Enhancing Robustness Against Component Failures in Intelligent Transportation Systems Through Self-diagnosis Functionality

The intelligent transportation systems (ITS) are part of possible solutions to the problems in transportation. Current systems generate digital twins of traffic participants. The traffic can be interpreted, and control signals can be sent to vehicles. Malfunctions could have disastrous consequences. Therefore, we present a self-diagnosis functionality for ITS which enhancing robustness against component failures. First, we identified sources of failures. Then, we compared existing failure detection approaches in use case of ITS. Based on this, we developed the methods Heartbeat, Sensor Metadata Checking, Process Pipeline Checking and Measurement Point Cross-Checking. To react to malfunctions, we introduce a remediation component. For testing, we used the real environment test bed Providentia++. The unique setup enables novel approaches for enhancement of robustness. In particular, Measurement Point Cross-Checking was tailored to our unique sensor setup. During the experiments, we verified the effectiveness of our methods. In future work, we suggest more plausibility checks.     «

The intelligent transportation systems (ITS) are part of possible solutions to the problems in transportation. Current systems generate digital twins of traffic participants. The traffic can be interpreted, and control signals can be sent to vehicles. Malfunctions could have disastrous consequences. Therefore, we present a self-diagnosis functionality for ITS which enhancing robustness against component failures. First, we identified sources of failures. Then, we compared existing failure detect...     »