To address the dual challenges of reducing fuel consumption and ensuring maritime safety under extreme weather, researchers from Dalian University of Technology, Nanyang Technological University, and Chongqing Jiaotong University have developed a novel ship weather routing framework based on Dual-Mode Deep Reinforcement Learning. Utilizing the D3QN algorithm, this framework effectively navigates both normal sea states and severe typhoon conditions, providing a robust route toward maritime safety security and decarbonization.

Researchers developed Found-RL, an open-source platform that integrates vision-language models into reinforcement learning for autonomous driving. By using an asynchronous batch inference framework, Found-RL allows large foundation models to provide semantic feedback without slowing down high-frequency simulation training. Experiments in CARLA show that lightweight reinforcement learning agents can achieve competitive driving performance while maintaining real-time policy inference.

To address the severe safety challenges of AI-driven autonomous driving under the Autonomy 2.0 paradigm, a joint research team from Tongji University, SAMR Defective Product Recall Technical Center, and Tsinghua University, proposed a hierarchical risk-filtering safety assurance framework, establishing a four-category assessment metric system and layered safety strategies, with a dedicated safety monitor for system-level architecture design.

Can vision-language models understand unstructured road scenes under degraded sensing conditions? Researchers developed the Unstructured Scene Benchmark (USB), a test and evaluation framework for autonomous driving models in unstructured road scenes and degraded visual conditions. By testing seven open-source vision-language models across six camera views, 20 corruption types, and 45,969 question-answer samples, the study reveals uneven robustness, weak planning ability, and signs of insufficient visual grounding.

Researchers have developed a method to generate customizable virtual road networks with adjustable complexity, which cuts required test mileage by over 30% while preserving real-world road features. This enables faster, more efficient identification of functional defects and performance boundaries in autonomous vehicles.