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

I'm sure you've all read articles about how very soon robots will be coming to take our jobs: self-driving cars taking our driving jobs, self-assembling machines taking our manufacturing jobs, self-eating food taking our restaurant jobs, self-internet surfing AIs taking our office jobs, and so on. I've always thought that I, a noted idiot who babbles about car stuff all day, would be relatively safe from this robotic replacement campaign. There's a new article-writing AI that might prove me wrong, though. The AI system is known as Grover, like Muppets and Presidents, and was developed as a system to detect artificially-generated news stories, when the developers realized the best way to make a fake-news detector was to make a fake-news generator. From what I can tell, the AI does attempt to replicate the style of the website you'd like it to generate a story for, and it appears to even take into account the author.