ITSC 2025 Paper Abstract

Paper VP-VP.54

Kumar, Rishav (Indian Institute of Technology Hyderabad), D, Santhosh (Indian Institute of Technology Hyderabad), Pachamuthu, Rajalakshmi (Indian Institute of Technology, Hyderabad)

DriveIndia: An Object Detection Dataset for Diverse Indian Traffic Scenes

Scheduled for presentation during the Video Session "On-Demand Video Presentations" (VP-VP), Saturday, November 22, 2025, 08:00−18:00, On-Demand Platform

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on April 2, 2026

Keywords Real-time Object Detection and Tracking for Dynamic Traffic Environments, Advanced Sensor Fusion for Robust Autonomous Vehicle Perception, Deep Learning for Scene Understanding and Semantic Segmentation in Autonomous Vehicles

Abstract

We introduce DriveIndia, a large-scale object detection dataset purpose-built to capture the complexity and unpredictability of Indian traffic environments. The dataset contains 66,986 high-resolution images annotated in YOLO format across 24 traffic-relevant object categories, encompassing diverse conditions such as varied weather (fog, rain), illumination changes, heterogeneous road infrastructure, and dense, mixed traffic patterns and collected over 120+ hours and covering 3,400+ kilometers across urban, rural, and highway routes. DriveIndia offers a comprehensive benchmark for real-world autonomous driving challenges. We provide baseline results using state-of-the-art YOLO family models, with the top-performing variant achieving a mAP50 of 78.7%. Designed to support research in robust, generalizable object detection under uncertain road conditions, DriveIndia will be publicly available via the TiHAN-IIT Hyderabad dataset repository (https://tihan.iith.ac.in/tiand-datasets/).