With the growth of self-driving technologies, the reliability analysis of automated driving systems has received considerable attention from both academia and industry. Safety of the intended functionality (SOTIF) serves as one of the primary standards to assure the reliability and safety of the automated driving system. One of its key issues is the performance limitations of perception sensor systems. Generally, the reliability of the perception sensor system depends on the different scenarios of the driving environment. In this work, we investigate the sensor features and dependencies of the front camera and the top LiDAR of the nuTonomy scenes (nuScenes) dataset with respect to scenarios (e.g., rain and night) and distance information (e.g., two distance-based regions of interest). In addition, we apply the obtained parameters to a proven analytical reliability model to examine the impact of scenario and distance information on the reliability assessment.

Multi-sensor systems are the key components of automated driving functions. They enhance the quality of the driving experience and assisting in preventing traffic accidents. Due to the rapid evolution of sensor technologies, sensor data collection errors occur rarely. Nonetheless, according to Safety Of The Intended Functionality (SOTIF), an erroneous interpretation of the sensor data can also cause safety hazards. For example the front-camera may not understand the meaning of a traffic sign. Due to safety concerns it is essential to analyze the system reliability throughout the whole development process. In this work, we present an approach to explore the sensor’s features, such as the dependencies between successive sensor detection errors and the correlation between different sensors on the KITTI dataset quantitatively. Besides, we apply the learned parameters to a proven multi-sensor system model, which is based on Discrete-time Markov chains, to estimate the reliability of a hypothetical Stereo camera-LiDAR based sensor system.

Precise environment perception, which consists of multi-sensor systems, ensures the safety of the automated driving functions (ADFs). With the rapid evolution of sensor technologies, sensor data collection errors occur rarely. Nevertheless, accurate interpretation of the sensor data context, such as 3D multi-object tracking, is still full of challenges. Safety of the Intended Functionality (SOTIF) takes concern of the vulnerability of perception systems. The research of quantitative SOTIF analysis is still ongoing. In this paper, we propose a multi-sensor system model to observe both false negative and false positive errors in different field-of-views. Besides, we also extend a proven Markov-based approach, which takes dependencies between successive sensor errors and correlation between two dependent sensors into account, to model three correlated sensors sharing the same region of interest. In the end, we present a numerical example to illustrate the quantitative reliability analysis of a multi-sensor perception system according to SOTIF.