Optimizing Vehicle Safety Systems Through Advanced Signal Processing and Sensor Fusion

The Advanced vehicle safety systems are composed of multifaceted and diverse sensor data, which is a huge challenge when it comes to high-precise decision making. The available solutions are usually not able to effectively integrate data between different sensors, radar, LIDAR, cameras and actually work in dynamic driving environments. To enhance sensor data occurrence and precision, the current work proposes superior signal processing strategies, e.g., the Kalman filtering, wavelet transforms and sensor fusion algorithm like the Extended and Unscented Kalman Filters (EKF,UKF). The critical concern involves the incorporation of the simulated sensor data of various sources to make up a strong, single model of the surrounding and help the autonomous vehicle safety structures to improve the decision-making procedure.  The outcomes indicate that the suggested algorithms can considerably remove noise in sensor records and enhance the efficiency of sensor fusion strategies successfully forecasting possible collision cases and maximizing safety responses like adaptive cruise control, collision avoidance and emergency braking. The techniques that have been invented can be used in designing and optimization of the vehicle safety systems so as to increase the road safety through more trustworthy decision-making in the autonomous vehicles. They can yield solutions that can enhance safety functionalities in many autonomous driving systems such as the adaptive cruise control, collision avoidance, and emergency braking.