The field of autonomous driving is rapidly evolving, with a focus on improving safety, efficiency, and decision-making. Recent research has emphasized the importance of integrating prediction and planning, as well as developing more accurate and robust models of human behavior and vehicle interactions. Noteworthy papers in this area include Perfect Prediction or Plenty of Proposals?, which investigates the role of prediction in integrated prediction and planning approaches, and Vision-Centric 4D Occupancy Forecasting and Planning via Implicit Residual World Models, which proposes a novel world model that focuses on modeling the current state and evolution of the world. Additionally, SparseWorld: A Flexible, Adaptive, and Efficient 4D Occupancy World Model Powered by Sparse and Dynamic Queries presents a flexible and adaptive world model that achieves state-of-the-art performance in perception, forecasting, and planning tasks.
Advancements in Autonomous Driving Research
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A Comparative Study of Oscillatory Perturbations in Car-Following Models
Perfect Prediction or Plenty of Proposals? What Matters Most in Planning for Autonomous Driving
Hypergame-based Cognition Modeling and Intention Interpretation for Human-Driven Vehicles in Connected Mixed Traffic
A Motivational Driver Steering Model: Task Difficulty Homeostasis From Control Theory Perspective
Vision-Centric 4D Occupancy Forecasting and Planning via Implicit Residual World Models
SparseWorld: A Flexible, Adaptive, and Efficient 4D Occupancy World Model Powered by Sparse and Dynamic Queries
From Forecasting to Planning: Policy World Model for Collaborative State-Action Prediction
Behavior-Aware Online Prediction of Obstacle Occupancy using Zonotopes