Julian also highlights the benefits of fine-tuning pretrained LLMs and LGMs with driving data, which can help these models understand the subtleties of complex driving situations and anticipate various road hazards more accurately. These models can also function as co-pilots for drivers, providing real-time coaching, warnings, and directives through natural language. Despite the potential benefits, Julian warns of the risks associated with these technologies, including the possibility of inaccurate information and the potential for distraction if too many cues are provided.
Key takeaways:
- Training large language models (LLMs) and large generative models (LGMs) on global driving data can improve safety for the fleet transportation industry, but can be further enhanced by fusing it with a pretrained multimodal LLM or LGM.
- LLMs and LGMs can help navigate complex driving scenarios by understanding generalized sequences and actions, improving coaching and real-time driving recommendations.
- Pretrained LLMs and LGMs can be fine-tuned with global driving data to understand the subtleties of complex driving situations, functioning as co-pilots for drivers and providing real-time coaching and warnings.
- While this technology has potential, it is not fully developed and carries risks such as hallucination and inaccurate information, and too many cues could distract drivers. Therefore, drivers should use their own insights as well and the amount of recommendations should be customizable.