Predictive Spliner: Data-Driven Overtaking in Autonomous Racing Using Opponent Trajectory Prediction

Abstract

Head-to-head racing against opponents is a challenging and emerging topic in the domain of autonomous racing. We propose Predictive Spliner, a data-driven overtaking planner designed to enhance competitive performance by anticipating opponent behavior. Using Gaussian Process (GP) regression, the method learns and predicts the opponent's trajectory, enabling the ego vehicle to calculate safe and effective overtaking maneuvers. Experimentally validated on a 1:10 scale autonomous racing platform, Predictive Spliner outperforms commonly employed overtaking algorithms by overtaking opponents at up to 83.1% of its own speed, being on average 8.4% faster than the previous best-performing method. Additionally, it achieves an average success rate of 84.5%, which is 47.6% higher than the previous best-performing method. The proposed algorithm maintains computational efficiency with a Central Processing Unit (CPU) load of 22.79% and a computation time of 8.4 ms, evaluated on a Commercial off-the-Shelf (CotS) Intel i7-1165G7, making it suitable for real-time robotic applications. These results highlight the potential of Predictive Spliner to enhance the performance and safety of autonomous racing vehicles.