the traffic flow model is called, the distribution in different areas of the road section is obtained according to different time, and then the simulation scene which is in accordance with the traffic flow characteristics of the road section is generated.
2. The method for constructing the simulation scene based on the roadside device natural driving data according to claim 1, wherein in the step 1, the roadside device comprises a camera, a laser radar and a millimeter wave radar.
3. The method for constructing the simulation scene based on the natural driving data of the roadside apparatus according to claim 1, wherein in the step 2, the road information comprises a lane line type, a lane width, a road temporary event, a road type and a lane type, and the road type comprises a straight road, a curve, an intersection, a T-shaped intersection and a roundabout.
4. The method for constructing the simulation scene based on the roadside device natural driving data according to claim 1, wherein in the step 2, the vehicle state information comprises vehicle speed, acceleration, vehicle head orientation, surrounding vehicle information, and transverse distance and longitudinal distance from surrounding vehicles at the time t, and the surrounding vehicles are specifically:
a vehicle having a radius within 20m with the target vehicle as a center.
5. The method for constructing the simulation scene based on the natural driving data of the roadside apparatus according to claim 1, wherein in the step 3, the traffic flow is specifically the number of vehicles entering the road section minus the number of vehicles leaving the road section within a time period T.
6. The method for constructing the simulated scene based on the roadside device natural driving data according to claim 1, wherein in the step 3, the average speed of the vehicle is specifically the average speed of each vehicle in a time period T.
7. The method for constructing the simulation scene based on the roadside device natural driving data according to claim 1, wherein in the step 3, the degree of congestion is specifically the vehicle length of all vehicles on each lane within a time interval Δ t and the ratio of the vehicle length to the lane length, and the larger the ratio is, the more serious the congestion condition is.
8. The method for constructing the simulation scene based on the natural driving data of the roadside device according to claim 1, wherein in the step 3, the vehicle acceleration/deceleration frequency is specifically the acceleration/deceleration frequency occurring when each vehicle passes through the road.
9. The method for constructing the simulation scene based on the natural driving data of the roadside apparatus according to claim 1, wherein in the step 3, the lane-change spatial-temporal distribution comprises a lane-change time distribution and a lane-change spatial distribution, specifically, the number of times of lane change in different areas of the road at each time T within a time period T;
the lane change time distribution is specifically the times of lane change on the road at each moment T in the time period T, and the lane change space distribution is specifically the times of lane change in different areas of the road in the time period T;
the blocking space-time distribution comprises blocking time distribution and blocking space distribution, and specifically is the blocking degree of different areas of the road at each moment T in a time period T;
the jam time distribution is specifically the jam degree of the road at each moment T in the time period T, and the jam space distribution is specifically the jam degree of different areas of the road in the time period T.
10. The method for constructing the simulation scene based on the natural driving data of the roadside apparatus as claimed in claim 1, wherein in the step 5, the distribution in different areas of the road section obtained according to different time includes vehicle speed distribution, behavior distribution and density distribution.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH11144182A (en) * | 1997-11-07 | 1999-05-28 | Toyota Central Res & Dev Lab Inc | Traffic flow simulation system |
| CN108122423A (en) * | 2016-11-28 | 2018-06-05 | 中国移动通信有限公司研究院 | A kind of method for guiding vehicles, apparatus and system |
| CN109657355A (en) * | 2018-12-20 | 2019-04-19 | 安徽江淮汽车集团股份有限公司 | A kind of emulation mode and system of road vehicle virtual scene |
| CN111797475A (en) * | 2020-06-30 | 2020-10-20 | 北京经纬恒润科技有限公司 | V2X test method and system |
| CN113849914A (en) * | 2021-09-28 | 2021-12-28 | 国汽(北京)智能网联汽车研究院有限公司 | Intelligent driving function test scene construction method and system and storage medium |
-
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- 2022-04-12 CN CN202210382516.1A patent/CN114802260A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH11144182A (en) * | 1997-11-07 | 1999-05-28 | Toyota Central Res & Dev Lab Inc | Traffic flow simulation system |
| CN108122423A (en) * | 2016-11-28 | 2018-06-05 | 中国移动通信有限公司研究院 | A kind of method for guiding vehicles, apparatus and system |
| CN109657355A (en) * | 2018-12-20 | 2019-04-19 | 安徽江淮汽车集团股份有限公司 | A kind of emulation mode and system of road vehicle virtual scene |
| CN111797475A (en) * | 2020-06-30 | 2020-10-20 | 北京经纬恒润科技有限公司 | V2X test method and system |
| CN113849914A (en) * | 2021-09-28 | 2021-12-28 | 国汽(北京)智能网联汽车研究院有限公司 | Intelligent driving function test scene construction method and system and storage medium |
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