Multi-Sensor Environmental Perception and Adaptive Cruise Control of Intelligent Vehicles Using Kalman Filter

This work aims to analyze the specific application of sensor environment perception based on the Kalman filter algorithm in intelligent vehicles. Hence, this work proposes a design for a multi-sensor environment perception and adaptive cruise control (ACC) system based on the Kalman filter algorithm. The system utilizes multiple sensors to collect data and employs the Kalman filter algorithm to process the data, enabling obstacle detection and tracking. This provides a new solution for environmental perception in intelligent vehicles. Meanwhile, combined with ACC technology, the vehicle speed is adjusted to achieve a safe and efficient autonomous driving experience. The experimental results indicate that the system using the Kalman filter algorithm performs in various scenarios, including different weather conditions, road conditions, and obstacle detection. This work achieves high detection accuracy and tracking precision, with the highest values reaching 97.5% and 96.3%, respectively. In the tests, the ACC system can maintain an appropriate following distance and control the vehicle speed well, whether it is a car, a large truck, or a motorcycle. This work has crucial reference value and promotion significance for developing intelligent vehicle technology.