Real-Time Braking Habit Prediction and Adaptive Response System: Enhancing Vehicle Braking Experience

With the rapid development of intelligent driving technology, the vehicle braking system has become a key factor in enhancing driving experience and safety. Traditional braking systems often rely on passive response modes, which fail to adapt to individual drivers' braking habits, making it challenging to achieve smoother and more efficient braking control. To address this issue, this paper proposes an intelligent braking system based on real-time braking habit prediction and adaptive response, incorporating a momentum recovery function. Using multi-sensor fusion technology, the system collects multidimensional data during the braking process, and a machine learning model is employed to analyze and predict the driver's braking intent in real-time. By combining historical data with the current road conditions, the system dynamically adjusts the braking response strategy and activates momentum recovery when appropriate, converting braking energy into regenerative power to improve energy efficiency. This design aims to achieve smoother and more precise braking, enhancing driving comfort while improving vehicle safety and energy utilization. Experimental results demonstrate that the system effectively adapts to different drivers' braking habits, reduces the impact during emergency braking, and performs excellently in terms of braking efficiency and energy recovery under various complex road conditions. This study provides new insights into the design and optimization of intelligent braking systems, and explores its potential application in autonomous driving.