70 citations · 71 across the 4 of their papers we have counts for
4 papers
X-Mind: Efficient Visual Chain-of-Thought via Predictive World Model for End-to-End Driving
Bohao Zhao, Chengrui Wei, Guangfeng Jiang +17
Predicting future states is essential for autonomous agents, yet current Vision-Language-Action (VLA) models fundamentally lack this capability, relying instead on reactive percept…
X-Foresight: A Joint Vision-Action Causal Forecasting Network via Predictive World Modeling
Baolu Li, Jingyu Qian, Rui Guo +17
Physical world knowledge resides mainly in videos. Equipping Vision-Language-Action (VLA) models with such knowledge is fundamental for safe and generalizable planning. Predictive…
Graph Convolution Networks for Probabilistic Modeling of Driving Acceleration
Jianyu Su, Peter A. Beling, Rui Guo +1
The ability to model and predict ego-vehicle's surrounding traffic is crucial for autonomous pilots and intelligent driver-assistance systems. Acceleration prediction is important…
Driver Action Prediction Using Deep (Bidirectional) Recurrent Neural Network
Oluwatobi Olabiyi, Eric Martinson, Vijay Chintalapudi +1
Advanced driver assistance systems (ADAS) can be significantly improved with effective driver action prediction (DAP). Predicting driver actions early and accurately can help mitig…