Early Anticipation of Driving Maneuvers
Authors: Abdul Wasi, Shankar Gangisetty, Shyam Nanadan, CV Jawahar
This work introduces Anticipating Driving Maneuvers (ADM) to predict driver actions before they begin, addressing the limitation of existing methods that only detect maneuvers after onset. It also presents the DAAD dataset and a transformer-based model to improve early maneuver anticipation using multi-view and multimodal data.
Visual Place Recognition in Unstructured Driving Environments
Authors: Utkarsh Rai, Shankar Gangisetty, A. H. Abdul Hafez, Anbumani Subramanian, C V Jawahar
This work introduces a new Indian Visual Place Recognition (VPR) dataset designed for unstructured driving environments with challenges like occlusions, traffic variation, and lighting changes. It also provides an annotation tool and shows that existing methods perform significantly worse, highlighting the need for more robust VPR models
Pedestrian Intention and Trajectory Prediction in Unstructured Traffic Using IDD-PeD
Authors: Ruthvik Bokkasam, Shankar Gangisetty, A. H. Abdul Hafez, C V Jawahar
This work introduces an Indian pedestrian behavior dataset designed for unstructured driving conditions, capturing challenges like occlusions, lighting variations, and vehicle–pedestrian interactions. It shows that existing intention and trajectory prediction models perform significantly worse, highlighting the need for more robust approaches.
Towards Safer and Understandable Driver Intention Predictions
Authors: Mukilan Karuppasamy, Shankar Gangisetty, Shyam Nanadan Rai, Carlo Masone, C V Jawahar
This work introduces DAAD-X, an explainable multimodal dataset for driver intention prediction with human-understandable reasoning using gaze and ego-vehicle data. It also proposes a Video Concept Bottleneck Model (VCBM) to generate interpretable predictions, showing transformer-based models are more explainable than CNNs