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

TL;DR

This paper introduces a cascaded CNN approach for robust vehicle light detection, association, and localization, improving accuracy by predicting light corners and reducing confusion with contextual preprocessing, evaluated on the LISA Lights Dataset.

Contribution

The paper presents a novel CNN-based method for vehicle light corner detection that integrates with existing vehicle detection pipelines, enhancing localization accuracy and robustness.

Findings

Average corner detection error of 4.77 pixels

Achieved 16.33% error relative to light size

Effective in diverse lighting and vehicle shapes

Abstract

Vehicle light detection, association, and localization are required for important downstream safe autonomous driving tasks, such as predicting a vehicle's light state to determine if the vehicle is making a lane change or turning. Currently, many vehicle light detectors use single-stage detectors which predict bounding boxes to identify a vehicle light, in a manner decoupled from vehicle instances. In this paper, we present a method for detecting a vehicle light given an upstream vehicle detection and approximation of a visible light's center. Our method predicts four approximate corners associated with each vehicle light. We experiment with CNN architectures, data augmentation, and contextual preprocessing methods designed to reduce surrounding-vehicle confusion. We achieve an average distance error from the ground truth corner of 4.77 pixels, about 16.33% of the size of the vehicle light on average. We train and evaluate our model on the LISA Lights Dataset, allowing us to thoroughly evaluate our vehicle light corner detection model on a large variety of vehicle light shapes and lighting conditions. We propose that this model can be integrated into a pipeline with vehicle detection and vehicle light center detection to make a fully-formed vehicle light detection network, valuable to identifying trajectory-informative signals in driving scenes.