Car Sensors Health Monitoring by Verification Based on Autoencoder and Random Forest Regression
Sahar Torkhesari, Behnam Yousefimehr, Mehdi Ghatee
TL;DR
The paper tackles sensor health assessment in automotive ECUs by addressing the challenge of multiple sensor faults in real-world driving conditions. It proposes a hybrid pipeline that uses an autoencoder to model inter-sensor relationships and detect anomalies, paired with a random forest regression model to estimate and replace readings from defective sensors, guided by a Gaussian-based health threshold. The approach achieves high fidelity on real-world data from the SaipaQuick ECU, reporting $R^2 > 0.99$ across 20 sensors, and enables proactive driver/maintenance alerts along with value replacement to maintain system reliability. By reducing reliance on hardware redundancy and leveraging inter-sensor correlations, the method offers practical improvements for vehicle health monitoring in production contexts.
Abstract
Driver assistance systems provide a wide range of crucial services, including closely monitoring the condition of vehicles. This paper showcases a groundbreaking sensor health monitoring system designed for the automotive industry. The ingenious system leverages cutting-edge techniques to process data collected from various vehicle sensors. It compares their outputs within the Electronic Control Unit (ECU) to evaluate the health of each sensor. To unravel the intricate correlations between sensor data, an extensive exploration of machine learning and deep learning methodologies was conducted. Through meticulous analysis, the most correlated sensor data were identified. These valuable insights were then utilized to provide accurate estimations of sensor values. Among the diverse learning methods examined, the combination of autoencoders for detecting sensor failures and random forest regression for estimating sensor values proved to yield the most impressive outcomes. A statistical model using the normal distribution has been developed to identify possible sensor failures proactively. By comparing the actual values of the sensors with their estimated values based on correlated sensors, faulty sensors can be detected early. When a defective sensor is detected, both the driver and the maintenance department are promptly alerted. Additionally, the system replaces the value of the faulty sensor with the estimated value obtained through analysis. This proactive approach was evaluated using data from twenty essential sensors in the Saipa's Quick vehicle's ECU, resulting in an impressive accuracy rate of 99\%.