TriDrive: Joint Driver, Vehicle, and Road Modeling for Forecasting and Driver Monitoring
cs.RO, cs.CV
Submitted: 2026-09-26
Updated: 2026-09-26
Terminology
Sources
- V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning
- PV-WM: A Heterogeneous Micro-Macro World Model for Articulated Pedestrian-Vehicle Co-Rollout
- Driver-WM: A Driver-Centric Traffic-Conditioned Latent World Model for In-Cabin Dynamics Rollout
- BADAS: Context Aware Collision Prediction Using Real-World Dashcam Data
- Distilling the Knowledge in a Neural Network
- Brain4Cars: Car That Knows Before You Do via Sensory-Fusion Deep Learning Architecture
- CEMFormer: Learning to Predict Driver Intentions from In-Cabin and External Cameras via Spatial-Temporal Transformers
- V-JEPA 2.1: Unlocking Dense Features in Video Self-Supervised Learning
- Risk-Aware Selective Multimodal Driver Monitoring with Driver-State World Modeling
- BATON: A Multimodal Benchmark for Bidirectional Automation Transition Observation in Naturalistic Driving
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