# Deep Reinforcement Learning for Human-Like Driving Policies in Collision Avoidance Tasks of Self-Driving Cars

arXiv: https://arxiv.org/abs/2006.04218 · HTML: https://mlsanity.com/paper/2006.04218/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2020.

## Built on by (most cited first, 7 of 7)

- [Efficient Policy Optimization in Robust Constrained MDPs with Iteration Complexity Guarantees](https://mlsanity.com/paper/2505.19238/) (2025)
- [Towards Human-Like RL: Taming Non-Naturalistic Behavior in Deep RL via Adaptive Behavioral Costs in 3D Games](https://mlsanity.com/paper/2309.15484/) (2023)
- [Rationale-aware Autonomous Driving Policy utilizing Safety Force Field implemented on CARLA Simulator](https://mlsanity.com/paper/2211.10237/) (2022)
- [Experimental Analysis of Trajectory Control Using Computer Vision and Artificial Intelligence for Autonomous Vehicles](https://mlsanity.com/paper/2106.07003/) (2021)
- [Towards human-compatible autonomous car: A study of non-verbal Turing test in automated driving with affective transition modelling](https://mlsanity.com/paper/2212.02908/) (2022)
- [Enhancing High-Speed Cruising Performance of Autonomous Vehicles through Integrated Deep Reinforcement Learning Framework](https://mlsanity.com/paper/2404.14713/) (2024)
- [Modeling Vehicle-Type-Specific Pedestrian Crash Avoidance Behavior in Safety-Critical Interactions Using Smooth-Mamba Deep Reinforcement Learning](https://mlsanity.com/paper/2605.28552/) (2026)

## Abstract (arXiv)

The technological and scientific challenges involved in the development of autonomous vehicles (AVs) are currently of primary interest for many automobile companies and research labs. However, human-controlled vehicles are likely to remain on the roads for several decades to come and may share with AVs the traffic environments of the future. In such mixed environments, AVs should deploy human-like driving policies and negotiation skills to enable smooth traffic flow. To generate automated human-like driving policies, we introduce a model-free, deep reinforcement learning approach to imitate an experienced human driver's behavior. We study a static obstacle avoidance task on a two-lane highway road in simulation (Unity). Our control algorithm receives a stochastic feedback signal from two sources: a model-driven part, encoding simple driving rules, such as lane-keeping and speed control, and a stochastic, data-driven part, incorporating human expert knowledge from driving data. To assess the similarity between machine and human driving, we model distributions of track position and speed as Gaussian processes. We demonstrate that our approach leads to human-like driving policies.