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This paper explores the representation of vehicle lights in computer visionand its implications for various tasks in the field of autonomous driving.Different specifications for representing vehicle lights, including boundingboxes, center points, corner points, and segmentation masks, are discussed interms of their strengths and weaknesses. Three important tasks in autonomousdriving that can benefit from vehicle light detection are identified: nighttimevehicle detection, 3D vehicle orientation estimation, and dynamic trajectorycues. Each task may require a different representation of the light. Thechallenges of collecting and annotating large datasets for training data-drivenmodels are also addressed, leading to introduction of the LISA Vehicle LightsDataset and associated Light Visibility Model, which provides light annotationsspecifically designed for downstream applications in vehicle detection, intentand trajectory prediction, and safe path planning. A comparison of existingvehicle light datasets is provided, highlighting the unique features andlimitations of each dataset. Overall, this paper provides insights into therepresentation of vehicle lights and the importance of accurate annotations fortraining effective detection models in autonomous driving applications. Ourdataset and model are made available athttps://cvrr.ucsd.edu/vehicle-lights-dataset

Patterns of Vehicle Lights: Addressing Complexities in Curation and Annotation of Camera-Based Vehicle Light Datasets and Metrics