Collision Detection and Overtaking Using Artificial Potential Fields in Car Racing game TORCS using Multi-Agent based Architecture

Abstract: The Car Racing competition platform is used for evaluating different car control solutions under competitive conditions [1]. These competitions are organized as part of the IEEE Congress on Evolutionary Computation (CEC) and Computational Intelligence and Games Sym-posium (CIG). The goal is to learn and develop a controller for a car in the TORCS open source racing game [2]. Oussama Khatib [3] (1986) introduced Artificial potential fields (APFs) for the first time while looking for new ways to avoid obstacles for manipulators and mobile robots in real time. In car racing games a novel combination of artificial potential fields as the major control paradigm for car controls in a multi-agent system is being used to coordinate control interests in different scenarios [1]. Here we extend the work of Uusitalo and Stefan J. Johansson by introducing effective collision detection, overtaking maneuvers, run time evaluation and detailed analysis of the track using the concept of multi-agent artificial potential fields MAPFs. The results of our extended car controller in terms of lap time, number of damages and position in the race is improved.