面向自动驾驶的车路协同感知点云融合模式时延影响分析  

An Analysis of the Impact of Time Delay of Fusion Modes for Point Clouds from Cooperative Road Vehicle Systems on Autonomous Driving

作　　者：叶青[1,2] 赵聪 朱逸凡 俞山川 YE Qing;ZHAO Cong;ZHU Yifan;YU Shanchuan(School of Big Data&Software Engineering,Chongqing University,Chongqing 401331,China;Research and Development Center of Transport Industry of Self-driving Technology,China Merchants Chongqing Communications Technology Research&Design Institute Co.,Ltd.,Chongqing 400060,China;Key Laboratory of Road and Traffic Engineering of the Ministry of Education,Tongji University,Shanghai 201804,China)

机构地区：[1]重庆大学大数据与软件学院,重庆401331 [2]招商局重庆交通科研设计院有限公司自动驾驶技术交通运输行业研发中心,重庆400060 [3]同济大学道路与交通工程教育部重点实验室,上海201804

出　　处：《交通信息与安全》2023年第4期72-79,共8页Journal of Transport Information and Safety

基　　金：国家重点研发计划项目(2021YFB1600403);重庆市技术创新与应用发展专项重点项目(cstc2021jscx-gksbX0057);自动驾驶技术交通运输行业研发中心(招商局重庆交通科研设计院有限公司)开放基金项目资助。

摘　　要：新一代通信技术的快速发展为车路协同感知提供了基础,可进一步提升自动驾驶车辆在复杂场景中的感知能力,现有研究对不同的协同感知信息融合模式进行了探索,但忽略了对感知精度与通信时延平衡性的分析。针对自动驾驶协同感知中点云融合模式的时延特征,本文以前融合、特征融合、后融合3种模式为研究对象,提出了基于模拟仿真的时延影响分析框架。考虑通信时延引起的协同感知结果时滞性,利用扩展卡尔曼滤波算法对存在时延的协同感知结果进行预测性补偿,创新提出了滞后补偿误差和等效时延评价指标,用以综合评价不同融合模式对协同感知结果的影响;针对不同点云融合模式的感知结果,构建了平均感知精度与平移误差分布关系模型,依据目标检测平移误差的分布特征生成带有感知误差的仿真轨迹,进而对协同感知效果进行评估。结合TrajNet++行人轨迹数据集,以不同时延参数及点云融合模式,对1200条轨迹进行了180000次数值仿真。结果显示,感知目标的已知轨迹长度越短、速度越高,时延对协同感知精度的影响越大,以100 ms时延下后融合为基准,当特征融合时延在500 ms以内、前融合时延在700 ms以内时,可以达到相同或更高的协同感知精度。针对目标易突然出现且速度快的复杂场景,宜采用低时延、低精度的后融合模式,反之,宜采用具有高时延、高精度的特征融合或前融合模式。本研究可为自动驾驶协同感知的点云融合模式选择提供依据。The rapid development of the new generation of communication technologies provides a foundation for cooperative perception between autonomous vehicles(AVs)and road.This advancement holds the potential to significantly enhance the perception capabilities of AVs in complex scenarios.Previous studies have explored different information fusion modes for cooperative perception,but neglected to analyze the balance between perception accuracy and communication delay.Aiming at the delay characteristics of point cloud fusion in cooperative perception of AVs,a delay impact analysis framework is proposed based on simulation,concentrating on three fusion modes:pre-fusion,feature fusion,and post-fusion.Considering the time lag of cooperative perception results caused by communication delay,the Extended Kalman Filter algorithm is used to make predictive compensation for cooperative perception results with delay.The novel metrics,namely Lag Compensation Error and equivalent time delay,are proposed for comprehensive evaluation of the impact of different fusion modes on cooperative perception results.Based on perception results from various point cloud fusion modes,a model is established to fit the relationship between average perception accuracy and translation error distribution.Utilizing the distribution characteristics of translation errors,this model serves as the basis for generating simulated trajectories with perception errors and subsequently the evaluating of cooperative perception performance.Finally,leveraging the TrajNet++pedestrian trajectory dataset,180000 numerical simulations are conducted across 1200 trajectories with various point cloud fusion modes and different delay parameters.The results demonstrate that the shorter trajectory lengths and the higher target speeds amplify the impact of delay on cooperative perception accuracy.In comparison to post-fusion with a 100 ms delay as the benchmark,the equivalent or superior cooperative perception accuracy is feasible when the feature-fusion delay is below 500 ms an

关 键 词：智能交通 时延影响 轨迹预测 自动驾驶 协同感知 点云融合 

分 类 号：U491.5[交通运输工程—交通运输规划与管理]