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R0:94ecd1a3b2168ef2dbb43222545d08bc-YOLOX: Exceeding YOLO Series in 2021

YOLOX: Exceeding YOLO Series in 2021

We switch the YOLO detector to an anchor-free manner and conduct other advanced detection techniques, i.e., a decoupled head and the leading label assignment strategy SimOTA to achieve state-of-the-art results across a large scale range of models: For YOLO-Nano with only 0.91M parameters and 1.08G…
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R0:1bd6c89454b8a29952356211c7075d12-YOLOP: You Only Look Once for Panoptic Driving Perception

YOLOP: You Only Look Once for Panoptic Driving Perception

A panoptic driving perception system is an essential part of autonomous driving. A high-precision and real-time perception system can assist the vehicle in making the reasonable decision while driving. We present a panoptic driving perception network (YOLOP) to perform traffic object detection, drivable area segmentation…
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Explaining Autonomous Driving by Learning End-to-End Visual Attention

In this work we propose to train an imitation learning based agent equipped with an attention model. The attention model allows us to understand what part of the image has been deemed most important. Interestingly, the use of attention also leads to superior performance in…
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When Autonomous Vehicles Are Hacked, Who Is Liable?

Who might face civil liability if autonomous vehicles (AVs) are hacked to steal data or inflict mayhem, injuries, and damage? How will the civil justice and insurance systems adjust to handle such claims? RAND researchers addressed these questions to help those in the automotive, technology,…
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