Abstract: This review examines noise reduction techniques in Advanced Driver Assistance Systems (ADAS) sensor data management, crucial for enhancing vehicle safety and performance. ADAS relies on real-time data from conventional sensors (e.g., wheel speed sensors, LiDAR, radar, cameras) and MEMS sensors (e.g., accelerometers, gyroscopes) to execute critical functions like lane keeping, collision avoidance, and adaptive cruise control. These sensors are susceptible to thermal noise, mechanical vibrations, and environmental interferences, which degrade system performance. We explore filtering techniques including KalmanNet, Simple Moving Average (SMA), Exponential Moving Average (EMA), Wavelet Denoising, and Low Pass Filtering (LPF), assessing their efficacy in noise reduction and data integrity improvement. These methods are compared using key performance metrics such as Signal-to-Noise Ratio (SNR), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). Recent advancements in hybrid filtering approaches and adaptive algorithms are discussed, highlighting their strengths and limitations for different sensor types and ADAS functionalities. Findings demonstrate the superior performance of Wavelet Denoising for non-stationary signals, SMA and EMA's effectiveness for smoother signal variations, and LPF's excellence in high-frequency noise attenuation with careful tuning. KalmanNet showed significant improvements in noise reduction and data accuracy, particularly in complex and dynamic environments. Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) were especially effective for RADAR sensors, handling non-linearities and providing accurate state estimation. Emphasizing Hardware-in-the-Loop (HIL) bench testing to validate these techniques in real-world scenarios, this study underscores the importance of selecting appropriate methods based on specific noise characteristics and system requirements. This research provides valuable insights for ADAS and autonomous driving technologies development, emphasizing precise signal processing's critical role in ensuring accurate sensor data interpretation and decision-making.

Ahmed Alami and Fouad Belmajdoub. “Noise Reduction Techniques in Adas Sensor Data Management: Methods and Comparative Analysis”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.8 (2024). http://dx.doi.org/10.14569/IJACSA.2024.0150868

@article{Alami2024,
title = {Noise Reduction Techniques in Adas Sensor Data Management: Methods and Comparative Analysis},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150868},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150868},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {8},
author = {Ahmed Alami and Fouad Belmajdoub}
}