# Decision-Making Technology for Autonomous Vehicles Learning-Based Methods, Applications and Future Outlook

arXiv: https://arxiv.org/abs/2107.01110 · HTML: https://mlsanity.com/paper/2107.01110/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 64 references that are in the map.
- First posted on arXiv 2 Jul 2021.

## Builds on (most cited first, 18 of 18)

- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [End-to-end Driving via Conditional Imitation Learning](https://mlsanity.com/paper/1710.02410/) (2017)
- [A Survey of Autonomous Driving: Common Practices and Emerging Technologies](https://mlsanity.com/paper/1906.05113/) (2019)
- [DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving](https://mlsanity.com/paper/1505.00256/) (2015)
- [INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps](https://mlsanity.com/paper/1910.03088/) (2019)
- [Self-Driving Cars: A Survey](https://mlsanity.com/paper/1901.04407/) (2019)
- [The inD Dataset: A Drone Dataset of Naturalistic Road User Trajectories at German Intersections](https://mlsanity.com/paper/1911.07602/) (2019)
- [Game-Theoretic Modeling of Driver and Vehicle Interactions for Verification and Validation of Autonomous Vehicle Control Systems](https://mlsanity.com/paper/1608.08589/) (2016)
- [Risk-Aware High-level Decisions for Automated Driving at Occluded Intersections with Reinforcement Learning](https://mlsanity.com/paper/2004.04450/) (2020)
- [A Review of Tracking, Prediction and Decision Making Methods for Autonomous Driving](https://mlsanity.com/paper/1909.07707/) (2019)
- [Reinforcement Learning with Uncertainty Estimation for Tactical Decision-Making in Intersections](https://mlsanity.com/paper/2006.09786/) (2020)
- [Addressing Inherent Uncertainty: Risk-Sensitive Behavior Generation for Automated Driving using Distributional Reinforcement Learning](https://mlsanity.com/paper/2102.03119/) (2021)
- [Interactive Decision Making for Autonomous Vehicles in Dense Traffic](https://mlsanity.com/paper/1909.12914/) (2019)
- [LIDAR-based Driving Path Generation Using Fully Convolutional Neural Networks](https://mlsanity.com/paper/1703.08987/) (2017)
- [Behavior Planning of Autonomous Cars with Social Perception](https://mlsanity.com/paper/1905.00988/) (2019)
- [Decision-Making for Automated Vehicles Using a Hierarchical Behavior-Based Arbitration Scheme](https://mlsanity.com/paper/2003.01149/) (2020)
- [A New Approach for Tactical Decision Making in Lane Changing: Sample Efficient Deep Q Learning with a Safety Feedback Reward](https://mlsanity.com/paper/2009.11905/) (2020)
- [A Multi-State Social Force Based Framework for Vehicle-Pedestrian Interaction in Uncontrolled Pedestrian Crossing Scenarios](https://mlsanity.com/paper/2005.07769/) (2020)

## Built on by (most cited first, 14 of 14)

- [Transformer-Based Autonomous Driving Models and Deployment-Oriented Compression: A Survey](https://mlsanity.com/paper/2304.10891/) (2023)
- [Large Language Model guided Deep Reinforcement Learning for Decision Making in Autonomous Driving](https://mlsanity.com/paper/2412.18511/) (2024)
- [Autonomous particles](https://mlsanity.com/paper/2301.10077/) (2023)
- [AUPO -- Abstracted Until Proven Otherwise: A Reward Distribution Based Abstraction Algorithm](https://mlsanity.com/paper/2510.23214/) (2025)
- [Data and Knowledge for Overtaking Scenarios in Autonomous Driving](https://mlsanity.com/paper/2305.19421/) (2023)
- [Building Decision Making Models Through Language Model Regime](https://mlsanity.com/paper/2408.06087/) (2024)
- [Graph Convolution-Based Deep Reinforcement Learning for Multi-Agent Decision-Making in Mixed Traffic Environments](https://mlsanity.com/paper/2201.12776/) (2022)
- [Adaptive Decision-Making for Autonomous Vehicles: A Learning-Enhanced Game-Theoretic Approach in Interactive Environments](https://mlsanity.com/paper/2402.11467/) (2024)
- [Demystifying the Physics of Deep Reinforcement Learning-Based Autonomous Vehicle Decision-Making](https://mlsanity.com/paper/2403.11432/) (2024)
- [Maneuver Decision-Making with Trajectory Streams Prediction for Autonomous Vehicles](https://mlsanity.com/paper/2409.10165/) (2024)
- [Socially-Aware Autonomous Driving: Inferring Yielding Intentions for Safer Interactions](https://mlsanity.com/paper/2504.20004/) (2025)
- [CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning](https://mlsanity.com/paper/2507.14903/) (2025)
- [Control Analysis and Design for Autonomous Vehicles Subject to Imperfect AI-Based Perception](https://mlsanity.com/paper/2509.12137/) (2025)
- [Cooperative Autonomous Driving in Diverse Behavioral Traffic: A Heterogeneous Graph Reinforcement Learning Approach](https://mlsanity.com/paper/2509.25751/) (2025)

## Abstract (arXiv)

Autonomous vehicles have a great potential in the application of both civil and military fields, and have become the focus of research with the rapid development of science and economy. This article proposes a brief review on learning-based decision-making technology for autonomous vehicles since it is significant for safer and efficient performance of autonomous vehicles. Firstly, the basic outline of decision-making technology is provided. Secondly, related works about learning-based decision-making methods for autonomous vehicles are mainly reviewed with the comparison to classical decision-making methods. In addition, applications of decision-making methods in existing autonomous vehicles are summarized. Finally, promising research topics in the future study of decision-making technology for autonomous vehicles are prospected.