There have been numerous studies attempting to overcome the limitations of current autonomous driving technologies. However, there is no doubt that it is challenging to promise integrity of safety regarding urban driving scenarios and dynamic driving environments. Among the reported countermeasures to supplement the uncertain behavior of autonomous vehicles, teleoperation of the vehicle has been introduced to deal with the disengagement of autonomous driving. However, teleoperation can lead the vehicle to unforeseen and hazardous situations from the viewpoint of wireless communication stability. In particular, communication delay outliers that severely deviate from the passive communication delay should be highlighted because they could hamper the cognition of the circumstances monitored by the teleoperator, or the control signal could be contaminated regardless of the teleoperator’s intention. In this study, communication delay outliers were detected and classified based on the stochastic approach (passive delays and outliers were estimated as 98.67% and 1.33%, respectively). Results indicate that communication delay outliers can be automatically detected, independently of the real-time quality of wireless communication stability. Moreover, the proposed framework demonstrates resilience against outliers, thereby mitigating potential performance degradation.

Functional safety in a four-wheel steering system is critical for smart vehicles, especially under steer-by-wire configurations where conventional methods fail to achieve 100% safety. In this study, we developed a hybrid AI-based fail-safe control framework that combines long short-term memory and convolution neural network classifiers for real-time fault detection, prediction, and safe state decision-making. The developed system was trained using steering sensor data, incorporating accurate response modeling and fault induction mechanisms. An LSTM model predicts deviations in steering behavior, while a random forest (RF) classifier identifies fault types and initiates safe state transitions in accordance with ISO 26262 functional safety guidelines. The system includes a Texas Instruments TMS320F28377D microcontroller and a rear steering actuator with a permanent magnet synchronous motor, utilizing controller area network communication for diagnostics and actuation. The system presents the potential of machine learning techniques to enhance the fail-safe capabilities of next-generation steer-by-wire systems.