Patterns of Vehicle Lights: Addressing Complexities in Curation and
  Annotation of Camera-Based Vehicle Light Datasets and Metrics
Ross Greer Akshay Gopalkrishnan Maitrayee Keskar Mohan Trivedi
This paper explores the representation of vehicle lights in computer vision
and its implications for various tasks in the field of autonomous driving.
Different specifications for representing vehicle lights, including bounding
boxes, center points, corner points, and segmentation masks, are discussed in
terms of their strengths and weaknesses. Three important tasks in autonomous
driving that can benefit from vehicle light detection are identified: nighttime
vehicle detection, 3D vehicle orientation estimation, and dynamic trajectory
cues. Each task may require a different representation of the light. The
challenges of collecting and annotating large datasets for training data-driven
models are also addressed, leading to introduction of the LISA Vehicle Lights
Dataset and associated Light Visibility Model, which provides light annotations
specifically designed for downstream applications in vehicle detection, intent
and trajectory prediction, and safe path planning. A comparison of existing
vehicle light datasets is provided, highlighting the unique features and
limitations of each dataset. Overall, this paper provides insights into the
representation of vehicle lights and the importance of accurate annotations for
training effective detection models in autonomous driving applications. Our
dataset and model are made available at
https://cvrr.ucsd.edu/vehicle-lights-dataset