Recently, numerous studies have investigated cooperative traffic systems using the communication between vehicle-to-everything (V2X). Unfortunately, when multiple autonomous vehicles are deployed while exposing communication failure, there might be a conflict of optimal conditions between various autonomous vehicles leading to adversarial situation on the roads. In South Korea, virtual and live urban autonomous multi-vehicle races were held in March and November of 2021, respectively. In this study, we introduce a full 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 studied 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,许多研究调查了使用车辆对一切之间通信联系(V2X)的合作交通系统。不幸的是,当多辆自主车辆在暴露通信失败时,各种自主车辆之间可能存在最佳条件的冲突,导致道路上的敌对状况。在南朝鲜,虚拟和活市自治多车辆赛事分别于2021年3月和11月举行。在本研究中,我们采用了完全自主的驾驶软件堆,以部署一个竞争性的驾驶模式,使我们能够赢得城市自主多车辆赛事。我们评价基于模块的系统,例如实际和虚拟环境中的导航、感知和规划。此外,在收集多车辆对通信的定位数据后,对交通进行了分析,以进一步了解多剂自主驾驶的情景。最后,我们提出了一种分析交通的方法,以比较多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。