Abstract
Perception is a key component of Automated vehicles (AVs). However, sensors mounted to the AVs often encounter blind spots due to obstructions from other vehicles, infrastructure, or objects in the surrounding area. While recent advancements in planning and control algorithms help AVs react to sudden object appearances from blind spots at low speeds and less complex scenarios, challenges remain at high speeds and complex intersections. Vehicle to Infrastructure (V2I) technology promises to enhance scene representation for AVs in complex intersections, providing sufficient time and distance to react to adversary vehicles violating traffic rules. Most existing methods for infrastructure-based vehicle detection and tracking rely on LIDAR, RADAR or sensor fusion methods, such as LIDAR-Camera and RADAR-Camera. Although LIDAR and RADAR provide accurate spatial information, the sparsity of point cloud data limits its ability to capture detailed object contours of objects far away, resulting in inaccurate 3D object detection results. Furthermore, the absence of LIDAR or RADAR at every intersection increases the cost of implementing V2I technology. To address these challenges, this paper proposes a V2I framework that utilizes monocular traffic cameras at road intersections to detect 3D objects. The results from the roadside unit (RSU) are then combined with the on-board system using an asynchronous late fusion method to enhance scene representation. Additionally, the proposed framework provides a time delay compensation module to compensate for the processing and transmission delay from the RSU. Lastly, the V2I framework is tested by simulating and validating a scenario similar to the one described in an industry report by Waymo. The results show that the proposed method improves the scene representation and the AV's perception range, giving enough time and space to react to adversary vehicles.
Abstract (translated)
感知是自动驾驶车辆（AV）的关键组成部分。然而，安装在AV上的传感器经常会因为其他车辆、基础设施或周围物体的遮挡而产生盲点。尽管最近规划和控制算法的进步有助于AV在低速和简单场景下应对从盲点突然出现的对象，但在高速度和复杂交叉口的情况下仍面临挑战。车到基础设施（V2I）技术有望提升复杂交叉口中自动驾驶车辆的场景表示能力，为遵守交通规则的对手车辆提供足够的反应时间和距离。目前大多数基于基础设施的车辆检测和跟踪方法依赖于激光雷达、雷达或传感器融合技术，如激光雷达-摄像头和雷达-摄像头组合。虽然激光雷达和雷达能提供精确的空间信息，但由于点云数据稀疏性，远距离物体细节轮廓捕捉能力有限，导致三维目标检测结果不准确。此外，每个交叉口都配备激光雷达或雷达会增加实施V2I技术的成本。 为解决这些挑战，本文提出了一种基于单目交通摄像头在道路交叉口进行3D对象检测的V2I框架。该框架通过异步延迟融合方法将路边单元（RSU）的结果与车载系统结合，以增强场景表示能力。此外，还提出了一个时间延迟补偿模块来弥补RSU处理和传输延迟。最后，在模拟并验证类似于Waymo行业报告中描述的情况下后测试了V2I框架。结果表明，该方法能改善场景表示能力和AV的感知范围，为对手车辆提供足够的反应时间和空间。
URL
https://arxiv.org/abs/2504.08172
