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Car-following is an important driving behaviour for intelligent vehicles and has a significant impact on traffic efficiency and traffic safety. Car-following models are widely developed to characterize the human-drivers car-following manoeuvre actions and adopted in traffic simulation and automated vehicle control system development. Car-following models need to be able to represent the drivers behaviour while following preceding vehicles. On the other hand, car-following controllers are an important component of intelligent vehicle systems, both for autonomous vehicles and connected vehicles. However, Adaptive Cruise Control (ACC) as well as Cooperative Adaptive Cruise Control (CACC) do not include human behaviour, which makes their car-following behaviour not human-like or natural for the on-board driver or passenger. To address this problem, in this study, the human-like Wiedemann car-following model is calibrated and verified with our driving simulator data. A human-like car-following nonlinear model predictive control (MPC) controller is developed based on the calibrated car-following model. Three different scenarios are tested to evaluate the performance of the proposed controller, with which the autonomous vehicle is able to have human-like and smooth trajectories at different phases and within different transition zones.

The operational capabilities of automated driving features are limited and sometimes require drivers’ intervention through a transition of control. Assistance at an operational level might be extremely beneficial during transitions but the literature lacks evidence on the topic. A simulator study was conducted to investigate the potential impacts that lateral assistance systems might have while the Automated Driving System (ADS) hands back control to the driver. Results showed that drivers benefitted from a strong Lane Keeping Assist during the first phase of the transfer, helping them to keep the lane centre. However, assisting the drivers at an operational level did not enhance their capability of addressing a more complex task, presented as a lane change. In fact, it was more task-specific assistance (Blind-spot assist) that allowed drivers to better cope with the tactical decision that the lane change required. Moreover, longer exposure to lane-keeping assist systems helped them in gaining awareness of the surrounding traffic and improved the way drivers interacted with the Blind-spot assist.

Automated driving needs to be comfortable to encourage the broad acceptance and usage of automated vehicles (AVs). However, current research provides limited knowledge on the descriptions and influencing factors of user comfort in automated driving, especially from the perspective of an AV’s driving styles. This paper presents results from an online workshop, in which nine experts with hands-on experience of AVs, and a long track record of research in this context, brainstormed and discussed a series of topics related to user comfort in automated driving. Results showed that a wide range of terms were used to describe user comfort and discomfort, when the attendees considered being driven by either currently available transport modes (e.g., taxi/bus/train) or higher-level AVs (with neither requiring control of the vehicle by the user). All of the terms used for existing vehicles were found to apply to AVs, but additional aspects of comfort/discomfort were revealed for AVs. This suggests that what makes existing transport modes comfortable is not the same as that for higher-level AVs. Regarding the relationship between comfort and discomfort in automated driving, seven and ten categories of terms were identified for comfort and discomfort, respectively, with both the number and the nature of these categories differing across the two states. This suggests that, to ensure user comfort in automated driving, additional factors that affect comfort must be considered, rather than solely mitigating discomfort. By integrating these multiple aspects that affect user comfort, knowledge from the workshop was used to develop a conceptual framework, to explain how AV driving styles, as well as other, non-driving-related factors, affect user comfort, from both a physical and psychological perspective. This work provides an overview of experts’ insights on the factors that affect comfort in automated driving, and will facilitate a more comprehensive definition, and more accurate measurement, of user comfort, facilitating the design of more comfortable and acceptable automated driving for future vehicles.

