TriDrive: Joint Driver, Vehicle, and Road Modeling for Forecasting and Driver Monitoring
cs.RO, cs.CV
Submitted: 2026-09-26
Updated: 2026-09-26
Comments: 32 pages (10-page main text plus appendices), 4 figures, 25 tables. Under review. Code, checkpoints, and demo video: https://huggingface.co/HenryYHW/TriDrive
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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
Related papers
- FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
- MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
- RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
- ARC: A Reasoning Recipe for Robot Foundation Models
- HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
- APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies