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.

翻译：在自动驾驶赛车领域，与对手进行头对头竞赛是一个具有挑战性且新兴的研究课题。我们提出了预测性样条规划器，这是一种数据驱动的超车规划器，它通过高斯过程回归学习对手的行为，并利用该信息在赛道未来路段计算可行的超车轨迹。该方法在1:10比例自动驾驶赛车平台上进行了实验验证，使用激光雷达信息感知对手。实验结果表明，预测性样条规划器以最高可达自身速度83.1%的速度超越对手，平均比先前最佳方法快8.4%，优于现有最先进算法。此外，其平均成功率达到了84.5%，较先前最佳方法提高了47.6%。在商用现成Intel i7-1165G7处理器上评估，该方法保持了计算效率，CPU负载为22.79%，计算时间为8.4毫秒，适用于实时机器人应用。这些结果凸显了预测性样条规划器在提升自动驾驶赛车性能与安全性方面的潜力。预测性样条规划器的代码发布于：https://github.com/ForzaETH/predictive-spliner。