7 papers
PEDESTRIANQA: A Benchmark for Vision-Language Models on Pedestrian Intention and Trajectory Prediction
Naman Mishra, Shankar Gangisetty, C. V. Jawahar
Pedestrian intention and trajectory prediction are critical for the safe deployment of autonomous driving systems, directly influencing navigation decisions in complex traffic envi…
MOTOR: A Multimodal Dataset for Two-Wheeler Rider Behavior Understanding
Varun A. Paturkar, Shankar Gangisetty, C. V. Jawahar
Two-wheelers account for a disproportionately high share of road fatalities in the Global South. Research on two-wheeler rider behavior, however, lags far behind four-wheelers, whe…
DriveSafe: A Framework for Risk Detection and Safety Suggestions in Driving Scenarios
Sainithin Artham, Shankar Gangisetty, Avijit Dasgupta +1
Comprehensive situational awareness is essential for autonomous vehicles operating in safety-critical environments, as it enables the identification and mitigation of potential ris…
Towards Safer and Understandable Driver Intention Prediction
Mukilan Karuppasamy, Shankar Gangisetty, Shyam Nandan Rai +2
Autonomous driving (AD) systems are becoming increasingly capable of handling complex tasks, mainly due to recent advances in deep learning and AI. As interactions between autonomo…
AI-Generated Lecture Slides for Improving Slide Element Detection and Retrieval
Suyash Maniyar, Vishvesh Trivedi, Ajoy Mondal +2
Lecture slide element detection and retrieval are key problems in slide understanding. Training effective models for these tasks often depends on extensive manual annotation. Howev…
Pedestrian Intention and Trajectory Prediction in Unstructured Traffic Using IDD-PeD
Ruthvik Bokkasam, Shankar Gangisetty, A. H. Abdul Hafez +1
With the rapid advancements in autonomous driving, accurately predicting pedestrian behavior has become essential for ensuring safety in complex and unpredictable traffic condition…