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Dr. Chhagan Devidas Jumnake is a CHB Lecturer in the Department of Computer Science at Shri Vasantrao Naik Night College, Pusad, Maharashtra, India. He is engaged in teaching and academic activities in computer science. His contribution focuses on intelligent traffic monitoring and accident detection using multimodal AI approaches.
The swift expansion of urban demographics, rising automobile ownership, and the proliferation of transportation infrastructures have posed considerable difficulties in traffic regulation, roadway safety, and accident mitigation. Traditional traffic monitoring systems predominantly rely on surveillance cameras, loop detectors, or standalone sensor technologies, which may prove inadequate for reliably discerning intricate traffic scenarios under diverse environmental and operating circumstances. Through the integration of these modalities, intelligent traffic systems can enhance vehicle detection, congestion assessment, identification of erratic driving behaviour, accident recognition, and coordination of emergency responses. This chapter examines the principles, structure, algorithms, applications, assessment techniques, obstacles, and novel developments related to multimodal AI in intelligent traffic surveillance and accident identification. The suggested methodology establishes a theoretical basis for a more secure, efficient, and adaptive intelligent transportation framework.