A Motorcycle Safety: a Vision-Language Framework for Retrospective Evaluation and Real-Time Feedback

Motorcycle safety remains a significant concern owing to the increasing number of crashes involving riders on roads. This study presents a systematic framework for retrospective safety evaluation and real-time advice generation for motorcycle riders. It introduces a novel combination of zero-shot in-context learning and fine-tuning of PaliGemma2 to generate personalized and actionable recommendations for motorcycle riders, representing a new integration of AI techniques in rider safety. The evaluation of a rider's journey incorporates the motorcycle rider's guide, which delineates the protocols for safe riding and traffic maneuvers in compliance with traffic rules. Videos analyzed using ZeroShot undergo human review and subsequently serve to fine-tune PaliGemma2, a relatively small vision-language model. This model was optimized to generate tailored recommendations based on frames processed from the videos. The generated advice underwent manual and visual assessments of the corresponding video frames. The results show significant potential in reducing rider risks, with further optimization achievable through the expansion of training datasets and applications across diverse riding scenarios. This innovative integration of AI into motorcycle safety has far-reaching implications for enhancing rider awareness and preventing accidents globally.