面向车-路-图协同的分布式自动驾驶仿真平台架构及应用

doi:10.19562/j.chinasae.qcgc.2024.08.001

Abstract

Abstract:

In order to solve the problems of low efficiency and insufficient system scalability of single-machine test platforms in the vehicle-road-map collaborative simulation environment， a distributed autonomous driving simulation platform architecture for vehicle-road-map cooperative simulation is proposed in this paper， named VIMS （Vehicle-Infrastructure-Map System）. The VIMS platform uses CARLA as the virtual simulation engine. By introducing in real high-definition maps and connecting the hardware-in-the-loop devices such as driving simulators and signal machines to VIMS， the virtual-real traffic scene is formed. Considering the interaction of functions， the VIMS platform is divided into four modules， namely， the main world， the intelligent vehicle， the intelligent roadside， and the high-definition map， adopting ROS distributed architecture to realize the relative independence of the modules and interconnection between the modules. Considering the computational reliability and availability of the platform， distributed computing is used to realize independent computation among the four modules. Through the lane-keeping and vehicle-road-map collaborative positioning algorithm as examples for application validation， data acquisition， transmission and algorithm validation tests and evaluation are realized through the platform. The results show that the platform proposed in this paper can realize the real-time simulation of vehicle， road， and map collaboration to ensure that the modules operate organically and that the system architecture is highly scalable.

Key words: collaborative vehicle infrastructure system, high-definition map, real–virtual simulation, autonomous driving

Jianan Zhang,Zhaozheng Hu,Jie Meng,Huahua Hu,Jie Zuo. Distributed Simulation Platform Architecture and Application of Autonomous Driving for Vehicle-Road-Map Collaboration[J].Automotive Engineering, 2024, 46(8): 1335-1345.

