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This paper introduces a Vision-Language Model (VLM)-based Advanced Rider Assistance System (ARAS) for motorcycles, using VLMs for contextual hazard reasoning and segmentation-based detection to build dense risk maps. The risk maps encode semantic and physical hazard attributes to generate per-pixel hazard costs specific to motorcycle dynamics. Evaluated in CARLA, the proposed system achieves higher success rates and lower hazard exposure compared to baseline methods.
Motorcycles can navigate complex road hazards more safely thanks to a new system that uses VLMs to understand and map risks in a way that's tailored to their unique dynamics.
Motorcycles face disproportionately high crash risks compared to cars due to limited protection and heightened sensitivity to surface hazards, yet Advanced Rider Assistance Systems (ARAS) remain underdeveloped relative to Advanced Driver Assistance Systems (ADAS). We propose a novel ARAS that enhances motorcycle safety through semantic perception and risk-aware planning. Our approach leverages Vision-Language Models (VLMs) for contextual hazard reasoning and integrates them with segmentation-based detection to construct dense risk maps. These maps encode both semantic characteristics (e.g., pothole severity, puddle slipperiness) and physical attributes (e.g., size, depth), which produce per-pixel hazard costs that capture motorcycle-specific risks. These maps are used by a sampling-based planner tailored to motorcycle dynamics to recommend throttle and steering actions that minimize hazard exposure while advancing toward the destination. We evaluate our system in different scenarios in the CARLA simulator. Compared to the baseline method, our method achieves higher success rates and lower hazard exposure, while qualitative results demonstrate interpretable risk maps and safe trajectory recommendations.
