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Vehicle light detection, association, and localization are required forimportant downstream safe autonomous driving tasks, such as predicting avehicle's light state to determine if the vehicle is making a lane change orturning. Currently, many vehicle light detectors use single-stage detectorswhich predict bounding boxes to identify a vehicle light, in a manner decoupledfrom vehicle instances. In this paper, we present a method for detecting avehicle light given an upstream vehicle detection and approximation of avisible light's center. Our method predicts four approximate corners associatedwith each vehicle light. We experiment with CNN architectures, dataaugmentation, and contextual preprocessing methods designed to reducesurrounding-vehicle confusion. We achieve an average distance error from theground truth corner of 4.77 pixels, about 16.33% of the size of the vehiclelight on average. We train and evaluate our model on the LISA Lights Dataset,allowing us to thoroughly evaluate our vehicle light corner detection model ona large variety of vehicle light shapes and lighting conditions. We proposethat this model can be integrated into a pipeline with vehicle detection andvehicle light center detection to make a fully-formed vehicle light detectionnetwork, valuable to identifying trajectory-informative signals in drivingscenes.

Robust Detection, Association, and Localization of Vehicle Lights: A Context-Based Cascaded CNN Approach and Evaluations