  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.