Figures/Tables 20

References 19

1	张毅，姚丹亚，李力，等.智能车路协同系统关键技术与应用［J］.交通运输系统工程与信息，2021，21（5）：40-51.
	ZHANG Y， YAO D Y， LI L， et al. Technologies and applications for intelligent vehicle-infrastructure cooperation systems［J］. Journal of Transportation Systems Engineering and Information Technology， 2021，21（5）：40-51.
2	HAN Y， ZHANG H， LI H， et al. Collaborative perception in autonomous driving： methods， datasets， and challenges［J］. IEEE Intelligent Transportation Systems Magazine， 2023，15（6）：131-151.
3	YU H， LUO Y， SHU M， et al. DAIR-V2X： a large-scale dataset for vehicle-infrastructure cooperative 3D object detection［C］. IEEE/CVF Conference on Computer Vision and Pattern Recognition（CVPR）， 2022： 21361-21370.
4	XU R， XIA X， LI J， et al. V2V4Real： a real-world large-scale dataset for vehicle-to-vehicle cooperative perception［C］. IEEE/CVF Conference on Computer Vision and Pattern Recognition（CVPR）， 2023： 13712-13722.
5	WANG T H， MANIVASAGAM S， LIANG M， et al. V2VNet： vehicle-to-vehicle communication for joint perception and prediction［C］. 16th European Conference in Computer Vision（ECCV）， 2020： 605-621.
6	XU R， XIANG H， XIA X， et al. OPV2V： an open benchmark dataset and fusion pipeline for perception with vehicle-to-vehicle communication［C］. International Conference on Robotics and Automation （ICRA）， 2022： 2583-2589.
7	DOSOVITSKIY A， ROS G， CODEVILLA F， et al. CARLA： an open urban driving simulator［C］. Conference on Robot Learning. PMLR， 2017： 1-16.
8	LI Y， MA D， AN Z， et al. V2X-Sim： multi-agent collaborative perception dataset and benchmark for autonomous driving［J］. IEEE Robotics and Automation Letters， 2022， 7（4）： 10914-10921.
9	KRAJZEWICZ D， ERDMANN J， BEHRISCH M， et al. Recent development and applications of SUMO-Simulation of Urban MObility［J］. International Journal on Advances in Systems and Measurements， 2012， 5（3&4）.
10	赵祥模国家重点研发计划（2021YFB2501200）团队. 自动驾驶测试与评价技术研究进展［J］. 交通运输工程学报， 2023， 23 （6）： 10-77.
	ZHAO Xiang-mo's Team Supported by the National Key Research and Development Program of China （2021YFB2501200）. Research progress in testing and evaluation technologies for autonomous driving［J］. Journal of Traffic and Transportation Engineering， 2023， 23 （6）： 10-77.
11	胡耘浩，李克强，向云丰，等. 智能网联汽车通用跨平台实时仿真系统架构及应用［J］.汽车工程， 2023， 45 （3）： 372-381.
	HU Y H， LI K Q， XIANG Y F， et al. The universal architecture and application of cross-platform real-time simulation system for intelligent connected vehicles［J］. Automotive Engineering， 2023， 45 （3）： 372-381.
12	赵树廉，来飞，李克强，等. 基于数字孪生技术的智能汽车测试方法研究［J］.汽车工程， 2023， 45 （1）： 42-51.
	ZHAO S L， LAI F， LI K Q， et al. Research on intelligent vehicle test method based on digital twin technology［J］. Automotive Engineering， 2023， 45 （1）： 42-51.
13	刘经南，詹骄，郭迟，等.智能高精地图数据逻辑结构与关键技术［J］.测绘学报，2019，48（8）：939-953.
	LIU J N， ZHAN J， GUO C， et al. Data logic structure and key technologies on intelligent high-precision map［J］. Acta Geodaetica et Cartographica Sinica，2019，48（8）：939-953.
14	YANG B， LIANG M， URTASUN R. HDNET： exploiting HD maps for 3D object detection［C］. Conference on Robot Learning. PMLR， 2018： 146-155.
15	余荣杰，田野，孙剑. 高等级自动驾驶汽车虚拟测试：研究进展与前沿［J］.中国公路学报， 2020， 33 （11）： 125-138.
	YU R J， TIAN Y， SUN J. Highly automated vehicle virtual testing： a review of recent developments and research frontiers［J］. China Journal of Highway and Transport， 2020， 33 （11）： 125-138.
16	SHAN T， ENGLOT B. LeGO-LOAM： lightweight and ground-optimized lidar odometry and map on variable terrain［C］. 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems （IROS）， 2018： 4758-4765.
17	SHAN T， ENGLOT B， MEYERS D， et al. LIO-SAM： tightly-coupled lidar inertial odometry via smoothing and map［C］. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems（IROS）， 2020： 5135-5142.
18	On-Road Automated Driving （ORAD） Committee. Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles： J3016_202104 ［S/OL］. SAE International， 2018. https：//www.sae.org/standards/content/j3016_202104/.
19	胡钊政，孙勋培，张佳楠，等. 基于时空图模型的车-路-图协同定位方法［J］.吉林大学学报（工学版）， 2024（5）：1246-1257.
	HU Z Z， SUN X P， ZHANG J N， et al. Vehicle-infrastructure-map cooperative localization method based on spatial-temporal graph model［J］. Journal of Jilin University（Engineering and Technology Edition）， 2024（5）：1246-1257.

数据集	来源	应用场景	传感器组成			高精地图
数据集	来源	应用场景	相机	激光雷达	IMU/GPS	高精地图
DAIR-V2X	真实场景	V2I	√	√	√	×
V2V4Real	真实场景	V2V	未开源	√	√	未开源
V2V-SIM	LiDARsim	V2V	×	√	×	√
OPV2V	OpenCDA和CARLA模拟器	V2V	√	√	√	√（虚拟）
V2X-Sim	SUMO和CARLA模拟器	V2I和V2V	√	√	√	√（虚拟）

传感器		参数
传感器		帧率/Hz	线束	水平视场角/（°）	垂直视场角/（°）	扫描半径/m	测量误差/m
车载传感器	语义激光雷达	10	32	360	-30~10	120
车载传感器	GPS	10					10
路侧传感器	语义激光雷达	10	128	360	-30~10	200

