Investigation of older driver's takeover performance in highly automated vehicles in adverse weather conditions

Abstract

Driving is important for older people to maintain mobility. To reduce age-related functional decline, older drivers may adjust their driving by avoiding difficult situations. One of these situations is driving in adverse weather conditions such as in the rain, snow and fog which reduce the visual clarity of the road ahead. The upcoming highly automated vehicle (HAV) has the potential of supporting older people. However, only limited work has been done to study older drivers' interaction with HAV, especially in adverse weather conditions. This study investigates the effect of age and weather on takeover control performance among drivers from HAV. A driving simulation study with 76 drivers has been implemented. The participants took over the vehicle control from HAV under four weather conditions clear weather, rain, snow and fog, where the time and quality of the takeover control are quantified and measured. Results show age did affect the takeover time (TOT) and quality. Moreover, adverse weather conditions, especially snow and fog, lead to a longer TOT and worst takeover quality. The results highlighted that a user-centred design of human-machine interaction would have the potential to facilitate a safe interaction with HAV under the adverse weather for older drivers.