Vulnerable road user detection: state-of-the-art and open challenges
arXiv:1902.03601
Abstract
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.
References in corpus (5)
- Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning
- Ten Years of Pedestrian Detection, What Have We Learned?
- Object Detection with Deep Learning: A Review
- How Far are We from Solving Pedestrian Detection?
- CityPersons: A Diverse Dataset for Pedestrian Detection