@article{4715,
  author = {Hathairat Ketmaneechairat},
  title = {Scenario-Based Temporal Simulation and Behavioral Transition Modeling for Autonomous Driving Using Multi-Feature Risk Dynamics},
  journal = {Journal of Multimedia Processing and Technologies},
  year = {2026},
  volume = {17},
  number = {2},
  doi = {https://doi.org/10.6025/jmpt/2026/17/2/75-89},
  url = {https://www.dline.info/jmpt/fulltext/v17n2/jmptv17n2_3.pdf},
  abstract = {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.},
}