Kernel-Based Metrics Learning for Uncertain Opponent Vehicle Trajectory Prediction in Autonomous Racing

The paper introduces heterogeneous kernel metrics for Deep Kernel Learning to predict uncertain opponent vehicle trajectories in autonomous racing. By aligning similar driving policies and separating dissimilar ones in an unsupervised way, the method improves prediction accuracy and enables safer overtaking. Experiments on a 1/10th scale racecar platform confirm the approach’s effectiveness and computational efficiency for onboard units.

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