Attack-Aware Multi-Sensor Integration Algorithm for Autonomous Vehicle Navigation Systems
This addresses security vulnerabilities in autonomous vehicles, but it is incremental as it builds on existing sensor integration methods.
The paper tackles the problem of detecting cyberattacks in autonomous vehicle navigation systems by proposing a fault detection and isolation algorithm, resulting in quick detection and low false alarm rates as validated through simulation.
In this paper, we propose a fault detection and isolation based attack-aware multi-sensor integration algorithm for the detection of cyberattacks in autonomous vehicle navigation systems. The proposed algorithm uses an extended Kalman filter to construct robust residuals in the presence of noise, and then uses a parametric statistical tool to identify cyberattacks. The parametric statistical tool is based on the residuals constructed by the measurement history rather than one measurement at a time in the properties of discrete-time signals and dynamic systems. This approach allows the proposed multi-sensor integration algorithm to provide quick detection and low false alarm rates for applications in dynamic systems. An example of INS/GNSS integration of autonomous navigation systems is presented to validate the proposed algorithm by using a software-in-the-loop simulation.