Achieving optimal performance in human-machine systems, such as highly automated vehicles, relies, in part, on individuals’ acceptance and use of the system, which is in turn affected by their enjoyment of engaging with, or experiencing, the system. This driving simulator study investigated individuals’ real-time subjective evaluation of four different Automated Vehicle (AV) driving styles, in different environmental contexts. Twenty-four participants were recruited to manually drive a contextually rich simulator environment, and to experience human-like and non-human-like AV driving styles, as well as the automated replay of their own manual drive. Their subjective real-time feedback towards these driving styles was analyzed. Our results showed that participants gave higher positive feedback towards the replay of their own drive, compared to the other three controllers. This difference was statistically significant, when compared to the high-speed controller (named as Fast), particularly for sharp curves. With respect to the replay of their own drive, participants gave higher negative feedback when navigating an Urban environment, compared to Rural settings. Moreover, changes in roadside furniture affected individuals’ feedback, and this effect was more prominent when the vehicle was driving closer to the edge of the road. Based on our results, we conclude that individuals’ perception of different AV driving styles changes based on different environmental conditions, including, but not limited to, road geometry and roadside furniture. These findings suggest that humans prefer a slower human-like driving style for AV controllers that adapts its speed and lateral offset to roadside objects and furniture. Investigating individual differences in AV driving style preference showed that low Sensation Seeking individuals preferred the slower human-like controller more than the faster human-like controller. Consideration of this human-centered feedback is important for the design of future AV controllers, to enhance individuals’ ride experience, and potentially improve acceptance and use of these vehicles.

Accidents involving pedestrians are particularly common at unsignalised intersections and mid-block crosswalks, where vehicles often do not yield to them. Analysing and understanding pedestrian crossing behaviour at such locations is vital for improving road safety. Previous studies have repeatedly shown that pedestrians tend to accept smaller time gaps in conditions with higher vehicle speeds and thus potentially less safe. This has prompted the hypothesis that pedestrians rely on spatial distance to make crossing decisions. However, few studies have investigated the mechanism underpinning this phenomenon. We propose a novel approach to characterise pedestrian crossing behaviour: a psychophysics-based gap acceptance (PGA) model based on visual looming cues and binary choice logit method. Road crossing data collected in a simulated experiment were used to analyse pedestrian behaviour and test the model. Our analysis indicates that, in line with previous studies, higher vehicle speed increased the tendency of gap acceptance, leading to a higher rate of unsafe crossings. Crucially, the PGA model could accurately account for these crossing decisions across experimental scenarios, more parsimoniously than a conventional model. These results explain the speed-induced unsafe behaviour by suggesting that pedestrians apply visual looming, which depends on vehicle speed and distance, to make crossing decisions. This study reinforces the notion that for two vehicles with the same time gap, the one with higher speed can elicit more risky crossing behaviour from pedestrians, potentially resulting in more severe accidents. The practical implications of the results for traffic safety management, modelling and development of automated vehicles are discussed.

A commonly used metric for measuring college costs, drawn from data in the Integrated Postsecondary Education Data System (IPEDS), is expenditure per full-time equivalent (FTE) student. This article discusses an error in this per FTE calculation when using IPEDS data, especially with regard to community colleges. The problem is that expenditures for noncredit courses are reported to IPEDS but enrollments are not. This exclusion inflates any per FTE student figure calculated from IPEDS, in particular expenditures and revenues. A 2021 IPEDS Technical Review Panel (TRP #62) acknowledged this problem and moved campus institutional research offices a step closer to reporting noncredit enrollment data (RTI International, 2021). This article is the first to provide some numbers on the magnitude of this problem. It covers eight states—California, Iowa, New Jersey, New York, North Carolina, South Carolina, Tennessee, and Virginia. Data on noncredit community college enrollments were made available from system offices in all states. In addition, discussions were held at both the system level and the campus level to verify the data and assumptions. Figures provided by states were merged with existing IPEDS data at the campus and state levels, and then were adjusted to account for noncredit enrollments. The results provide evidence that calculations using IPEDS data alone overestimate the resources that community colleges have to spend on each student, although distortions vary greatly between states and among colleges in the same state. The results have important implications for research studies and college benchmarking.

