Abstract
The paper presents a reconfigurable control strategy for the lateral stability of autonomous vehicles. The control strategy is based on the analysis of big data, which are provided by the sensor networks of autonomous vehicles. The core of the analysis method is a machine learning algorithm, with which the impacts of various vehicle signals on the lateral dynamics have been examined. In the analysis several scenarios with faults in the steering and in-wheel systems are considered using a high-fidelity simulation software. The results of the examination are built into the fault-tolerant reconfiguration strategy.
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