TriDrive: Joint Driver, Vehicle, and Road Modeling for Forecasting and Driver Monitoring
Predicting how drivers, vehicles, and road scenes interact and evolve together is central to driver monitoring. Prior work models in-cabin activity or traffic-conditioned driver motion in isolation, motivating joint driver, vehicle, and road modeling with real-time on-vehicle evaluation. We introduce TriDrive, to our knowledge the first unified framework that jointly forecasts driver kinematics, vehicle dynamics, and road demands through an automation-conditioned transition model. Modality-specific encoders (an anchored kinematic representation of the driver, causal CAN-bus dynamics, and frozen V-JEPA 2 road latents with structured road margins) are connected by directed residual connecti...
| Authors | Yuhang Wang, Jingxin Yang, Chuheng Wei et al. |
|---|---|
| Primary category | cs.RO |
| Published | 26 September 2026 |
| Last updated at source | 26 September 2026 |
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