Motion Prediction on Self-driving Cars: A Review
This is an incremental review paper summarizing existing research for autonomous vehicle developers and researchers.
This paper reviews motion prediction literature for autonomous vehicles, identifying it as the most challenging task and analyzing state-of-the-art methods including classical/physical approaches, deep learning, and reinforcement learning, concluding that deep reinforcement learning is the best candidate for tackling this problem.
The autonomous vehicle motion prediction literature is reviewed. Motion prediction is the most challenging task in autonomous vehicles and self-drive cars. These challenges have been discussed. Later on, the state-of-theart has reviewed based on the most recent literature and the current challenges are discussed. The state-of-the-art consists of classical and physical methods, deep learning networks, and reinforcement learning. prons and cons of the methods and gap of the research presented in this review. Finally, the literature surrounding object tracking and motion will be presented. As a result, deep reinforcement learning is the best candidate to tackle self-driving cars.