How speed and visibility influence preferred headway distances in highly automated driving

Abstract

While the introduction of highly automated vehicles promises lower accident numbers, a main requirement for wide use of these vehicles will be the acceptance by drivers. In this study a crucial variable for the acceptance of highly automated vehicles, the vehicle to vehicle distance expressed in time headway, was researched in a driving simulator. Research has shown that time headway distances, perceived as comfortable in self-driving and assisted driving with adaptive cruise control, remain constant over a range of different speeds. This study aims to test these findings for highly automated driving. Since time headway is perceived visually, the driving situation was varied to investigate the influence of visibility on the subjective comfort of the driver in a highly automated driving situation. In a withinsubject design, drivers followed a passenger car in clear weather conditions, the same passenger car in fog which occluded parts of the traffic environment, as well as a truck that occluded the lane ahead, also in clear weather condition. Subjective comfort of drivers in each condition was rated with a haptic rating lever. Results suggest that comfortable time headway following distances in highly automated driving are not constant over different speeds, but that these distances decrease with increasing speed. Reduced visibility generally led to a shift in comfortable following distances towards larger headways. These results have implications for the introduction of highly automated vehicles and their time headway adjustments, which will need to be adaptive to speed and visibility in the road environment.

Fleet Management for HDVs and CAVs on Highway in Dense Fog Environment

“…It is used to describe the characteristic of the driver in dense fog environment as to follow the AGV within the scope of visibility and keep safety distance from the AGV. e simulation results show that the phenomenon shown in the simulation is consistent with the assumptions of HDV driver characteristics and fall into the relationship of speed and visibility as studied by Siebert and Wallis [45]. irdly, the distributed serial model predictive control (DMPC) model is used to control the fleet in dense fog environment.…”

“…From the phenomena shown in Figures 4 and 5, they are consistent with the assumptions of HDV driver characteristics discussed above and falls into the relationship of speed and visibility as studied by Siebert and Wallis [45]. erefore, the proposed car-following model above can effectively describe the driver behavior within dense fog environment in the fleet control.…”

“…Whether the HDV car-following model meets the driving characteristics of drivers in dense fog weather conditions is the first most important factor in managing and controlling the fleet mixed with HDVs and CAVs efficiently and precisely. Siebert and Wallis [45] had studied "how speed and visibility influence preferred headway distances in highly automated driving." Based on the study of Siebert and Wallis on the relationship between visibility and speed, the proposed car-following model is verified in three parameters groups.…”

The determinants of acceptability and behavioural intention of automated vehicles – a review

“…Few studies deal with driving parameters, which are mostly conducted on a simulator. However, the parameters evaluated were varied: driving style (Techer et al, 2019;Hartwich et al, 2018;Mühl et al, 2019;Oliveira et al, 2019), driving behaviour (time headway, lateral offset, quality of driving; Siebert & Wallis, 2019;Voß et al, 2018;Walker et al, 2019). These studies highlighted the crucial role of the conditions such as road type, time pressure, traffic volume, visibility, weather, other vehicle behaviour, presence of explanations or side of the seat in the evaluation (Frison et al, 2019;Wintersberger et al, 2019), indicating the need to multiply studies to investigate combinations of conditions.…”

Adjustable automation and manoeuvre control in automated driving

“…We register drivers subjective experience through three items of the disco-scale [18], assessing perceived control, ability to intervene, and perceived responsibility for potential accidents. We further investigate driver behaviour, by comparing headways assumed in manual driving to headways assumed under manoeuvre control driving conditions, building on research that has identified a high inter-individual variance in time headways [19,20] and a need for adjustable headways in highly automated driving [21]. Parts of this study have been reported in a previous paper [22].…”