Abnormal Driving Behavior Detection: Deep Reinforcement Learning Based on Expert Guidance

Abnormal driving behavior is a leading cause of road accidents. Traditional detection methods, relying on classification or unsupervised learning, struggle with accuracy and generalization. Deep Reinforcement Learning (DRL) offers potential but faces challenges such as handling unlabeled and imbalanced data, designing effective reward functions, and ensuring efficient exploration. To address these, we propose an expert-guided DRL framework that integrates a CNN-BiLSTM-Self Attention (CBSA) model and an Isolation Forest (iForest) to guide Proximal Policy Optimization (PPO), enhancing detection accuracy and computational efficiency. Our framework consists of two stages. First, a deep learning model trained on labeled data provides expert guidance. Second, unlabeled data is processed through the pre-trained model and iForest, refining the DRL model via a sparse reward function to detect unknown anomalies while mitigating class imbalance and improving generalization. Experiments on an open-source dataset show our method out-performs baselines, achieving the highest recall (0.70) and Fl-score (0.63). Additionally, attention maps and anomaly heatmaps enhance interpretability, confirming its effectiveness for real-time abnormal driving behavior detection and improved driver safety.