# A Review of Tracking, Prediction and Decision Making Methods for Autonomous Driving

arXiv: https://arxiv.org/abs/1909.07707 · HTML: https://mlsanity.com/paper/1909.07707/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2019.

## Built on by (most cited first, 17 of 17)

- [Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys](https://mlsanity.com/paper/2303.17220/) (2023)
- [Review of Pedestrian Trajectory Prediction Methods: Comparing Deep Learning and Knowledge-based Approaches](https://mlsanity.com/paper/2111.06740/) (2021)
- [Machine Learning for Autonomous Vehicle's Trajectory Prediction: A comprehensive survey, Challenges, and Future Research Directions](https://mlsanity.com/paper/2307.07527/) (2023)
- [Interpretable Self-Aware Neural Networks for Robust Trajectory Prediction](https://mlsanity.com/paper/2211.08701/) (2022)
- [Motion-Grounded Video Reasoning: Understanding and Perceiving Motion at Pixel Level](https://mlsanity.com/paper/2411.09921/) (2024)
- [Decision-Making Technology for Autonomous Vehicles Learning-Based Methods, Applications and Future Outlook](https://mlsanity.com/paper/2107.01110/) (2021)
- [Trajectory Prediction for Autonomous Driving: Progress, Limitations, and Future Directions](https://mlsanity.com/paper/2503.03262/) (2025)
- [VT-Former: An Exploratory Study on Vehicle Trajectory Prediction for Highway Surveillance through Graph Isomorphism and Transformer](https://mlsanity.com/paper/2311.06623/) (2023)
- [Relation-based Motion Prediction using Traffic Scene Graphs](https://mlsanity.com/paper/2212.02503/) (2022)
- [Enhanced method for reinforcement learning based dynamic obstacle avoidance by assessment of collision risk](https://mlsanity.com/paper/2212.04123/) (2022)
- [ComTraQ-MPC: Meta-Trained DQN-MPC Integration for Trajectory Tracking with Limited Active Localization Updates](https://mlsanity.com/paper/2403.01564/) (2024)
- [RiskNet: Interaction-Aware Risk Forecasting for Autonomous Driving in Long-Tail Scenarios](https://mlsanity.com/paper/2504.15541/) (2025)
- [OccluTrack: Rethinking Awareness of Occlusion for Enhancing Multiple Pedestrian Tracking](https://mlsanity.com/paper/2309.10360/) (2023)
- [A Socially Aware Reinforcement Learning Agent for The Single Track Road Problem](https://mlsanity.com/paper/2109.05486/) (2021)
- [TrajectoryNAS: A Neural Architecture Search for Trajectory Prediction](https://mlsanity.com/paper/2403.11695/) (2024)
- [F1tenth Autonomous Racing With Offline Reinforcement Learning Methods](https://mlsanity.com/paper/2408.04198/) (2024)
- [Adversarial Attack for RGB-Event based Visual Object Tracking](https://mlsanity.com/paper/2504.14423/) (2025)

## Abstract (arXiv)

This literature review focuses on three important aspects of an autonomous car system: tracking (assessing the identity of the actors such as cars, pedestrians or obstacles in a sequence of observations), prediction (predicting the future motion of surrounding vehicles in order to navigate through various traffic scenarios) and decision making (analyzing the available actions of the ego car and their consequences to the entire driving context). For tracking and prediction, approaches based on (deep) neural networks and other, especially stochastic techniques, are reported. For decision making, deep reinforcement learning algorithms are presented, together with methods used to explore different alternative actions, such as Monte Carlo Tree Search.