Design, Field Evaluation, and Traffic Analysis of a Competitive Autonomous Driving Model in the a Congested Environment

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.