| Summary: | The proliferation of autonomous vehicles (AVs) emphasises the pressing need to navigate challenging road networks riddled with anomalies like unapproved speed bumps, potholes, and other hazardous conditions, particularly in low- and middle-income countries. These anomalies not only contribute to driving stress, vehicle damage, and financial implications for users but also elevate the risk of accidents. A significant hurdle for AV deployment is the vehicle’s environmental awareness and the capacity to localise effectively without excessive dependence on pre-defined maps in dynamically evolving contexts. Addressing this overarching challenge, this paper introduces a specialised deep learning model, leveraging YOLO v4, which profiles road surfaces by pinpointing defects, demonstrating a mean average precision (mAP@0.5) of 95.34%. Concurrently, a comprehensive solution—RA-SLAM, which is an enhanced Visual Simultaneous Localisation and Mapping (V-SLAM) mechanism for road scene modeling, integrated with the YOLO v4 algorithm—was developed. This approach precisely detects road anomalies, further refining V-SLAM through a keypoint aggregation algorithm. Collectively, these advancements underscore the potential for a holistic integration into AV’s intelligent navigation systems, ensuring safer and more efficient traversal across intricate road terrains.
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