Improved Distance Estimation in Dynamic Environments through Multi-Sensor Fusion with Extended Kalman Filter

<div class="section abstract"><div class="htmlview paragraph">The application of multi-sensor fusion for enhanced distance estimation accuracy in dynamic environments is crucial for advanced driver assistance systems (ADAS) and autonomous vehicles. The Limitations of single sensors such as cameras or radar in adverse conditions motivate the use of combined camera and radar data to improve reliability, adaptability, and object recognition. A multi-sensor fusion approach using an Extended Kalman Filter (EKF) is proposed to combine sensor measurements with a dynamic system model, achieving robust and accurate distance estimation. The research utilizes the Mississippi State University Autonomous Vehicular Simulator (MAVS), a physics-based simulation platform, to generate realistic synthetic datasets incorporating sensor imperfections such as noise and missed detections to create a controlled environment for data collection. Data analysis is performed using MATLAB. Qualitative metrics such as visualization of fused data vs ground truth and quantitative metrics such as Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Square Error (RMSE) are employed for performance assessment. Initial results with simulated data demonstrate accurate distance estimation compared to individual sensors. The optimal sensor measurement noise variance and plant noise variance parameters within the EKF are identified, and the algorithm is validated with a real-world dataset from nuScenes, a public large-scale dataset for autonomous driving. In summary, this research introduces a novel simulation-based framework and demonstrates that multi-sensor fusion with an EKF significantly improves distance estimation accuracy in dynamic environments. This is supported by comprehensive evaluation metrics, with validation transitioning from simulated to real-world data, paving the way for safer and more reliable autonomous vehicle control.</div></div>