Automated Vehicle System Architecture with Performance Assessment

This paper proposes a reference architecture to increase reliability and robustness of an automated vehicle. The architecture exploits the benefits arising from the interdependencies of the system and provides self awareness. Performance Assessment units attached to subsystems quantify the reliability of their operation and return performance values. The Environment Condition Assessment, which is another important novelty of the architecture, informs augmented sensors on current sensing conditions. Utilizing environment conditions and performance values for subsequent centralized integrity checks allow algorithms to adapt to current driving conditions and thereby to increase their robustness. We demonstrate the benefit of the approach with the example of false positive object detection and tracking, where the detection of a ghost object is resolved in centralized performance assessment using a Bayesian network.