3 papers
cs.CV2026
AD-R1: Closed-Loop Reinforcement Learning for End-to-End Autonomous Driving with Impartial World Models
Tianyi Yan, Tao Tang, Xingtai Gui +11
End-to-end models for autonomous driving hold the promise of learning complex behaviors directly from sensor data, but face critical challenges in safety and handling long-tail eve…
cs.CV2025
Map-World: Masked Action planning and Path-Integral World Model for Autonomous Driving
Bin Hu, Zijian Lu, Haicheng Liao +7
Motion planning for autonomous driving must handle multiple plausible futures while remaining computationally efficient. Recent end-to-end systems and world-model-based planners pr…
cs.CV2024
V2VSSC: A 3D Semantic Scene Completion Benchmark for Perception with Vehicle to Vehicle Communication
Yuanfang Zhang, Junxuan Li, Kaiqing Luo +6
Semantic scene completion (SSC) has recently gained popularity because it can provide both semantic and geometric information that can be used directly for autonomous vehicle navig…