# Safe Decision-making for Lane-change of Autonomous Vehicles via Human Demonstration-aided Reinforcement Learning

arXiv: https://arxiv.org/abs/2207.00448 · HTML: https://mlsanity.com/paper/2207.00448/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 9 of its 27 references that are in the map.
- First posted on arXiv 1 Jul 2022.

## Placed in

- End-to-end autonomous driving / [Reinforcement learning for driving](https://mlsanity.com/driving/reinforcement-learning-for-driving/)

## Builds on (most cited first, 9 of 9)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations](https://mlsanity.com/paper/1709.10087/) (2017)
- [Efficient Deep Reinforcement Learning with Imitative Expert Priors for Autonomous Driving](https://mlsanity.com/paper/2103.10690/) (2021)
- [Making Efficient Use of Demonstrations to Solve Hard Exploration Problems](https://mlsanity.com/paper/1909.01387/) (2019)
- [Prioritized Experience-based Reinforcement Learning with Human Guidance for Autonomous Driving](https://mlsanity.com/paper/2109.12516/) (2021)
- [Human-in-the-Loop Deep Reinforcement Learning with Application to Autonomous Driving](https://mlsanity.com/paper/2104.07246/) (2021)
- [Intervention Aided Reinforcement Learning for Safe and Practical Policy Optimization in Navigation](https://mlsanity.com/paper/1811.06187/) (2018)
- [RHONN Modelling-enabled Nonlinear Predictive Control for Lateral Dynamics Stabilization of An In-wheel Motor Driven Vehicle](https://mlsanity.com/paper/2201.08558/) (2022)

## Built on by (most cited first, 5 of 5)

- [HAIM-DRL: Enhanced Human-in-the-loop Reinforcement Learning for Safe and Efficient Autonomous Driving](https://mlsanity.com/paper/2401.03160/) (2024)
- [Goal-Guided Transformer-Enabled Reinforcement Learning for Efficient Autonomous Navigation](https://mlsanity.com/paper/2301.00362/) (2023)
- [Large Language Model guided Deep Reinforcement Learning for Decision Making in Autonomous Driving](https://mlsanity.com/paper/2412.18511/) (2024)
- [A Survey on Data-Driven Modeling of Human Drivers' Lane-Changing Decisions](https://mlsanity.com/paper/2505.06680/) (2025)
- [CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning](https://mlsanity.com/paper/2507.14903/) (2025)

## Abstract (arXiv)

Decision-making is critical for lane change in autonomous driving. Reinforcement learning (RL) algorithms aim to identify the values of behaviors in various situations and thus they become a promising pathway to address the decision-making problem. However, poor runtime safety hinders RL-based decision-making strategies from complex driving tasks in practice. To address this problem, human demonstrations are incorporated into the RL-based decision-making strategy in this paper. Decisions made by human subjects in a driving simulator are treated as safe demonstrations, which are stored into the replay buffer and then utilized to enhance the training process of RL. A complex lane change task in an off-ramp scenario is established to examine the performance of the developed strategy. Simulation results suggest that human demonstrations can effectively improve the safety of decisions of RL. And the proposed strategy surpasses other existing learning-based decision-making strategies with respect to multiple driving performances.