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HERMES: A Holistic End-to-End Risk-Aware Multimodal Embodied System with Vision-Language Models for Long-Tail Autonomous Driving

HerMES is proposed, a holistic risk-aware end-to-end multimodal driving framework that explicitly incorporates long-tail semantic knowledge into trajectory planning and demonstrates consistent improvements over representative recent baselines in overall planning performance and across diverse safety-critical scenarios.

Wei-Zhe Tang, Jun-Wei You, Jia-Xi Liu et al. · 5 citations
#artificial intelligence Preprint Sep 2026

AURORA: A Natural Language-Driven Agentic Framework for Understanding, Reasoning, and Orchestrating Reliable Air-Ground Co-Simulation

The results show that reliable scenario generation requires verifying realized behavior, not merely executable code, and demonstrate the value of explicit intermediate representations for verifiable and repairable language-driven co-simulation.

Keshu Wu, Hao Zhang, Rui Gan et al. · 0 citations

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