Deep reinforcement learning for personalized driving recommendations to mitigate aggressiveness and riskiness: modeling and impact assessment
Lane change detection and prediction using real-world connected vehicle data
Managing merging from a CAV lane to a human-driven vehicle lane considering the uncertainty of human driving
Modeling and simulation of approaching behaviors to signalized intersections based on risk quantification
Robust unsupervised learning of temporal dynamic vehicle-to-vehicle interactions
Short-term traffic prediction using physics-aware neural networks
TrajGAT: a map-embedded graph attention network for real-time vehicle trajectory imputation of roadside perception