mlsanityData to 14 Oct 2026

Autonomous vehicle decision and control through reinforcement learning with traffic flow randomization

arXiv 2403.02882 ↗First posted 5 Mar 2024eess.SY cs.LG cs.ROcited by 0

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

also in 1 more
Abstract (arXiv)

Most of the current studies on autonomous vehicle decision-making and control tasks based on reinforcement learning are conducted in simulated environments. The training and testing of these studies are carried out under rule-based microscopic traffic flow, with little consideration of migrating them to real or near-real environments to test their performance. It may lead to a degradation in performance when the trained model is tested in more realistic traffic scenes. In this study, we propose a method to randomize the driving style and behavior of surrounding vehicles by randomizing certain parameters of the car-following model and the lane-changing model of rule-based microscopic traffic flow in SUMO. We trained policies with deep reinforcement learning algorithms under the domain randomized rule-based microscopic traffic flow in freeway and merging scenes, and then tested them separately in rule-based microscopic traffic flow and high-fidelity microscopic traffic flow. Results indicate that the policy trained under domain randomization traffic flow has significantly better success rate and calculative reward compared to the models trained under other microscopic traffic flows.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving. Builds on 19 of its 37 references that are in the map. First posted on arXiv 5 Mar 2024.

Builds on

19 of 37 references.

Closest ancestors · 1

Same direction, newest first

  1. 2021MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning26 Sep

Other references · 18

  1. 2018Soft Actor-Critic Algorithms and Applicationscited by 1,240
  2. 2018Learning Dexterous In-Hand Manipulationcited by 687
  3. 2017AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehiclescited by 523
  4. 2016Learning to reinforcement learncited by 377
  5. 2017Deep Reinforcement Learning framework for Autonomous Drivingcited by 225
  6. 2019Action Robust Reinforcement Learning and Applications in Continuous Controlcited by 174
  7. 2022TrafficGen: Learning to Generate Diverse and Realistic Traffic Scenarioscited by 106
  8. 2015Deep Reinforcement Learning in Parameterized Action Spacecited by 68
  9. 2022CitySim: A Drone-Based Vehicle Trajectory Dataset for Safety Oriented Research and Digital Twinscited by 43
  10. 2018Automated Speed and Lane Change Decision Making using Deep Reinforcement Learningcited by 36
  11. 2020Automated Lane Change Strategy using Proximal Policy Optimization-based Deep Reinforcement Learningcited by 31
  12. 2022InterSim: Interactive Traffic Simulation via Explicit Relation Modelingcited by 27
6 more
  1. 2021Deep Structured Reactive Planningcited by 19
  2. 2018Automated vehicle's behavior decision making using deep reinforcement learning and high-fidelity simulation environmentcited by 16
  3. 2023LimSim: A Long-term Interactive Multi-scenario Traffic Simulatorcited by 13
  4. 2021TIP: Task-Informed Motion Prediction for Intelligent Vehiclescited by 8
  5. 2024Discretionary Lane-Change Decision and Control via Parameterized Soft Actor-Critic for Hybrid Action Spacecited by 6
  6. 2021Enhancing SUMO simulator for simulation based testing and validation of autonomous vehiclescited by 2

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2024Traffic Smoothing Controllers for Autonomous Vehicles Using Deep Reinforcement Learning and Real-World Trajectory Data
  2. 2025Self-Regulating Cars: Automating Traffic Control in Free Flow Road Networks
  3. 2023Parameterized Decision-making with Multi-modal Perception for Autonomous Driving
  4. 2026Aligning Microscopic Vehicle and Macroscopic Traffic Statistics: Reconstructing Driving Behavior from Partial Data
  5. 2025A Hybrid Input based Deep Reinforcement Learning for Lane Change Decision-Making of Autonomous Vehicle
  6. 2023Integration of Reinforcement Learning Based Behavior Planning With Sampling Based Motion Planning for Automated Driving
  7. 2025Impact of Collective Behaviors of Autonomous Vehicles on Urban Traffic Dynamics: A Multi-Agent Reinforcement Learning Approach
  8. 2023Data-driven Traffic Simulation: A Comprehensive Review