Vulnerable road user detection: state-of-the-art and open challenges
It addresses the critical safety problem of environment perception for autonomous vehicles, but is incremental as it synthesizes existing knowledge without new results.
This survey reviews the state-of-the-art in detecting vulnerable road users like cyclists and pedestrians for autonomous vehicles, covering benchmarks, datasets, and detection techniques, and identifies open challenges and future research directions.
Correctly identifying vulnerable road users (VRUs), e.g. cyclists and pedestrians, remains one of the most challenging environment perception tasks for autonomous vehicles (AVs). This work surveys the current state-of-the-art in VRU detection, covering topics such as benchmarks and datasets, object detection techniques and relevant machine learning algorithms. The article concludes with a discussion of remaining open challenges and promising future research directions for this domain.