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Objective This study investigated users’ subjective evaluation of three highly automated driving styles, in terms of comfort and naturalness, when negotiating a UK road in a high-fidelity, motion-based, driving simulator. Background Comfort and naturalness play an important role in contributing to users’ acceptance and trust of automated vehicles (AVs), although not much is understood about the types of driving style which are considered comfortable or natural. Method A driving simulator study, simulating roads with different road geometries and speed limits, was conducted. Twenty-four participants experienced three highly automated driving styles, two of which were recordings from human drivers, and the other was based on a machine learning (ML) algorithm, termed Defensive, Aggressive, and Turner, respectively. Participants evaluated comfort or naturalness of each driving style, for each road segment, and completed a Sensation Seeking questionnaire, which assessed their risk-taking propensity. Results Participants regarded both human-like driving styles as more comfortable and natural, compared with the less human-like, ML-based, driving controller. Particularly, between the two human-like controllers, the Defensive style was considered more comfortable, especially for the more challenging road environments. Differences in preference for controller by driver trait were also observed, with the Aggressive driving style evaluated as more natural by the high sensation seekers. Conclusion Participants were able to distinguish between human- and machine-like AV controllers. A range of psychological concepts must be considered for the subjective evaluation of controllers. Application Insights into how different driver groups evaluate automated vehicle controllers are important in designing more acceptable systems.

There is evidence that drivers’ behaviour adapts after using different advanced driving assistance systems. For instance, drivers’ headway during car-following reduces after using adaptive cruise control. However, little is known about whether, and how, drivers’ behaviour will change if they experience automated car-following, and how this is affected by engagement in non-driving-related tasks (NDRT). The aim of this driving simulator study, conducted as part of the H2020 L3Pilot project, was to address this topic. We also investigated the effect of the presence of a lead vehicle during the resumption of control, on subsequent manual driving behaviour. Thirty-two participants were divided into two experimental groups. During automated car-following, one group was engaged in an NDRT (SAE Level 3), while the other group was free to look around the road environment (SAE Level 2). Both groups were exposed to Long (1.5 s) and Short (.5 s) Time Headway (THW) conditions during automated car-following, and resumed control both with and without a lead vehicle. All post-automation manual drives were compared to a Baseline Manual Drive, which was recorded at the start of the experiment. Drivers in both groups significantly reduced their time headway in all post-automation drives, compared to a Baseline Manual Drive. There was a greater reduction in THW after drivers resumed control in the presence of a lead vehicle, and also after they had experienced a shorter THW during automated car-following. However, whether drivers were in L2 or L3 did not appear to influence the change in mean THW. Subjective feedback suggests that drivers appeared not to be aware of the changes to their driving behaviour, but preferred longer THWs in automation. Our results suggest that automated driving systems should adopt longer THWs in car-following situations, since drivers’ behavioural adaptation may lead to adoption of unsafe headways after resumption of control.

Bituminous mixes are generally designed with gradation and air void content ranges adopted by different agencies. The selected gradation and binder content are permitted to vary within specified to...

Rutting in asphalt mixes is a common type of distress in pavements with thick asphalt layers. It is more severe for pavements subjected to high pavement temperatures, slow moving, uphill in mountainous areas and heavy traffic loads. A number of binder rutting parameters have been proposed by different researchers for ranking/grading of the binders in terms of their rutting resistance. The suitability of different binder rutting parameters for explaining the rutting resistance of unmodified binders as well as modified binders has been a topic of research for several years. Considering that asphalt mixes in pavement layers are subjected to different conditions of temperature, loading time/frequency and stresses/strains, it is expected that these conditions will affect the way binder rutting resistance is judged. This study was conducted to examine the suitability of different binder rutting parameters for ranking of binders (unmodified as well as polymer and crumb rubber modified), distinguish modified binders from unmodified binders and for their ability to correlate with mix rutting. For this purpose, nine binders (four unmodified and five modified) were evaluated in oscillation and creep and recovery modes and various rutting parameters were examined. Oscillation tests were performed at varying frequencies and multiple stress creep recovery test was performed at different stress levels. The binder parameters were correlated with wheel tracking rut depths of bituminous concrete mix with NMAS of 19 mm. The frequency sensitivity of oscillation parameters and stress sensitivity of creep and recovery parameters were examined. Jnr and% recovery have been found to be stress sensitive whereas |G∗|, δ and |G∗|/sinδ were frequency sensitive. Some of the binder parameters have distinctly different ranges of values for unmodified and modified binders. The rankings produced by ZSV and Jnr closely matched the ranking of the mixes identified based on wheel tracking rut depths. While both oscillation and MSCR parameters correlated well with mix rutting for unmodified binder mixes, the correlation of oscillation parameters with mix rut depth was not good for modified binder mixes.

Rutting in bituminous layers is a major mode of distress, especially in pavements with thick bituminous layers. Different binder and mix rutting specifications are adopted by agencies worldwide to ...

Rutting potential of asphalts can be evaluated in the laboratory by different tests. Any visco-elastic parameter of the mix evaluated at a high pavement temperature can be considered to be a possib...

The aim of this study was to understand driver responses to “silent” failures in automated driving, where automation failed during a simulator drive, without a take-over warning. The effect of a visual non-driving related task (NDRT) and a road-based vigilance task presented drivers’ take-over response and visual attention was also investigated. Currently, automated driving systems face a number of limitations that require control to be handed back to the driver. Much of the research to date has focused on explicit take-over requests (ToRs) and shows that drivers struggle to resume control safely, exacerbated by disengagement from the driving task, for instance, due to the presence of NDRTs. However, little is known about whether, and how, drivers will respond to more subtle automation failures that come without a warning, and how this is affected by NDRT engagement. Thirty participants drove a simulated automated drive in two conditions, which had 6 silent automation failures each (3 on a Curve, 3 in a Straight), with no ToRs. In one condition, drivers were required to constantly monitor the road, which was enforced by a road-based vigilance task (VMS Only). In the other, drivers performed an additional visual NDRT, requiring them to divide their attention (VMS + Arrows). Results showed that, in both conditions, all drivers eventually detected and responded to all silent automation failures. However, engaging in an additional NDRT during automation resulted in significantly more lane excursions and longer take-over times. Adding a visual NDRT not only changed the distribution of drivers’ visual attention before and after the failure but also how they divided their attention between information on the road environment and the human–machine interface, which provided information on automation status. These results provide support for how driver monitoring systems may be used to detect drivers’ visual attention to the driving task and surroundings, and used as a tool for encouraging driver intervention, when required.

For accurate prediction of pavement performance, it is necessary that the effect of ageing on different mix parameters is accounted for. The short-term- and long-term-aged bitumen viscosities estimated using Global Ageing System (GAS) models are used in the Mechanistic Empirical Pavement Design Guide for the estimation of dynamic modulus. Comparison of the viscosities of the bitumens extracted from field- and laboratory-aged asphalt mixes with the viscosities predicted using the GAS model suggests over-prediction for high temperature and lower bitumen film thickness (FT) combinations and under-prediction for low temperatures and high film thicknesses. That the ageing codes used in the GAS model are not sensitive to FT and temperature also has been established in this study. The new statistical model proposed in this study based on fixed effect regression modelling approach for estimating the short-term-aged bitumen viscosity as a function of bitumen type, FT and temperature has been found to predict the s...