Evaluation of Fuel Economy Benefits of Radar-Based Driver Assistance in Randomized Traffic

Abstract

Certain advanced driver assistance systems (ADAS) have the potential to boost energy efficiency in real-world scenarios. This article details a radar-based driver assistance scheme designed to minimize fuel consumption for a commercial vehicle by predictively optimizing braking and driving torque inputs while accommodating the driver’s demand. The workings of the proposed scheme are then assessed with a novel integration of the driver assistance functionality in randomized traffic microsimulation. Although standardized test procedures are intended to mimic urban and highway speed profiles for the purposes of evaluating fuel economy and emissions, they do not explicitly consider the interactions present in real-world driving between the ego vehicle equipped with ADAS and other vehicles in traffic. This article presents one approach to address the drawback of standardized test procedures for evaluating the fuel economy benefits of ADAS technologies. This approach is demonstrated by using a microsimulation of a traffic network into which the ego vehicle with the proposed driver assistance scheme is embedded for continuous interaction with the traffic. The analysis and results from stochastic simulations consider variations in the behavioral style of the driver of the ego vehicle and the traffic density. Strong variations, up to 10% in the fuel economy benefits, are observed between both variations presented in this study and what is obtained in typical deterministic evaluations mirroring standard test procedures.