CBR-LLM retrieves similar past driving risk cases and feeds them as few-shot examples to an LLM, improving evasive maneuver recommendations on a real near-miss dashcam dataset.
This could occur if one vehicle crosses over into the opposing lane for any reason, such as a loss of control or attempting to pass another vehicle
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Case-based Reasoning Augmented Large Language Model Framework for Decision Making in Realistic Safety-Critical Driving Scenarios
CBR-LLM retrieves similar past driving risk cases and feeds them as few-shot examples to an LLM, improving evasive maneuver recommendations on a real near-miss dashcam dataset.