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.

To answer this question: How to accurately predict the future trajectories of mixed-flow vehicle platoons in traffic oscillation scenarios? This study introduces CPIDLM, a hybrid AI model that significantly improves vehicle movement predictions during traffic speed changes by combining physical driving laws with individual human behavior patterns. By explicitly accounting for driver uncertainty and how vehicles affect one another, this approach reduced distance prediction errors by 16.7% compared to existing methods, providing a more reliable foundation for autonomous vehicles.

Researchers developed COIN, a collaborative and interaction-aware reinforcement learning framework for multi-agent autonomous driving. COIN enables vehicles to learn both individual navigation and system-level cooperation, improving safety and efficiency in dense scenarios such as intersections, roundabouts, and bottlenecks. The framework was evaluated in simulation and further validated on a hybrid robotic platform.

A PPPL researcher has developed a faster way of finding the best diamond-based materials for quantum computers. The new approach treats qubits, the basic units of information in quantum computers and the key components of quantum sensors, as isolated molecules. It has already identified promising materials for practical quantum devices. The new research also shows that qubits can be understood through chemistry, enabling scientists to design and build qubits the way a chemist designs custom molecules for a new medication.