Control
Marziyeh Barootkar
Abstract
Background and Objectives: While intelligent vehicle teleoperation systems prioritize operational performance, their vulnerability to cyber-physical attacks—such as sensor spoofing and latency exploitation—remains a critical unsolved challenge. Existing solutions predominantly focus on attack ...
Read More
Background and Objectives: While intelligent vehicle teleoperation systems prioritize operational performance, their vulnerability to cyber-physical attacks—such as sensor spoofing and latency exploitation—remains a critical unsolved challenge. Existing solutions predominantly focus on attack prevention, leaving systems defenseless during active attacks that threaten stability and collision avoidance. This study addresses the unmet need for real-time resilience by introducing an adaptive control framework that dynamically mitigates attack-induced disruptions without relying on predefined vehicle models. Methods: We propose a novel adaptive LQR-based optimal controller that compensates for multi-vector attacks (e.g., false data injection, GPS spoofing) by estimating disturbed signals in real time. Unlike static models, our data-driven approach eliminates dependency on fixed dynamics. A rigorous case study evaluates performance under simultaneous command injection and DoS attacks, measuring trajectory deviation and recovery time. Results: The framework achieves ≤12% trajectory deviation (35% improvement over benchmarks) and 40% faster recovery from destabilizing attacks. It outperforms conventional controllers by adapting to model uncertainties and multi-vector threats without prior knowledge of system parameters. Conclusion: This work pioneers a model-agnostic, real-time resilience paradigm for teleoperated vehicles, merging human oversight with autonomous adaptability. Beyond immediate safety gains, it underscores the necessity of embedding cybersecurity-aware control mechanisms in connected vehicles, shifting from passive prevention to active threat mitigation.
