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Notes on autonomous driving
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NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment
The research introduces NSF-HRPT, a framework that uses neural semantic fields and structured reasoning to accurately assess risks in safety-critical scenarios such as autonomous driving. The method learns to model…
Compact Latent Coordination for Autonomous Vehicles at Unsignalized Intersections
Researchers proposed a new system called Master-Agent Proto-plan System (MAPS) for coordinating autonomous vehicles at unsignalized intersections using deep reinforcement learning. MAPS outperformed state-of-the-art…
TerraZero: Procedural Driving Simulation for Zero-Demonstration Self-Play at Scale
TerraZero is a procedural driving simulator and training stack for reinforcement learning at scale, designed to train autonomous driving agents without human demonstrations or fallback planners. The simulator provides…
Technical Report on the CVPR 2026@AdvML Workshop Challenge
This text is about the CVPR 2026@AdvML Workshop Challenge which focuses on testing the robustness of vision-language agents (VLAs) used in autonomous driving. The challenge involved creating adversarial images and…
Technical Report on the CVPR 2026@AdvML Workshop Challenge
This text is about the CVPR 2026@AdvML Workshop Challenge, focusing on adversarial attacks against Vision-Language Agents (VLAs) used in autonomous driving. The challenge involves creating adversarial images and…
WCog-VLA: A Dual-Level World-Cognitive Vision-Language-Action Model for End-to-End Autonomous Driving
The research presents WCog-VLA, a novel framework for autonomous driving that bridges semantic world forecasting with generative world evolution. This dual-level model aims to overcome the limitations of existing…
CARLA-GS: Decoupling Representation, Reasoning, and Physics Simulation for Autonomous Driving Corner-Case Synthesis
Researchers have proposed a new method called CARLA-GS for generating corner cases in autonomous driving simulations that are essential for safety evaluation. The method decouples the visual representation, semantic…
CARLA-GS: Decoupling Representation, Reasoning, and Physics Simulation for Autonomous Driving Corner-Case Synthesis
The paper proposes CARLA-GS, a modular pipeline for synthesizing corner cases in autonomous driving simulations. The pipeline decouples visual representation, semantic reasoning, and physics-based execution while…
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