Journal of Multimedia Processing and Technologies Hathairat Ketmaneechairat 17 2 2026 https://doi.org/10.6025/jmpt/2026/17/2/75-89 https://www.dline.info/jmpt/fulltext/v17n2/jmptv17n2_3.pdf Preventing traffic accidents in intelligent connected vehicles requires accurate anomaly detection and behavioral modeling. Traditional rule-based systems are insufficient for dynamic traffic environments. This study presents an integrated architecture for vehicle trajectory anomaly detection and scenario-based temporal simulation. The framework synthesises deep learning spatiotemporal modelling, driver fatigue monitoring, and data driven scenario generation into a unified six layer system. Utilizing a dataset of 5,000 samples with 19 features, the core analysis module combines reconstruction based models (DAGMM, VAE) and prediction-based networks (STSSN, STCL) for anomaly detection. A supervised classifier achieves 98.24% accuracy in behavior classification, identifying risk probability and obstacle distance as dominant predictors. A distinguishing feature is the Scenario-Based Temporal Simulation Engine, which models behavioral evolution using probabilistic transition matrices (e.g., follow ï‚® yield ï‚® stop). This engine enables continuous state evolution and closed loop simulation, capturing hierarchical safety responses under varying conditions such as congestion or approaching obstacles. Specific scenarios include approaching obstacles, visibility degradation, and lane maneuvers under traffic pressure. While temporal dynamics are simulated due to dataset constraints, the system provides interpretable decision rules and risk assessments. Results indicate driving behavior emerges from threshold-based interactions rather than independent features. Despite limitations regarding real-time trajectory data, this holistic, risk-centric approach significantly advances autonomous driving safety by enabling proactive risk assessment and robust decision support for nextgeneration intelligent transportation systems. Future work should incorporate real time timestamped datasets to enhance generalization.