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Optical Flow Based Detection and Tracking of Moving Objects for Autonomous Vehicles

2024-03-26 15:12:46
MReza Alipour Sormoli, Mehrdad Dianati, Sajjad Mozaffari, Roger woodman


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.

Abstract (translated)

准确的速度估计周围运动的物体及其轨迹是自动驾驶车辆(AVs)感知系统的关键要素,对车辆的安全具有直接影响。由于这些物体具有多样化和大小的非 trivial 问题以及其动态和随机行为,因此这些问题并不简单。最近基于点云的解决方案通常使用迭代最近点(ICP)技术,而众所周知,这种技术存在某些局限性。例如,由于其迭代性质,它们的计算成本很高,并且随着目标物体相对速度的增加(>2 m/sec),它们的估计误差往往恶化。为了克服这些缺陷,本文首先提出了一个基于光流技术的自动驾驶车辆(DATMO)新检测和跟踪系统,该技术通过证明在类似问题中具有计算效率和高度准确性的特点而得到证实。\textcolor{black}{通过将驾驶场景表示为向量场,并应用向量微积分理论来确保时空连续性。}我们还对所提出的 DATMO 技术进行了全面性能评估,该评估在本文中使用合成和真实世界数据进行。本研究的结果表明,与文献中的 DATMO 技术相比,所提出的技术在广泛的相对速度范围内具有更高的估计精度和处理时间。最后,我们评估和讨论了所提出的 DATMO 技术的估计误差对各种系统和环境参数以及运动物体的相对速度的敏感性。



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