Recently, numerous studies have investigated cooperative traffic systems using the communication among vehicle-to-everything (V2X). Unfortunately, when multiple autonomous vehicles are deployed while exposed to communication failure, there might be a conflict of ideal conditions between various autonomous vehicles leading to adversarial situation on the roads. In South Korea, virtual and real-world urban autonomous multi-vehicle races were held in March and November of 2021, respectively. During the competition, multiple vehicles were involved simultaneously, which required maneuvers such as overtaking low-speed vehicles, negotiating intersections, and obeying traffic laws. In this study, we introduce a fully autonomous driving software stack to deploy a competitive driving model, which enabled us to win the urban autonomous multi-vehicle races. We evaluate module-based systems such as navigation, perception, and planning in real and virtual environments. Additionally, an analysis of traffic is performed after collecting multiple vehicle position data over communication to gain additional insight into a multi-agent autonomous driving scenario. Finally, we propose a method for analyzing traffic in order to compare the spatial distribution of multiple autonomous vehicles. We study the similarity distribution between each team's driving log data to determine the impact of competitive autonomous driving on the traffic environment.
翻译:最近,许多研究都调查了使用车辆到任何物品之间通信(V2X)的合作交通系统。 不幸的是,当多辆自主车辆在出现通信故障时部署时,各种自主车辆之间可能存在理想条件的冲突,导致道路上的敌对状态。在南朝鲜,虚拟和现实世界城市自治多车辆竞赛分别于2021年3月和11月举行。在竞争期间,多辆车辆同时参与,这需要各种动作,例如超载低速车辆、谈判交叉点和遵守交通法。在本研究中,我们引入了完全自主的驾驶软件堆,以部署竞争性驾驶模式,使我们能够赢得城市自主多车辆竞赛。我们评估基于模块的系统,如导航、认知和在现实和虚拟环境中的规划。此外,在收集多部车辆在通信方面的定位数据后,对交通进行了分析,以进一步了解多部自主驾驶方案。最后,我们提出了一种分析交通流量的方法,以比较多部自主车辆的空间分布。我们研究了各队驾驶记录数据之间的相似性分布,以确定竞争性自主驾驶对交通环境的影响。