Reliable detection and tracking of surrounding objects are indispensable for comprehensive motion prediction and planning of autonomous vehicles. Due to the limitations of individual sensors, the fusion of multiple sensor modalities is required to improve the overall detection capabilities. Additionally, robust motion tracking is essential for reducing the effect of sensor noise and improving state estimation accuracy. The reliability of the autonomous vehicle software becomes even more relevant in complex, adversarial high-speed scenarios at the vehicle handling limits in autonomous racing. In this paper, we present a modular multi-modal sensor fusion and tracking method for high-speed applications. The method is based on the Extended Kalman Filter (EKF) and is capable of fusing heterogeneous detection inputs to track surrounding objects consistently. A novel delay compensation approach enables to reduce the influence of the perception software latency and to output an updated object list. It is the first fusion and tracking method validated in high-speed real-world scenarios at the Indy Autonomous Challenge 2021 and the Autonomous Challenge at CES (AC@CES) 2022, proving its robustness and computational efficiency on embedded systems. It does not require any labeled data and achieves position tracking residuals below 0.1 m. The related code is available as open-source software at https://github.com/TUMFTM/FusionTracking.

翻译：对周围物体的可靠检测与跟踪是实现自动驾驶车辆全面运动预测与规划不可或缺的基础。由于单一传感器的局限性，需要融合多种传感器模态以提升整体检测能力。此外，鲁棒的运动跟踪对于降低传感器噪声影响、提高状态估计精度至关重要。在自主赛车这一涉及车辆操控极限、复杂且对抗性强的高速场景中，自动驾驶软件系统的可靠性变得更为关键。本文提出了一种面向高速应用的模块化多模态传感器融合与跟踪方法。该方法基于扩展卡尔曼滤波（Extended Kalman Filter, EKF），能够融合异构检测输入以一致性地跟踪周围物体。一种新颖的延迟补偿机制能够降低感知软件延迟的影响，并输出更新后的目标列表。这是首个在2021年印地自动驾驶挑战赛（Indy Autonomous Challenge 2021）及2022年CES自动驾驶挑战赛（Autonomous Challenge at CES, AC@CES）的高速真实场景中得到验证的融合与跟踪方法，证明了其在嵌入式系统上的鲁棒性与计算效率。该方法无需任何标注数据，即可实现低于0.1米的位置跟踪残差。相关代码已作为开源软件发布在https://github.com/TUMFTM/FusionTracking。