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With the development of vehicles equipped with automated driving systems, the need for systematic evaluation of AV performance has grown increasingly imperative. According to ISO 34502, one of the safety test objectives is to learn the minimum performance levels required for diverse scenarios. To address this need, this paper combines two essential methodologies - scenario-based testing procedures and scoring systems - to systematically evaluate the behavioral competence of AVs. In this study, we conduct comprehensive testing across diverse scenarios within a simulator environment following Mcity AV Driver Licensing Test procedure. These scenarios span several common real-world driving situations, including BV Cut-in, BV Lane Departure into VUT Path from Opposite Direction, BV Left Turn Across VUT Path, and BV Right Turn into VUT Path scenarios.

Advanced Driver Assistance Systems (ADAS) is an essential aspect of the automotive technology in this era of technological revolution, where the goal is to make vehicles more convenient, safe, and energy efficient. Taking advantage of more degrees of freedom available within vehicle “energy management” allows more margin to maximize efficiency in the propulsion systems. It is envisioned by this research that future fuel economy regulations will consider the potential benefits of emerging connectivity and automation technologies of vehicle’s fuel consumption. The application focuses on reducing the energy consumption in vehicles by acquiring information about the road grade. Road elevation are obtained by use of Geographic Information System (GIS) maps in order to optimize the controller. The optimization is then reflected on the powertrain of the vehicle. The approach uses a Model Predictive Control (MPC) algorithm that allows the energy management strategy to leverage road grade.