Advanced rider assistance systems (ARAS) play a crucial role in enhancing motorcycle safety through features such as collision avoidance, blind-spot detection, and adaptive cruise control, which rely heavily on sensors like radar, cameras, and LiDAR. However, their performance is often compromised under adverse weather conditions, leading to sensor interference, reduced visibility, and inconsistent reliability. This study evaluates the effectiveness and limitations of ARAS technologies in rain, fog, and snow, focusing on how sensor performance, algorithms, techniques, and dataset suitability influence system reliability. A thematic analysis was conducted, selecting studies focused on ARAS in adverse weather conditions based on specific selection criteria. The analysis shows that while ARAS offers substantial safety benefits, its accuracy declines in challenging environments. Existing datasets, algorithms, and techniques were reviewed to identify the most effective options for ARAS applications. However, more comprehensive weather-resilient datasets and adaptive multi-sensor fusion approaches are still needed. Advancing in these areas will be critical to improving the robustness of ARAS and ensuring safer riding experiences across diverse environmental conditions.

Road safety has become an increasingly important concern and the integration of Advanced Rider Assistance Systems and Advanced Driver Assistance Systems plays a crucial role in preventing accidents. This work proposes a computer vision pipeline to automatically detect hazardous road anomalies—loose gravel, potholes, and puddles—from a motorcycle-mounted camera, targeting real-time operation on embedded edge devices. A hybrid dataset of 28764 annotated images was created by combining real-world photos, Blender-rendered synthetic scenes, and AI-generated images to improve diversity and coverage. Multiple state-of-the-art object detectors were trained and benchmarked, including the YOLOv5/7/11/12 families and the transformer-based RT-DETR architecture. While the RT-DETR model achieved the highest precision overall, its computational complexity and heavy resource requirements limited its suitability for real-time deployment on low-cost embedded platforms. Conversely, the YOLOv11n model demonstrated the best accuracy–efficiency trade-off, reaching mAP@0.5 = 0.872 at 320$\times$320 with 0.045 s/frame on a Jetson Nano, while lighter variants remained viable on Raspberry Pi boards. Across classes, gravel was the most reliably detected, and operating points around a confidence threshold of $\tau \approx 0.31$ yielded balanced F1 scores up to 0.82. Although results show that automatic road-condition monitoring on affordable hardware is feasible, the prototype has not yet undergone on-road field trials. It does not include an integrated rider alert module or energy-use assessment. These gaps define the immediate roadmap for deployment.