  Autonomous terrestrial vehicles must be capable of perceiving traffic lights
and recognizing their current states to share the streets with human drivers.
Most of the time, human drivers can easily identify the relevant traffic
lights. To deal with this issue, a common solution for autonomous cars is to
integrate recognition with prior maps. However, additional solution is required
for the detection and recognition of the traffic light. Deep learning
techniques have showed great performance and power of generalization including
traffic related problems. Motivated by the advances in deep learning, some
recent works leveraged some state-of-the-art deep detectors to locate (and
further recognize) traffic lights from 2D camera images. However, none of them
combine the power of the deep learning-based detectors with prior maps to
recognize the state of the relevant traffic lights. Based on that, this work
proposes to integrate the power of deep learning-based detection with the prior
maps used by our car platform IARA (acronym for Intelligent Autonomous Robotic
Automobile) to recognize the relevant traffic lights of predefined routes. The
process is divided in two phases: an offline phase for map construction and
traffic lights annotation; and an online phase for traffic light recognition
and identification of the relevant ones. The proposed system was evaluated on
five test cases (routes) in the city of Vit\'oria, each case being composed of
a video sequence and a prior map with the relevant traffic lights for the
route. Results showed that the proposed technique is able to correctly identify
the relevant traffic light along the trajectory.

Computer Vision and Pattern Recognition;Machine Learning;Robotics;Machine Learning