This paper proposes a novel pipeline that leverages large language models (LLMs) to derive legal driving requirements for autonomous vehicles (AVs) by grounding LLM reasoning in a traffic scenario taxonomy. The method significantly improves law-scenario matching and the accuracy of derived mandatory and prohibitive requirements, providing a solid foundation for lawful AV development and deployment.

This paper introduces a novel deep reinforcement learning framework for safe multi-agent control in highway merging scenarios, integrating partial attention mechanisms into a QMIX architecture. It proposes both spatial and temporal attention to focus on relevant neighboring vehicles and their historical states, combined with a comprehensive reward signal to balance global traffic objectives and individual agent interests. The approach demonstrates significant improvements in safety, driving speed, and overall reward compared to baseline models in SUMO simulations.

This paper reviews the current state of autonomous driving, identifies limitations in scalability and adaptability, and proposes a data-driven, two-stage fine-tuning process and a "service-oriented modular end-to-end (SO-M-E2E)" architecture to achieve fully autonomous driving while integrating technological and socio-political aspects.

This paper introduces a modular architecture for autonomous vehicles designed for racing in closed circuits. It integrates perception, localization, path planning, and control subsystems to achieve real-time, precise autonomous navigation in controlled environments.