Accurate velocity estimation of surrounding moving objects and their trajectories are critical elements of perception systems in Automated/Autonomous Vehicles (AVs) with a direct impact on their safety. These are non-trivial problems due to the diverse types and sizes of such objects and their dynamic and random behaviour. Recent point cloud based solutions often use Iterative Closest Point (ICP) techniques, which are known to have certain limitations. For example, their computational costs are high due to their iterative nature, and their estimation error often deteriorates as the relative velocities of the target objects increase (>2 m/sec). Motivated by such shortcomings, this paper first proposes a novel Detection and Tracking of Moving Objects (DATMO) for AVs based on an optical flow technique, which is proven to be computationally efficient and highly accurate for such problems. \textcolor{black}{This is achieved by representing the driving scenario as a vector field and applying vector calculus theories to ensure spatiotemporal continuity.} We also report the results of a comprehensive performance evaluation of the proposed DATMO technique, carried out in this study using synthetic and real-world data. The results of this study demonstrate the superiority of the proposed technique, compared to the DATMO techniques in the literature, in terms of estimation accuracy and processing time in a wide range of relative velocities of moving objects. Finally, we evaluate and discuss the sensitivity of the estimation error of the proposed DATMO technique to various system and environmental parameters, as well as the relative velocities of the moving objects.

翻译：周围运动目标的精确速度估计及其轨迹是自动驾驶车辆（AV）感知系统的关键要素，直接影响其安全性。由于此类目标类型多样、尺寸各异，且具有动态随机行为，这些问题极具挑战性。现有基于点云的解决方案常采用迭代最近点（ICP）技术，但该技术存在固有局限性：迭代特性导致计算成本高昂，且当目标相对速度超过2米/秒时，其估计误差往往显著恶化。针对上述不足，本文首次提出一种基于光流技术的自动驾驶车辆运动目标检测与跟踪（DATMO）新方法，该方法被证明在此类问题中兼具计算高效性和高精度。\textcolor{black}{其核心思想是将驾驶场景表征为矢量场，并应用矢量微积分理论确保时空连续性。}本研究利用合成数据与真实数据对所提DATMO技术进行了全面性能评估。结果表明，在运动目标相对速度的广泛变化范围内，该技术在估计精度与处理时间两方面均优于文献中现有DATMO技术。最后，我们评估并讨论了所提DATMO技术的估计误差对各类系统参数、环境参数以及运动目标相对速度的敏感性。