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Traffic safety is a significant issue, particularly for motorcyclists, who are significantly more vulnerable to fatal accidents compared to drivers of enclosed vehicles. Advanced Rider Assistance Systems have the potential to improve motorcyclist safety by mitigating critical risks like rear-end collisions. This paper proposes an integrated Advanced Rider Assistance Systems architecture using the RTDETR object detection model to predict rear-end collisions and provide real-time alerts to the riders. Specifically, it aims to mitigate critical risks like rear-end collisions, offering a comprehensive safety solution. The experimental data indicate that the proposed system is suitable for diverse operational environments, supporting the development of advanced sensing and alert systems to improve motorcyclist safety. Our system achieves an average precision (AP) of 0.688 at IoU=0.50 and reduces collision risk by issuing timely warnings. Contributions include the integration of RTDETR for improved detection accuracy, a multi-threshold warning mechanism, and a detailed analysis of system performance under varied conditions.

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Enhancing Motorcyclist Safety: A RTDETR-Based Proactive Rear-End Collision Detection System

  • 会议论文

  • 发表日期：

    2026年

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  • João A. C. da Silva,
  • Tiago Silva,
  • Ranan Venancio,
  • Lio Gonçalves,
  • Cristiano Pendão,
  • Vítor Filipe

摘要

Traffic safety is a significant issue, particularly for motorcyclists, who are significantly more vulnerable to fatal accidents compared to drivers of enclosed vehicles. Advanced Rider Assistance Systems have the potential to improve motorcyclist safety by mitigating critical risks like rear-end collisions. This paper proposes an integrated Advanced Rider Assistance Systems architecture using the RTDETR object detection model to predict rear-end collisions and provide real-time alerts to the riders. Specifically, it aims to mitigate critical risks like rear-end collisions, offering a comprehensive safety solution. The experimental data indicate that the proposed system is suitable for diverse operational environments, supporting the development of advanced sensing and alert systems to improve motorcyclist safety. Our system achieves an average precision (AP) of 0.688 at IoU=0.50 and reduces collision risk by issuing timely warnings. Contributions include the integration of RTDETR for improved detection accuracy, a multi-threshold warning mechanism, and a detailed analysis of system performance under varied conditions.