Revolutionizing Automotive Safety: A Novel Method for Reverse Propulsion and Regenerative Braking in Critical Scenarios

Automotive safety has become a cornerstone of modern vehicle design, with increasing emphasis on technologies that enhance accident prevention and energy efficiency. This study presents a novel Hybrid Control Mechanism that integrates reverse propulsion and regenerative braking, leveraging artificial intelligence (AI) and machine learning (ML) to dynamically respond to critical scenarios. The proposed system combines predictive algorithms, sensor fusion, and model predictive control to optimize deceleration and energy recovery. Extensive simulations and prototype testing demonstrated a deceleration accuracy of 96.5%, energy recovery efficiency of 76.5%, and a real-time response time of 60 milliseconds, outperforming existing automotive safety solutions. The system evaluated under diverse conditions, including dry, wet, and icy roads, achieved better stability. Comparative analysis confirmed superior performance in terms of stopping distance, response time, and energy efficiency. This research establishes a robust foundation for the adoption of intelligent safety technologies in automotive systems, ensuring safer and more sustainable transportation. Future work will focus on scalability and deployment in real-world scenarios.