Traffic light detection by fusing object detection and map info
Date
2021
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Tartu Ülikool
Abstract
To share streets with human drivers, self-driving cars must locate traffic lights and
recognize their states. While for human drivers recognizing a relevant traffic light does
not require much effort, it is a challenging task for self-driving cars. Although, for
state-of-the-art object detection methods detecting traffic lights is simple, identifying to
which lane they apply is non-trivial.
The most common approach relies on precise locations of traffic lights on the highdefinition
map, localization of the car, and camera position with respect to the car. When
a vehicle approaches a traffic light, traffic lights from HD-map are projected to the
camera images. Then, regions that include traffic lights, or regions of interest (ROIs) for
traffic lights are extracted and fed to the classifier. To mitigate localization errors, ROIs
need to be enlarged. However, this can lead to imprecise classification as the bounding
box might not capture traffic light adequately.
In this thesis, the problem is addressed by introducing traffic light recognition by
fusing object detection and HD-map information. The process is divided into three
phases: get 2D traffic lights’ ROIs by projecting 3D bounding boxes from the map to the
camera image; perform traffic light detection on the image to get 2D bounding boxes
and traffic light states; associate traffic lights with lanes by matching detected bounding
boxes with ROIs using Intersection-over-Union metric.
The proposed method was integrated into Autoware.AI and tested on prerecorded
routes in Tallinn and Tartu. The approach achieved an accuracy of 93% and outperformed
the approach currently used by Autoware.AI.
Description
Keywords
datasets, neural networks, object detection, traffic light recognition, autonomous driving, HD-map, YOLOv3