Emergency Pull-Over Algorithm for Level 4 Autonomous Vehicles Based on Model-Free Adaptive Feedback Control With Sensitivity and Learning Approaches

Abstract

This paper presents an emergency pullover algorithm for fail-safe systems designed for level-4 autonomous vehicles. The proposed algorithm utilizes feedback gain adaptation, based on sensitivity estimation, and cost-based learning. Vehicle failure within this paper does not encompass every type of failure and refers only to any situation where the upper controller or communications from the upper controller shuts down. When this type of failure occurs, the algorithm performs an emergency pullover maneuver. This maneuver does not require any form of independent control from the driver to be performed successfully. However, the highest control priority is still given to the driver if the driver intervenes during the maneuver. The feedback gain adaptation is comprised of two sections: Sensitivity Estimation and Gradient Descent (GD) based Adaptation. For Sensitivity Estimation, a relationship function has been designed with feedback gain, from the feedback gain adaptation, and changes in state error. The sensitivity of state error with respect to feedback gain can then be estimated. This estimation is done through the Recursive Least Squares (RLS) method with multiple forgetting factors through the directional forgetting method. For GD based Adaptation, state errors are applied with parameters for the cost-based learning to give Adaptation Gains. These Adaptation Gains are used in tandem with the estimated sensitivity to update the feedback gain. To reduce the number of tuning parameters required in the GD method, an additional distance condition has been proposed. This condition utilizes feedback change rates and state errors, obtained from the multi-dimensional plane of the feedback gain’s change rates. A proportional coefficient is also required as a tuning parameter for this condition. This parameter is tuned by a cost-based learning algorithm, also designed in this study. Resultantly, these methods allow the adaptive feedback controller to forgo any system information such as mathematical models and system parameters. This indicates that the vehicle model is not expected to hinder performance. Hence, controllers that do not require system information are indeed a preferable algorithm for fail-safe modules. Performance evaluations for the controller has also been conducted with actual vehicle tests, under longitudinal and lateral autonomous driving scenarios.

Keywords

• Automated vehicle
• fail-safe
• autonomous driving
• sensitivity estimation
• cost-based learning
• recursive least squares