Should I Overtake? Cue Learning using Evolution for Accurate Recognition of Safe Autonomous Vehicle Maneuvers
TL;DR
This work creates a novel open-source symbolic traffic model EvoDrive designed specifically for EC research, which outputs LLM-readable snapshots and shows that LLMs + EvoDrive with CLEAR can reduce error by more than 20% compared to without CLEAR, with statistically significant results.
Abstract
The overtake car maneuver involves high risk and complex judgement. For autonomous vehicles this is challenging, especially for human-initiated overtake requests. If a user requests the maneuver there must be a rapid safety assessment. Language models have great potential to classify safety with explanations, but they struggle to disentangle critical information from complex vehicular environments. We apply CLEAR (Cue Learning using Evolution for Accurate Recognition) to evolve prompt cues that optimize the ability of language models to correctly predict safety scores for overtaking maneuvers. To achieve this, we create a novel open-source symbolic traffic model EvoDrive designed specifically for EC research, which outputs LLM-readable snapshots. We show that LLMs + EvoDrive with CLEAR can reduce error by more than 20% compared to without CLEAR, with statistically significant results. Analysis shows evolved cues are coherent and have reduced variability in LLM output.