# Learning a Decision Module by Imitating Driver's Control Behaviors

arXiv: https://arxiv.org/abs/1912.00191 · HTML: https://mlsanity.com/paper/1912.00191/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2019.

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

- [MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning](https://mlsanity.com/paper/2109.12674/) (2021)
- [Efficient Learning of Safe Driving Policy via Human-AI Copilot Optimization](https://mlsanity.com/paper/2202.10341/) (2022)
- [Safe Driving via Expert Guided Policy Optimization](https://mlsanity.com/paper/2110.06831/) (2021)
- [Uni-RLHF: Universal Platform and Benchmark Suite for Reinforcement Learning with Diverse Human Feedback](https://mlsanity.com/paper/2402.02423/) (2024)
- [Generative Models in Decision Making: A Survey](https://mlsanity.com/paper/2502.17100/) (2025)
- [Plan Your Target and Learn Your Skills: Transferable State-Only Imitation Learning via Decoupled Policy Optimization](https://mlsanity.com/paper/2203.02214/) (2022)
- [Neuro-symbolic Learning Yielding Logical Constraints](https://mlsanity.com/paper/2410.20957/) (2024)
- [SteerVLA: Steering Vision-Language-Action Models in Long-Tail Driving Scenarios](https://mlsanity.com/paper/2602.08440/) (2026)
- [NEUSIS: A Compositional Neuro-Symbolic Framework for Autonomous Perception, Reasoning, and Planning in Complex UAV Search Missions](https://mlsanity.com/paper/2409.10196/) (2024)
- [A Versatile and Efficient Reinforcement Learning Framework for Autonomous Driving](https://mlsanity.com/paper/2110.11573/) (2021)
- [Generative Adversarial Imitation Learning for End-to-End Autonomous Driving on Urban Environments](https://mlsanity.com/paper/2110.08586/) (2021)
- [Fail-Safe Adversarial Generative Imitation Learning](https://mlsanity.com/paper/2203.01696/) (2022)
- [Safe CoR: A Dual-Expert Approach to Integrating Imitation Learning and Safe Reinforcement Learning Using Constraint Rewards](https://mlsanity.com/paper/2407.02245/) (2024)
- [Embodied Intelligence: The Key to Unblocking Generalized Artificial Intelligence](https://mlsanity.com/paper/2505.06897/) (2025)
- [Does Unpredictability Influence Driving Behavior?](https://mlsanity.com/paper/2307.15287/) (2023)
- [Connected Autonomous Vehicle Motion Planning with Video Predictions from Smart, Self-Supervised Infrastructure](https://mlsanity.com/paper/2309.07504/) (2023)
- [Confidence-Guided Human-AI Collaboration: Reinforcement Learning with Distributional Proxy Value Propagation for Autonomous Driving](https://mlsanity.com/paper/2506.03568/) (2025)

## Abstract (arXiv)

Autonomous driving systems have a pipeline of perception, decision, planning, and control. The decision module processes information from the perception module and directs the execution of downstream planning and control modules. On the other hand, the recent success of deep learning suggests that this pipeline could be replaced by end-to-end neural control policies, however, safety cannot be well guaranteed for the data-driven neural networks. In this work, we propose a hybrid framework to learn neural decisions in the classical modular pipeline through end-to-end imitation learning. This hybrid framework can preserve the merits of the classical pipeline such as the strict enforcement of physical and logical constraints while learning complex driving decisions from data. To circumvent the ambiguous annotation of human driving decisions, our method learns high-level driving decisions by imitating low-level control behaviors. We show in the simulation experiments that our modular driving agent can generalize its driving decision and control to various complex scenarios where the rule-based programs fail. It can also generate smoother and safer driving trajectories than end-to-end neural policies.