Interaction-aware Trajectory Prediction for Opponent Vehicle in High Speed Autonomous Racing

In this paper, we present an innovative trajectory prediction algorithm that is specifically crafted for high-speed autonomous racing, with a focus on the mutual influence between vehicles. This algorithm was developed in the context of the Hyundai Autonomous Challenge 2023, a pioneering event that featured the world’s first competitive racing scenario involving three autonomous vehicles simultaneously navigating a road course race track. Stable overtaking in 1:N races requires accurate prediction of the trajectories of surrounding vehicles, taking into account their inter-vehicle dynamics. To meet this challenge, our approach leverages the Model Predictive Path Integral technique, which not only considers information from neighboring vehicles but also incorporates prior knowledge of the race track. Furthermore, we have augmented our algorithm with maneuver intention estimation-based trajectory prediction, an approach that leverages a vehicle’s historical trajectory data to forecast its future path. By integrating these two methodologies, our algorithm adeptly anticipates the motion of other vehicles under a variety of conditions on the race track. The efficacy of our proposed solution has been substantiated through extensive simulation and real-world testing, demonstrating its capability to deliver real-time performance in high-speed environments, with a processing time as low as 20 milliseconds.