No abstract

This research was funded by Science and technology projects of State Grid Zhejiang Electric Power Co., Ltd. 2021ZK37 (Research on partition coordinated self-healing recovery technology of power system based on artificial intelligence).

Automated Lane Centering (ALC) systems are convenient and widely deployed today, but also highly security and safety critical. Recently, Dirty Road Patch (DRP) attack is proposed as a state-of-the-art adversarial attack against ALC systems. In this work, we report our recent progress of improving the DRP attack on attack deployability, attack stealthiness, and effectiveness on real vehicle. We also discuss future directions.

The conventional Kalman filter demands complete knowledge of the actual filter model, which is usually inaccessible in practical problems. When dealing with the filtering problem with uncertainty, any improper prediction of the uncertainty may degrade the filtering performance and even lead to divergence phenomenon. In this paper, the innovation feedback Kalman filter, which introduces the innovation feedback controller to the Kalman filter equations, is newly proposed on the basis of automatic control theory to address the filtering problem with uncertainty and is different from other filtering methods. By studying the estimate error equations, the estimate bias is extracted and its propagation mechanism is formulated. The estimate bias propagation equations reveal that eliminating the estimate bias essentially equivalents to an output regulation problem with uncertain exosystem. And a concise yet effective innovation feedback controller is subsequently given as an example. The proposed method is applied to one-dimension target tracking scenario and its high estimation accuracy performance and good stability are simulated through a comparative analysis with the ideal filtering results.

No abstract