Interval-based/Data-Driven Risk Management for Intelligent Vehicles: Application to an Adaptive Cruise Control System

Abstract

In this work, a novel interval-based/data-driven safety verification technique is introduced for Intelligent/Autonomous Vehicles (I/AV). The interval arithmetic is adopted to enhance the reliability of the analytical models used for the autonomous navigation. Furthermore, a data-driven technique, which monitors the correlation relating variables of the modeled system, is adopted to ameliorate the uncertainty assessment. In such a manner, tight bounds of safety margins are obtained. To provide reliable safety verification, the proposed risk management approach has been integrated on an Adaptive Cruise Control (ACC) system. It permits to detect erroneous uncertainty estimation of an Extended Kalman Filter (EKF). Simulation results prove the overall risk management efficiency and its ability to handle uncertainties.