<p>This paper proposes an innovative approach for mitigating the effects of sensor attacks in autonomous vehicles by developing an adaptive event-triggered adaptive neural network control strategy. Firstly, to address the challenge of modeling difficulty for nonlinear systems, Dynamic Mode Decomposition is used to establish the linear model from data. Additionally, to effectively identify sensor attacks, the radial-basis-function-based neural network is utilized to approximate unknown attack signals. Subsequently, an adaptive controller is designed to counteract malicious sensor attack signals. The sufficient condition for the semi-globally uniformly ultimately bounded of the system are derived via apply the Lyapunov–Kraskovskii method. In the end, the feasibility of the proposed secure control approach is demonstrated via three simulation cases.</p>