Predictive Spliner: Data-Driven Overtaking in Autonomous Racing Using Opponent Trajectory Prediction
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Date
2025-02
Publication Type
Journal Article
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yes
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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.
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published
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Journal / series
Volume
10 (2)
Pages / Article No.
1816 - 1823
Publisher
IEEE
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Software
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Date created
Subject
Autonomous driving; collision avoidance; motion planning
Organisational unit
09563 - Zeilinger, Melanie / Zeilinger, Melanie
01225 - D-ITET Zentr. f. projektbasiertes Lernen / D-ITET Center for Project-Based Learning
02636 - Institut für Integrierte Systeme / Integrated Systems Laboratory
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Is new version of: 10.48550/arXiv.2410.04868