<div>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.</div>
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