用对手轨迹预测实现自动驾驶赛车超车，实时高效且成功率超前。
Predictive Spliner: Data-Driven Overtaking in Autonomous Racing Using Opponent Trajectory Prediction
- 基于高斯过程学习对手行为，动态规划未来超车路径。
- 超车成功率达84.5%，比之前最佳方法高47.6%。
- 计算耗时仅8.4毫秒，适合实时机器人系统部署。
在自主赛车领域，与对手进行正面竞速是一项具有挑战性且日益重要的课题。本文提出Predictive Spliner，一种数据驱动的超车规划器，通过高斯过程（GP）回归学习对手行为，并据此计算赛道未来路段的可行超车动作。在1:10比例的自主赛车平台上的实验验证表明，该方法利用激光雷达（LiDAR）感知对手，可实现自身速度83.1%的超车能力，平均比此前最优方法快8.4%。同时，其平均超车成功率达84.5%，较前人方法提升47.6%。系统在商用英特尔i7-1165G7处理器上保持22.79%的CPU负载，单次计算耗时8.4毫秒，具备良好的实时性，适用于实际机器人应用。代码已开源：https://github.com/ForzaETH/predictive-spliner。
原文摘要 · Abstract (English)
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 that learns the behavior of opponents through Gaussian Process (GP) regression, which is then leveraged to compute viable overtaking maneuvers in future sections of the racing track. Experimentally validated on a 1:10 scale autonomous racing platform using Light Detection and Ranging (LiDAR) information to perceive the opponent, Predictive Spliner outperforms State-of-the-Art (SotA) 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 method 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. The code for Predictive Spliner is available at: https://github.com/ForzaETH/predictive-spliner.
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