Benchmarking vision-language models for traffic scene understanding in South Asian traffic environments

Abstract

A substantial body of research has investigated methods to promote safer driving, yet many challenges persist, particularly in environments with complex traffic patterns. This

study focuses on how drivers evaluate their surroundings and make safety-critical deci- sions, with a specific emphasis on the role of deep learning in enhancing safe driving.

Deep learning enables rapid identification of hazardous situations by providing real-time feedback and alert mechanisms, thereby improving driving behavior and reducing risks.

The objective of this work is to develop an AI-powered driving assistance system de- signed to enhance road safety, especially for inexperienced drivers. Real-world driving

conditions were incorporated by collecting YouTube footage across diverse road types and traffic densities. Experts annotated the dataset by labeling key objects and identifying context-specific risk factors, decision-making cues, and hazard indicators.

The system is powered by a custom-designed AI model capable of providing context- aware guidance, regulatory reminders, and hazard alerts in real time. By leveraging

expert-annotated data and multimodal deep learning techniques, the system delivers per- sonalized and immediate support to increase driver situational awareness, confidence, and

safety.