Autonomous driving paper index
Optimal Trajectory Planning Using Data-Driven Tire-Road Friction Mapping for Autonomous Racing Cars
One-line summary
This paper presents a trajectory planning strategy to optimize lap times by employing minimum curvature optimization, complemented by Gaussian Process models for tire-road friction estimation.
Engineering notes
Key topics: autonomous driving, trajectory planning, planning. See the paper for implementation details and experimental results.
Chinese explanation / 中文解读
中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。
Original abstract
Autonomous racing vehicles rely on global planning algorithms to generate efficient and safe trajectories, which are crucial for minimizing lap times. This paper presents a trajectory planning strategy to optimize lap times by employing minimum curvature optimization, complemented by Gaussian Process models for tire-road friction estimation. The incorporation of Gaussian Process models into the global planning framework has been shown to improve the accuracy of vehicle motion approximation, subsequently enhancing the tracking performance of the generated trajectory. This paper details the friction estimation techniques, the integration of the friction map with global planning, and the validation of the proposed trajectory planning framework. Simulation results demonstrate the efficiency and safety of the planning outcomes from the proposed methods, highlighting their potential advantages in real-world racing scenarios.
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