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Traffic Light Recognition Using Deep Learning and Prior Maps for Autonomous Cars

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 Vitória, 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.


The following video demonstrates the proposed system working offline (not in the car).


Trained Models

Datasets used for training:

We trained a YOLOv3 model that can detect traffic lights and classify their state: red or yellow (in a single class); and green. To run the inference using our trained model, you will need the following files:

After compiling darknet and downloading the previous files to the same place where the darknet binary is, run the folowing command to see the results on your own images.

./darknet detector test yolov3-nrgr-10000-const-lr-1e-4.cfg yolov3-nrgr-10000-const-lr-1e-4_15000.weights YOUR_IMAGE.png
# Or, for multiple images (file paths will be read from STDIN):
./darknet detector test yolov3-nrgr-10000-const-lr-1e-4.cfg yolov3-nrgr-10000-const-lr-1e-4_15000.weights

While doing inference you can also pass the option -thresh 0.2 to the binary in order to decrease its confidence threshold to 0.2, like we do in our paper.

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