New multi-modal AI framework brings human-like reasoning to self-driving vehicles

Autonomous driving has advanced rapidly, transitioning from rule-based systems to deep neural networks. Yet end-to-end models still face major deficits: they often lack world knowledge, struggle in rare or ambiguous scenarios, and provide minimal insight into their decision-making process. Large language models (LLMs), by contrast, excel at reasoning, contextual understanding, and interpreting complex instructions. However, LLM outputs are linguistic rather than executable, making integration with real vehicle control difficult. These gaps highlight the need for frameworks that combine multi-modal perception with structured, actionable decision outputs grounded in established driving logic.

https://www.eurekalert.org/news-releases/1109065