[1]	Le Tao,Hai Wang,Yingfeng Cai,Long Chen. Multi-object Detection Algorithm Based on Point Cloud for Autonomous Driving Scenarios [J]. Automotive Engineering, 2024, 46(7): 1208-1218.
[2]	Linhui Li,Yifan Fu,Ting Wang,Xuecheng Wang,Jing Lian. Trajectory Prediction Method Enhanced by Self-supervised Pretraining [J]. Automotive Engineering, 2024, 46(7): 1219-1227.
[3]	Hai Wang,Yuxuan Ding,Tong Luo,Meng Qiu,Yingfeng Cai,Long Chen. A Multi-class Multi-target Tracking Algorithm Combining Motion Speed and Appearance Features in Driving Scenarios [J]. Automotive Engineering, 2024, 46(6): 956-964.
[4]	Jing Huang,Xiangzhen Liu,Xiaoyang Deng,Ran Chen. Research on Intelligent Vehicle Trajectory Planning Based on Multimodal Trajectory Prediction [J]. Automotive Engineering, 2024, 46(6): 965-974.
[5]	Fuxing Yao,Chao Sun,Yungang Lan,Bing Lu,Bo Wang,Haiyang Yu. A Lane Change Decision Method for Intelligent Connected Vehicles Based on Mixture of Expert Model [J]. Automotive Engineering, 2024, 46(5): 882-892.
[6]	Mengfan Li,Zhongxiang Feng,Weihua Zhang,Jingyu Li. Study on Driver's Visual Transfer Characteristics During the Takeover Process of Human-Computer Co-driving Mode [J]. Automotive Engineering, 2024, 46(5): 795-804.
[7]	Ting Chikit,Yafei Wang,Yichen Zhang,Mingyu Wu,Yile Wang. Energy-Saving Planning Method for Autonomous Driving Mining Trucks Based on Composite Dynamic Sampling [J]. Automotive Engineering, 2024, 46(4): 588-595.
[8]	Yiwei Zhou,Mo Xia,Bing Zhu. Multimodal Vehicle Trajectory Prediction Methods Considering Multiple Traffic Participants in Urban Road Scenarios [J]. Automotive Engineering, 2024, 46(3): 396-406.
[9]	Xiaocong Zhao,Shiyu Fang,Zirui Li,Jian Sun. Extraction and Application of Key Utility Term for Social Driving Interaction [J]. Automotive Engineering, 2024, 46(2): 230-240.
[10]	Yanli Ma, Qin Qin, Fangqi Dong, Yining Lou. Takeover Risk Assessment Model Based on Risk Field Theory Under Different Cognitive Secondary Tasks [J]. Automotive Engineering, 2024, 46(1): 9-17.
[11]	Weiguo Liu,Zhiyu Xiang,Weiping Liu,Daoxin Qi,Zixu Wang. Research on Vehicle Control Algorithm Based on Distributed Reinforcement Learning [J]. Automotive Engineering, 2023, 45(9): 1637-1645.
[12]	Ming Wang,Xiaolin Tang,Kai Yang,Guofa Li,Xiaosong Hu. A Motion Planning Method for Autonomous Vehicles Considering Prediction Risk [J]. Automotive Engineering, 2023, 45(8): 1362-1372.
[13]	Dongyu Zhao, Shuen Zhao. Autonomous Driving 3D Object Detection Based on Cascade YOLOv7 [J]. Automotive Engineering, 2023, 45(7): 1112-1122.
[14]	Jiahao Zhao,Zhiquan Qi,Zhifeng Qi,Hao Wang,Lei He. Calculation of Heading Angle of Parallel Large Vehicle Based on Tire Feature Points [J]. Automotive Engineering, 2023, 45(6): 1031-1039.
[15]	Lisheng Jin,Guangde Han,Xianyi Xie,Baicang Guo,Guofeng Liu,Wentao Zhu. Review of Autonomous Driving Decision-Making Research Based on Reinforcement Learning [J]. Automotive Engineering, 2023, 45(4): 527-540.

Distributed Simulation Platform Architecture and Application of Autonomous Driving for Vehicle-Road-Map Collaboration

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 20

References 19

Related Articles 15

Metrics

Comments

Recommended 0