In recent years, Autonomous vehicles (AVs) require robust reasoning mechanisms to ensure safety and efficiency dynamic traffic environments. However, existing Hybrid Intrusion Detection System using Deep Reinforcement Learning (HIDS-DRL) model limited by static rule hierarchies, complications in resolving rule conflicts, and lacking generalization to unseen situations. To overcome these limitations, this research proposes the Rule-based Reasoning in Dynamic Knowledge Transformer for AV (RDK-AV) framework that combines symbolic reasoning and data-driven learning. Initially, a dynamic knowledge base encodes traffic laws, contextual constraints, and safety priorities, updated continuously with environmental perception. After that, a rule-based reasoning layer filters candidate actions and resolves conflicts adaptively, while a knowledge-infused transformer captures temporal traffic patterns and aligns them with rule embeddings to support contextual reasoning. Then, a decision fusion module integrates both outputs to generate safe, explainable and adaptive driving policies with increased robustness for autonomous navigation in real-world scenarios. Experimental results demonstrates that the proposed RDK-AV model achieved fairly good performance in optimizing the whole traffic system with Average Cumulative Delay (ACD) per vehicle of 2.6%, when compared with existing HIDS-DRL model.
Khanagoudar et al. (Wed,) studied this question.