mlsanityData to 7 Oct 2026

Towards Safe Autonomous Driving Policies using a Neuro-Symbolic Deep Reinforcement Learning Approach

arXiv 2307.01316 ↗First posted 3 Jul 2023cs.RO cs.AI cs.LGcited by 7

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

Abstract (arXiv)

The dynamic nature of driving environments and the presence of diverse road users pose significant challenges for decision-making in autonomous driving. Deep reinforcement learning (DRL) has emerged as a popular approach to tackle this problem. However, the application of existing DRL solutions is mainly confined to simulated environments due to safety concerns, impeding their deployment in real-world. To overcome this limitation, this paper introduces a novel neuro-symbolic model-free DRL approach, called DRL with Symbolic Logic (DRLSL) that combines the strengths of DRL (learning from experience) and symbolic first-order logic (knowledge-driven reasoning) to enable safe learning in real-time interactions of autonomous driving within real environments. This innovative approach provides a means to learn autonomous driving policies by actively engaging with the physical environment while ensuring safety. We have implemented the DRLSL framework in a highway driving scenario using the HighD dataset and demonstrated that our method successfully avoids unsafe actions during both the training and testing phases. Furthermore, our results indicate that DRLSL achieves faster convergence during training and exhibits better generalizability to new highway driving scenarios compared to traditional DRL methods.

Key facts. Cited by 7 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 35 of its 69 references that are in the map. First posted on arXiv 3 Jul 2023.

Builds on

35 of 69 references.

References · 35

  1. 2017CARLA: An Open Urban Driving Simulatorcited by 2,431
  2. 2017Constrained Policy Optimizationcited by 928
  3. 2020Deep Reinforcement Learning for Autonomous Driving: A Surveycited by 698
  4. 2017AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehiclescited by 517
  5. 2017Safe Reinforcement Learning via Shieldingcited by 427
  6. 2018Reward Constrained Policy Optimizationcited by 397
  7. 2018The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systemscited by 276
  8. 2017A Semantic Loss Function for Deep Learning with Symbolic Knowledgecited by 267
  9. 2022A Review of Safe Reinforcement Learning: Methods, Theory and Applicationscited by 169
  10. 2019Neural Logic Machinescited by 144
  11. 2017On a Formal Model of Safe and Scalable Self-driving Carscited by 129
  12. 2020Logical Neural Networkscited by 118
23 more
  1. 2020Inductive logic programming at 30: a new introductioncited by 83
  2. 2019Reinforcement Learning for Temporal Logic Control Synthesis with Probabilistic Satisfaction Guaranteescited by 77
  3. 2018Foundations for Restraining Bolts: Reinforcement Learning with LTLf/LDLf restraining specificationscited by 69
  4. 2017OptLayer - Practical Constrained Optimization for Deep Reinforcement Learning in the Real Worldcited by 64
  5. 2021Neuro-Symbolic Artificial Intelligence: Current Trendscited by 54
  6. 2019Robust Model Predictive Shielding for Safe Reinforcement Learning with Stochastic Dynamicscited by 45
  7. 2021Safe Reinforcement Learning Using Robust Control Barrier Functionscited by 44
  8. 2023Safe Reinforcement Learning via Probabilistic Logic Shieldscited by 41
  9. 2021Self-Supervised Online Reward Shaping in Sparse-Reward Environmentscited by 34
  10. 2021Neuro-Symbolic Reinforcement Learning with First-Order Logiccited by 30
  11. 2020Temporal-Logic-Based Reward Shaping for Continuing Reinforcement Learning Taskscited by 28
  12. 2019Lane Change Decision-making through Deep Reinforcement Learning with Rule-based Constraintscited by 24
  13. 2019Deep Reinforcement Learning with Enhanced Safety for Autonomous Highway Drivingcited by 21
  14. 2020Symbolic Logic meets Machine Learning: A Brief Survey in Infinite Domainscited by 16
  15. 2022Fifty Years of Prolog and Beyondcited by 15
  16. 2023Deep Explainable Relational Reinforcement Learning: A Neuro-Symbolic Approachcited by 15
  17. 2021Reinforcement Learning with External Knowledge by using Logical Neural Networkscited by 10
  18. 2021A Critical Review of Inductive Logic Programming Techniques for Explainable AIcited by 7
  19. 2019Deep Reinforcement-Learning-based Driving Policy for Autonomous Road Vehiclescited by 5
  20. 2022Responsibility-Sensitive Safety: an Introduction with an Eye to Logical Foundations and Formalizationcited by 5
  21. 2023Reinforcement Learning with Knowledge Representation and Reasoning: A Brief Surveycited by 5
  22. 2022Formal Development of Safe Automated Driving using Differential Dynamic Logiccited by 2
  23. 2021Towards formalization and monitoring of microscopic traffic parameters using temporal logiccited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 3; 2026: 1.

7 papers.

  1. 2025Neuro-Symbolic AI in 2024: A Systematic Reviewcited by 36
  2. 2024HackAtari: Atari Learning Environments for Robust and Continual Reinforcement Learningcited by 11
  3. 2025Neuro-Symbolic Generative Diffusion Models for Physically Grounded, Robust, and Safe Generationcited by 9
  4. 2023Symbolic Imitation Learning: From Black-Box to Explainable Driving Policiescited by 1
  5. 2025Integrating Neurosymbolic AI in Advanced Air Mobility: A Comprehensive Surveycited by 1
  6. 2024Bridging the Gap: Representation Spaces in Neuro-Symbolic AIcited by 0
  7. 2026Reinforcement Learning Enhancement Using Vector Semantic Representation and Symbolic Reasoning for Human-Centered Autonomous Emergency Brakingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2021A Survey of Deep RL and IL for Autonomous Driving Policy Learning
  2. 2026Safe Reinforcement Learning of Autonomous Highway Driving: A Unified Framework for Safety and Efficiency
  3. 2023Review of Deep Reinforcement Learning for Autonomous Driving
  4. 2025Evaluating Scenario-based Decision-making for Interactive Autonomous Driving Using Rational Criteria: A Survey
  5. 2022Self-Awareness Safety of Deep Reinforcement Learning in Road Traffic Junction Driving
  6. 2026An LLM-Explainable DRL Framework for Passenger-Directed Autonomous Driving
  7. 2023Predictive Maneuver Planning with Deep Reinforcement Learning (PMP-DRL) for comfortable and safe autonomous driving
  8. 2025LA-RL: Language Action-guided Reinforcement Learning with Safety Guarantees for Autonomous Highway Driving