mlsanityData to 7 Oct 2026

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

arXiv 2207.00448 ↗First posted 1 Jul 2022cs.RO cs.AIcited by 5

On the map: End-to-end autonomous driving → Reinforcement learning for driving

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.

Key facts. Cited by 5 papers in the map (data through 7 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.

Builds on

9 of 27 references.

Closest ancestors · 3

Same direction, newest first

  1. 2021Prioritized Experience-based Reinforcement Learning with Human Guidance for Autonomous Driving26 Sep
  2. 2021Human-in-the-Loop Deep Reinforcement Learning with Application to Autonomous Driving15 Apr
  3. 2021Efficient Deep Reinforcement Learning with Imitative Expert Priors for Autonomous Driving19 Mar

Other references · 6

  1. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  2. 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 769
  3. 2017Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrationscited by 685
  4. 2019Making Efficient Use of Demonstrations to Solve Hard Exploration Problemscited by 45
  5. 2018Intervention Aided Reinforcement Learning for Safe and Practical Policy Optimization in Navigationcited by 12
  6. 2022RHONN Modelling-enabled Nonlinear Predictive Control for Lateral Dynamics Stabilization of An In-wheel Motor Driven Vehiclecited by 1

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 2; 2025: 2.

5 papers.

  1. 2024HAIM-DRL: Enhanced Human-in-the-loop Reinforcement Learning for Safe and Efficient Autonomous Drivingcited by 31
  2. 2024Large Language Model guided Deep Reinforcement Learning for Decision Making in Autonomous Drivingcited by 8
  3. 2023Goal-Guided Transformer-Enabled Reinforcement Learning for Efficient Autonomous Navigationcited by 7
  4. 2025A Survey on Data-Driven Modeling of Human Drivers' Lane-Changing Decisionscited by 2
  5. 2025CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Balancing Progress and Safety: A Novel Risk-Aware Objective for RL in Autonomous Driving
  2. 2020Meta Reinforcement Learning-Based Lane Change Strategy for Autonomous Vehicles
  3. 2024Trustworthy Human-AI Collaboration: Reinforcement Learning with Human Feedback and Physics Knowledge for Safe Autonomous Driving
  4. 2021Multi-agent Reinforcement Learning for Cooperative Lane Changing of Connected and Autonomous Vehicles in Mixed Traffic
  5. 2025A Survey of Reinforcement Learning-Based Motion Planning for Autonomous Driving: Lessons Learned from a Driving Task Perspective
  6. 2024Autonomous Algorithm for Training Autonomous Vehicles with Minimal Human Intervention
  7. 2026Game-Theoretic Inverse Reinforcement Learning for Modeling Competitive Human Driving: A Cut-in Prediction Study
  8. 2025A Hybrid Input based Deep Reinforcement Learning for Lane Change Decision-Making of Autonomous Vehicle