Following four successful years in the SAE AutoDrive Challenge Series I, the University of Toronto is participating in the Series II competition to develop a Level 4 autonomous passenger vehicle capable of handling various urban driving scenarios by 2025. Accurate detection of traffic lights and correct identification of their states is essential for safe autonomous operation in cities. Herein, we describe our recently-redesigned traffic light perception system for autonomous vehicles like the University of Toronto's self-driving car, Artemis. Similar to most traffic light perception systems, we rely primarily on camera-based object detectors. We deploy the YOLOv5 detector for bounding box regression and traffic light classification across multiple cameras and fuse the observations. To improve robustness, we incorporate priors from high-definition semantic maps and perform state filtering using hidden Markov models. We demonstrate a multi-camera, real time-capable traffic light perception pipeline that handles complex situations including multiple visible intersections, traffic light variations, temporary occlusion, and flashing light states. To validate our system, we collected and annotated a varied dataset incorporating flashing states and a range of occlusion types. Our results show superior performance in challenging real-world scenarios compared to single-frame, single-camera object detection.

翻译：在成功参与SAE自动驾驶挑战赛系列I并取得四年成果后，多伦多大学正投身于系列II竞赛，致力于在2025年前开发一款能够应对各类城市驾驶场景的L4级自动驾驶乘用车。准确检测交通信号灯并正确识别其状态，对于城市环境下的安全自动驾驶至关重要。本文介绍了我们为自动驾驶车辆（如多伦多大学自主研发的Artemis自动驾驶汽车）近期重新设计的交通信号灯感知系统。与多数交通信号灯感知系统类似，我们主要依赖基于摄像头的目标检测器。采用YOLOv5检测器进行多摄像头间的边界框回归与交通信号灯分类，并对观测结果进行融合。为提升鲁棒性，我们引入了高清语义地图的先验信息，并利用隐马尔可夫模型实现状态滤波。我们展示了一种支持多摄像头、具备实时处理能力的交通信号灯感知流水线，可处理包含多交叉口可视、交通信号灯形态变化、临时遮挡及闪烁状态在内的复杂场景。为验证系统性能，我们采集并标注了涵盖闪烁状态及多种遮挡类型的多样化数据集。实验结果表明，与单帧单摄像头目标检测相比，该方法在复杂现实场景中展现出更优性能。