A SELF-SUPERVISED LEARNING FRAMEWORK FOR ROBUST AUTONOMOUS VEHICLE NAVIGATION IN COMPLEX INDIAN ROAD NETWORKS

Abstract

Autonomous vehicle navigation in India presents unique challenges due to highly unstructured roads, diverse traffic participants, unpredictable human behavior, and inconsistent lane markings [1], [2]. Conventional supervised learning approaches struggle to generalize in such dynamic environments because they require extensive labeled data, which is both time-consuming and impractical to obtain at scale [3], [10]. This study proposes a self-supervised learning (SSL) framework that leverages unlabeled driving videos, multimodal sensor streams, and contrastive representation learning to develop a robust navigation and perception model for Indian road conditions [12]. The proposed approach enables the vehicle to learn meaningful road semantics, object behavior patterns, and motion cues without explicit annotations [4], [11]. Experiments demonstrate that SSL significantly improves navigation stability, obstacle avoidance, and environment understanding in dense traffic scenarios compared to traditional supervised counterparts [1], [5], [6]. This framework provides a scalable pathway for building reliable autonomous driving systems tailored to Indian roads [7], [8], [9].