mlsanityData to 14 Oct 2026

Reinforcement Learning Based Safe Decision Making for Highway Autonomous Driving

arXiv 2105.06517 ↗First posted 13 May 2021cs.AI cs.LG cs.ROcited by 5

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

Abstract (arXiv)

In this paper, we develop a safe decision-making method for self-driving cars in a multi-lane, single-agent setting. The proposed approach utilizes deep reinforcement learning (RL) to achieve a high-level policy for safe tactical decision-making. We address two major challenges that arise solely in autonomous navigation. First, the proposed algorithm ensures that collisions never happen, and therefore accelerate the learning process. Second, the proposed algorithm takes into account the unobservable states in the environment. These states appear mainly due to the unpredictable behavior of other agents, such as cars, and pedestrians, and make the Markov Decision Process (MDP) problematic when dealing with autonomous navigation. Simulations from a well-known self-driving car simulator demonstrate the applicability of the proposed method

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 13 of its 36 references that are in the map. First posted on arXiv 13 May 2021.

Builds on

13 of 36 references.

References · 13

  1. 2015Trust Region Policy Optimizationcited by 2,580
  2. 2016End to End Learning for Self-Driving Carscited by 1,009
  3. 2017Constrained Policy Optimizationcited by 983
  4. 2016Safe, Multi-Agent, Reinforcement Learning for Autonomous Drivingcited by 311
  5. 2017Noisy Networks for Explorationcited by 266
  6. 2015DeepDriving: Learning Affordance for Direct Perception in Autonomous Drivingcited by 231
  7. 2017Deep Reinforcement Learning framework for Autonomous Drivingcited by 225
  8. 2016End-to-end Learning of Driving Models from Large-scale Video Datasetscited by 148
  9. 2019Combining Planning and Deep Reinforcement Learning in Tactical Decision Making for Autonomous Drivingcited by 52
  10. 2018Automated Speed and Lane Change Decision Making using Deep Reinforcement Learningcited by 36
  11. 2017Towards Full Automated Drive in Urban Environments: A Demonstration in GoMentum Station, Californiacited by 17
  12. 2016Learning to Drive using Inverse Reinforcement Learning and Deep Q-Networkscited by 15
1 more
  1. 2018Action-Constrained Markov Decision Processes With Kullback-Leibler Costcited by 1

Built on by

Cited by year

Citing papers per year: 2022: 4; 2023: 1.

5 papers.

  1. 2022Robustness and Adaptability of Reinforcement Learning based Cooperative Autonomous Driving in Mixed-autonomy Trafficcited by 15
  2. 2022Prediction-aware and Reinforcement Learning based Altruistic Cooperative Drivingcited by 6
  3. 2023C-MCTS: Safe Planning with Monte Carlo Tree Searchcited by 6
  4. 2022Prediction Based Decision Making for Autonomous Highway Drivingcited by 4
  5. 2022Learning-based social coordination to improve safety and robustness of cooperative autonomous vehicles in mixed trafficcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2022Discrete Control in Real-World Driving Environments using Deep Reinforcement Learning
  2. 2026Safe Reinforcement Learning of Autonomous Highway Driving: A Unified Framework for Safety and Efficiency
  3. 2025A Comprehensive Review of Reinforcement Learning for Autonomous Driving in the CARLA Simulator
  4. 2019Model-free Deep Reinforcement Learning for Urban Autonomous Driving
  5. 2021Building Safer Autonomous Agents by Leveraging Risky Driving Behavior Knowledge
  6. 2024Deep Reinforcement Learning in Autonomous Car Path Planning and Control: A Survey
  7. 2021Evaluating the Robustness of Deep Reinforcement Learning for Autonomous Policies in a Multi-agent Urban Driving Environment
  8. 2020Deep Reinforcement Learning for Autonomous Driving: A Survey