Fault detection with confidence level evaluation for perception module of autonomous vehicles based on long short term memory and Gaussian Mixture Model

The reliability of perception is crucial for developing higher levels of autonomy to enhance the safety and performance of fully autonomous vehicles. As a result, fault detection in the perception module is an essential function for autonomous vehicles. While many approaches rely on hardware redundancy, necessitating additional sensors, achieving analytical redundancy in perception is a pivotal factor in reducing the cost and complexity of the autonomous system. This paper utilizes a motion predictor for surrounding vehicles and a Gaussian Mixture Model (GMM) to evaluate the confidence level of the perception module without requiring additional sensor installations. The motion predictor is designed based on a long short-term memory-based recurrent neural network. The error between the motion prediction and detection results is leveraged to estimate the confidence level of the perception module. In essence, the motion prediction results are treated as redundant sensor measurements. The error distribution is modeled using a GMM, and the cumulative probability density of the GMM is employed to assess the confidence level. The proposed algorithm's effectiveness is evaluated through a driving data-based simulation study with fault injection. This study shows an enhanced sensitivity to injected faults and a reduced time for fault detection. Furthermore, the proposed algorithm provides a quantitative estimate of the performance degradation level of the perception module. This estimation can serve as an indicator of uncertainty that the motion planner should account for.