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.

We have concluded by implementing a controller that make use of multi agent based artificial potential field approach to achieve the tricky and complex task of collision detection and overtaking while driving in a car racing game with different other controllers as opponents. We exploited the advantages of APFs to the best of our knowledge using laws of physics and discrete mathematics in order to achieve successful overtaking behavior and overcome its drawbacks as being very complex to implement and high memory requirements for time critical applications e.g. car racing games (Section 3.1, RQ1). Dynamic objects in a fast changing environment like a car racing game are likely to collide more often with each other, thus resulting in higher number of damages. Using APFs instead of traditionally used collision avoidance techniques resulted in less number of damages during the race, thus minimizing the lap’s time which in turn contribute to better position in the race as shown in experiment 3 (Section 3.1, RQ2 and Section 6.6). Overtaking maneuvers are complex and tricky and is the major cause of collision among cars both in real life as well as in car racing games, thus the criteria to measure the performance regarding overtaking behavior of different controllers in the race is based on number of damages taken during the race. The comparison between the participating controllers in terms of damages taken during various rounds of the race is analyzed in experiment 3 (Section 3.1, RQ3 and Section 6.6). The results of the quick race along with opponents shows good results on three tracks while having bad performance on the remaining other track. Our controller got 1st position on the CG track 2 while kept 2nd position on CS Speed way 1 and Alpine 1. It had worse performance on wheel 2 which needs to be optimized in the future for better results on this track and other similar complex and tricky tracks.