DriveIntent

Efficient perception and predictive intelligence are critical components of modern autonomous driving systems. DriveIntent: AI-Powered Trajectory Prediction System is designed to enhance road safety by accurately forecasting the future movements of vulnerable road users using advanced artificial intelligence techniques. The system employs transformer- based trajectory prediction combined with multi-sensor fusion to generate context-aware and multimodal future paths, overcoming the limitations of traditional reactive approaches. It further improves prediction reliability by modelling social interactions between agents and leveraging kinematic motion features. To ensure robustness, data from cameras, LiDAR, and radar are integrated, enabling consistent performance under occlusion and challenging environmental conditions. A real-time backend supports efficient inference, while an interactive dashboard provides intuitive visualization of predicted trajectories. Developed using modern deep learning frameworks and scalable system architecture, DriveIntent demonstrates the potential of AI-driven prediction systems to transform safety and decision- making in autonomous mobility.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-05-11
DOI
https://doi.org/10.5281/zenodo.20117110
Primary Topic
Autonomous Vehicle Technology and Safety
Type
article
Field-Weighted Citation Impact
0.00
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article

DriveIntent

Sanjay Nithin S, Sajith.J, Sudhan Sanjay V P, Vignesh K et al.
Zenodo (CERN European Organization for Nuclear Research)
Autonomous Vehicle Technology and Safety
article

DriveIntent

Sanjay Nithin S, Sajith.J, Sudhan Sanjay V P, Vignesh K, Ms. Sini Prabhakar
article en

Abstract

Efficient perception and predictive intelligence are critical components of modern autonomous driving systems. DriveIntent: AI-Powered Trajectory Prediction System is designed to enhance road safety by accurately forecasting the future movements of vulnerable road users using advanced artificial intelligence techniques. The system employs transformer- based trajectory prediction combined with multi-sensor fusion to generate context-aware and multimodal future paths, overcoming the limitations of traditional reactive approaches. It further improves prediction reliability by modelling social interactions between agents and leveraging kinematic motion features. To ensure robustness, data from cameras, LiDAR, and radar are integrated, enabling consistent performance under occlusion and challenging environmental conditions. A real-time backend supports efficient inference, while an interactive dashboard provides intuitive visualization of predicted trajectories. Developed using modern deep learning frameworks and scalable system architecture, DriveIntent demonstrates the potential of AI-driven prediction systems to transform safety and decision- making in autonomous mobility.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 8%
Autonomous Vehicle Technology and Safety
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