Generative AI for traffic accident prevention system

The Generative AI for Traffic Accident Prevention System marks a monumental leap forward in the domain of intelligent transportation. The project has transcended the limitation of traditional reactive safety systems wherein one needs to "see" a danger before acting on it. In this paper using Generative AI we have shown that a machine can successfully "foresee" danger. This creates an environment that can simulate probabilistic futures and generate synthetic training data addressing two of the biggest hurdles in autonomous safety: human behavior being unpredictable and accident data being scarce. The proposed system develops a dynamic predictive safety shield around the vehicle through multi-modal sensor fusion Video Transformers and Deep Reinforcement Learning. This report presents the feasibility study and comprehensive testing methodologies that validate the technology as computationally demanding yet viable for deployment on modern edge-AI hardware.

1.   Goodfellow I. et al. (2014). Generative Adversarial Nets. Advances in Neural Information Processing Systems. Thisfoundational  text  provided  the architectural basisfor the synthetic data generation module used in the project. 2.  Vaswani A. et al. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems. This paper introduced the Transformer architecture which was adapted in this project for spatiotemporal sequence prediction. 3.  Geiger A. Lenz P. & Urtasun R. (2012). Are we ready for autonomous driving? The KITTI vision benchmark suite. Conference on Computer Vision and Pattern Recognition (CVPR). Used as a benchmark for validating the object detection capabilities of the system. 4. Codevilla F. et al. (2018). End-to-end Driving via Conditional Imitation Learning. International Conference on Robotics and Automation (ICRA). Provided insights into the behavioral cloning and policy learning aspects of the proposed methodology. 5. Dosovitskiy A. et al. (2017). CARLA: An Open Urban Driving Simulator.