Functionality Resilience of Autonomous Vehicles: Case Studies of Lateral Control and Positioning

Autonomous vehicles (AVs) face functionality degradation and safety risks in complex dynamic environments. Such risks arise from uncertainties, including the coupling of internal subsystems, changes in the external environment, and interactions among multiple traffic participants. To address this problem, a functionality-centered resilience framework together with its characterization, quantification, and enhancement methods is proposed. Functionality resilience is formalized as a triplet (A, F, U) consisting of an adverse event (A), a functionality (F), and uncertainty (U). Based on the geometric characteristics of the performance curve Q(t), functionality resilience is evaluated from both global and local dimensions. The global resilience loss R_L quantifies the functionality loss during adverse events, while the local geometric features of the performance curve characterize the absorption and recovery behavior of the system. Resilience enhancement principles centered on reducing epistemic uncertainty, optimizing the functionality level, and introducing performance feedback are summarized. In the vehicle lateral control case, the functionality degradation of a pure pursuit controller in sharp-curve scenarios is addressed. An adaptive look-ahead distance method that fuses real-time vehicle speed and path curvature is designed, and a closed-loop correction consisting of proportional-integral-moving-average-derivative feedback of the lateral error is introduced. Vehicle tests and simulation experiments show that, for the proposed strategy, the resilience loss R_L at two successive sharp curves decreases from 63.93 to 49.50 and from 61.50 to 45.58. The absolute value of the performance drop L₁ decreases from 6.73 to 5.15 and from 5.83 to 3.94. The recovery speed k₂ increases from 0.530 to 0.683 and from 0.565 to 0.864. In the vehicle positioning case, a position"prediction-correction"network is developed. Global navigation satellite system (GNSS) positioning errors are used as supervisory information to train the correction network, thereby improving the positioning functionality resilience of the integrated navigation system during GNSS outages. Under three GNSS outage scenarios, the positioning functionality resilience of the prediction-correction strategy is superior to that of dead reckoning and the " $p-\Delta p$ " architecture. The resilience loss R_L at the second outage decreases from 420.13 for dead reckoning to 141.41. The results indicate that the (A, F, U) framework can effectively characterize, quantify, and enhance the functionality resilience of AVs. Reducing epistemic uncertainty and optimizing the functionality level are effective ways to improve functionality resilience.