Researchers proposed HERMES, a holistic end-to-end multimodal driving framework that incorporates long-tail semantic knowledge into trajectory planning for autonomous driving models. This framework uses a foundation-model-assisted annotation pipeline to capture hazard-centric scene information and risk-aware planning guidance, improving overall planning performance and safety in mixed-traffic environments.