# Motion Prediction on Self-driving Cars: A Review

arXiv: https://arxiv.org/abs/2011.03635 · HTML: https://mlsanity.com/paper/2011.03635/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2020.

## Built on by (most cited first, 3 of 3)

- [OFMPNet: Deep End-to-End Model for Occupancy and Flow Prediction in Urban Environment](https://mlsanity.com/paper/2404.02263/) (2024)
- [Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review](https://mlsanity.com/paper/2302.11089/) (2023)
- [Kraken: enabling joint trajectory prediction by utilizing Mode Transformer and Greedy Mode Processing](https://mlsanity.com/paper/2312.05144/) (2023)

## Abstract (arXiv)

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.