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RegulaRAG outperforms existing RAG systems by 43% in generating regulation-compliant scenarios while using significantly fewer tokens, showcasing a breakthrough in efficiency and effectiveness.
Large language models can automate complex model transformations in automotive engineering, cutting manual effort significantly while ensuring structural validity.
Ditch the ECU-by-ECU grind: this ViL framework lets you test full autonomous driving stacks on a central car server by syncing a real vehicle with its digital twin.
LLMs can intelligently route sensor modalities in autonomous vehicles, slashing computation by 6% without sacrificing driving performance.
Automating SDV testing is now closer to reality: a GenAI pipeline turns natural language requirements into runnable test scripts with 89% success.
