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SAE AutoDrive挑战系列I中连续四年取得成功后，多伦多大学正在参与系列II竞赛，旨在开发一款能够应对多种城市驾驶场景的L4级自动驾驶乘用车（目标2025年）。准确检测交通灯并正确识别其状态，对于城市中的安全自动驾驶至关重要。本文描述了我们最近为多伦多大学自动驾驶汽车Artemis等自动驾驶车辆重新设计的交通灯感知系统。与大多数交通灯感知系统类似，我们主要依赖基于相机的目标检测器。我们部署YOLOv5检测器在多个相机间进行边界框回归和交通灯分类，并融合观测结果。为提升鲁棒性，我们融入高清语义地图的先验信息，并利用隐马尔可夫模型进行状态滤波。我们展示了一个多相机、实时响应的交通灯感知流水线，可处理包括多个可见交叉路口、交通灯变化、临时遮挡及闪烁状态在内的复杂场景。为验证系统性能，我们收集并标注了一个包含闪烁状态及多种遮挡类型的多样化数据集。结果表明，与单帧单相机目标检测相比，本系统在复杂真实场景中表现出更优性能。