Neuro-Symbolic Reinforcement Learning for Context-Aware Decision Making in Safe Autonomous Vehicles

Autonomous vehicles need to be equipped with smart, understandable, and context-aware decision-making frameworks to drive safely within crowded environments. Current deep learning approaches tend to generalize poorly, lack transparency, and perform inadequately in dealing with uncertainty within dynamic city environments. Towards overcoming these deficiencies, this study suggests a new hybrid approach that combines Neuro-Symbolic reasoning with a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architecture, together with a Deep Q-Network (DQN) for learning through reinforcement. The model employs symbolic logic to enforce traffic regulations and infer context while relying on CNN for extracting spatial features and LSTM for extracting temporal dependencies in vehicle motion. The system is trained and tested using the Lyft Level 5 Motion Prediction dataset, which emulates varied and realistic driving scenarios in urban environments. Enforced on the Python platform, the new framework allows autonomous cars to generate rule-adherent, strong, and explainable choices under diverse driving scenarios. Neuro-symbolic combination is more robust for learning as well as explainability, whereas reinforcement improves long-term rewards regarding safety and efficiency. The experiment shows that the model provides high accuracy of 98% on scenario-based decision-making problems in contrast to classical deep learning models used in safety-critical routing. This work is advantageous to autonomous vehicle manufacturers, smart mobility system developers, and urban planners by providing a scalable, explainable, and reliable AI-based solution for future transportation systems.

@article{Khan2025,
  title     = {Neuro-Symbolic Reinforcement Learning for Context-Aware Decision Making in Safe Autonomous Vehicles},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {5},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Huma Khan and Tarunika D Chaudhari and Janjhyam Venkata Naga Ramesh and A. Smitha Kranthi and Elangovan Muniyandy and Yousef A.Baker El-Ebiary and David Neels Ponkumar Devadhas},
  doi       = {10.14569/IJACSA.2025.0160558},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160558}
}