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Hybrid and electric vehicles (HVs and EVs) have demonstrated low noise levels relative to their Internal Combustion Engine (ICE) counterparts, particularly at low speeds. As the number of HVs/EVs on the road increases, so does the need for data quantifying auditory detectability by pedestrians; in particular, those who are vision impaired. Manufacturers have started implementing additive noise solutions designed to increase vehicle detectability while in electric mode and/or when traveling below a certain speed. A detailed description of the real-time acoustic measurement system, the corresponding vehicular data, development of an immersive noise field, and experimental methods pertaining to a recent evaluation of candidate vehicles is provided herein. Listener testing was completed by 24 legally blind test subjects for four vehicle types: an EV and HV with different additive noise approaches, an EV with no additive noise, and a traditional ICE vehicle.

Partially automated driving involves the relinquishment of longitudinal and/or latitudinal control to the vehicle. Partially automated systems, however, are fallible and require driver oversight to avoid all road hazards. Researchers have expressed concern that automation promotes extended eyes-off-road (EOR) behavior that may lead to a loss of situational awareness (SA), degrading a driver’s ability to detect hazards and make necessary overrides. A potential countermeasure to visual inattention is the orientation of the driver’s glances towards potential hazards via cuing. This method is based on the assumption that drivers are able to rapidly identify hazards once their attention is drawn to the area of interest regardless of preceding EOR duration. This work examined this assumption in a simulated automated driving context by projecting hazardous and nonhazardous road scenes to a participant while sitting in a stationary vehicle.