Object detection and multiple object tracking (MOT) are essential components of self-driving systems. Accurate detection and uncertainty quantification are both critical for onboard modules, such as perception, prediction, and planning, to improve the safety and robustness of autonomous vehicles. Collaborative object detection (COD) has been proposed to improve detection accuracy and reduce uncertainty by leveraging the viewpoints of multiple agents. However, little attention has been paid to how to leverage the uncertainty quantification from COD to enhance MOT performance. In this paper, as the first attempt to address this challenge, we design an uncertainty propagation framework called MOT-CUP. Our framework first quantifies the uncertainty of COD through direct modeling and conformal prediction, and propagates this uncertainty information into the motion prediction and association steps. MOT-CUP is designed to work with different collaborative object detectors and baseline MOT algorithms. We evaluate MOT-CUP on V2X-Sim, a comprehensive collaborative perception dataset, and demonstrate a 2% improvement in accuracy and a 2.67X reduction in uncertainty compared to the baselines, e.g. SORT and ByteTrack. In scenarios characterized by high occlusion levels, our MOT-CUP demonstrates a noteworthy $4.01\%$ improvement in accuracy. MOT-CUP demonstrates the importance of uncertainty quantification in both COD and MOT, and provides the first attempt to improve the accuracy and reduce the uncertainty in MOT based on COD through uncertainty propagation. Our code is public on https://coperception.github.io/MOT-CUP/.

翻译：目标检测与多目标跟踪（MOT）是自动驾驶系统的核心组成部分。精确的检测与不确定性量化对于感知、预测和规划等车载模块至关重要，能够提升自动驾驶车辆的安全性与鲁棒性。协同目标检测（COD）通过利用多智能体的视角优势，已被提出用于提升检测精度并降低不确定性。然而，如何利用COD的不确定性量化来增强MOT性能尚未受到足够关注。本文作为应对这一挑战的首次尝试，设计了一个名为MOT-CUP的不确定性传播框架。该框架首先通过直接建模与共形预测量化COD的不确定性，并将这一不确定性信息传播至运动预测与数据关联步骤。MOT-CUP可与不同协同目标检测器及基线MOT算法协同工作。我们在V2X-Sim这一综合协同感知数据集上评估了MOT-CUP，结果显示相较于SORT和ByteTrack等基线方法，精度提升2%，不确定性降低2.67倍。在高遮挡场景下，MOT-CUP展示了显著的4.01%精度提升。MOT-CUP证明了不确定性量化在COD与MOT中的重要性，并首次通过不确定性传播基于COD提升MOT精度与降低不确定性。我们的代码已开源至https://coperception.github.io/MOT-CUP/。