This distributed simulator study investigated pedestrians’ head-turning behaviour during a series of road crossings in a CAVE-based pedestrian simulator. Pedestrians were required to cross the road in front of an approaching vehicle, the kinematic behaviour of which was either programmed by the simulation to depict an automated vehicle (AV) or controlled by a human driver (HD), via a connected (hidden) desktop driving simulator. A within-participant experimental design was used with twenty-five pairs of participants (a pedestrian and a driver). For each trial, pedestrians had to decide whether to cross in front of the HD/AV, which was instructed (or programmed) to yield (or not) to the pedestrian. For the AV trials, two braking patterns were included: a hard-braking AV (AVHB, deceleration rate = 3.2 m/s2, stopping distance = 12 m from pedestrian) and soft-braking (AVSB, deceleration rate = 2.5 m/s2, stopping distance = 4 m from pedestrian). Pedestrians’ head-turning frequency and the change in head-turning angle, were calculated for each condition, both before a crossing was initiated, and during the actual road crossing. Results showed a significant increase in head-turning behaviour in the last 2 seconds before a crossing initiation in the yielding trials, in line with a ‘last-second check’ reported in observations of real-world crossings (Hassan, Geruschat, & Turano, 2005). The vehicle’s braking behaviour and stopping distance were the most important factors affecting pedestrians’ head-turning patterns during the crossing, with the least head-turning behaviour seen in the AVSB condition, compared with AVHB and HDB trials. This suggests that a closer stopping distance for the AV was associated with less confusion for the pedestrian, although this condition was also associated with the longest crossing initiation time. In contrast, the highest number of head-turnings were seen for the human-driven vehicle, which, on average, yielded about 40 m away from the participants, enabling a much faster crossing initiation. Overall, the shortest crossing initiation time (~ 1 sec) and highest head-turning behaviour were seen in the non-braking conditions, where participants crossed as quickly as the circumstances allowed. These results provide new insights about the use of VR simulators for understanding pedestrians’ crossing behaviour in response to different vehicle kinematics. They also extend our knowledge of pedestrian cues for the development of suitable sensors in future automated vehicles, which should help with providing a more seamless interaction between AVs and other road users in mixed traffic settings.

This driving simulator study investigated the effect of inconsistent steering guidance during system and user-initiated transitions from Highly Automated Driving (HAD). In particular, the aim of the study was to understand if steering conflicts could be achieved by adopting inconsistent steering guidance and whether these conflicts could be exploited to accelerate drivers' steering engagement within a limited time. Inconsistent steering guidance was generated by switching the guidance on and off at 3 different frequencies (0.1, 0.2 and 0.3 Hz). Results revealed that steering engagement has more to do with the initiation rather than the quality of the steering guidance. In fact, drivers were more engaged with the steering task when they initiated the transition themselves. Compared to system-initiated transitions, in user-initiated ones, drivers exerted stronger steering inputs throughout the transition, which allowed them to maintain larger Time To Lane Crossing (TTLC) values with fewer steering corrections. During system-initiated transitions, drivers started to actively engage with the steering activity only after more than 5 s from the start of the transition but were able to achieve a steering behaviour close to the one shown during user-initiated transitions at 10 s.

There is quickly growing literature on machine-learned models that predict human driving trajectories in road traffic. These models focus their learning on low-dimensional error metrics, for example average distance between model-generated and observed trajectories. Such metrics permit relative comparison of models, but do not provide clearly interpretable information on how close to human behavior the models actually come, for example in terms of higher-level behavior phenomena that are known to be present in human driving. We study highway driving as an example scenario, and introduce metrics to quantitatively demonstrate the presence, in a naturalistic dataset, of two familiar behavioral phenomena: (1) The kinematics-dependent contest, between on-highway and on-ramp vehicles, of who passes the merging point first. (2) Courtesy lane changes away from the outermost lane, to leave space for a merging vehicle. Applying the exact same metrics to the output of a state-of-the-art machine-learned model, we show that the model is capable of reproducing the former phenomenon, but not the latter. We argue that this type of behavioral analysis provides information that is not available from conventional model-fitting metrics, and that it may be useful to analyze (and possibly fit) models also based on these types of behavioral criteria., This paper has been submitted to 24th IEEE International Conference on Intelligent Transportation - ITSC2021, September 19-22, 2021 Indianapolis, IN, United States

Driving simulators can be used to test vehicle designs earlier, prior to building physical prototypes. One area of particular interest is winter testing since testing is limited to specific times of year and specific regions in the world. To ensure that the simulator is fit for purpose, an objective assessment is required. In this study a simulator and real world comparison was performed with three simulator configurations (standard, no steering torque, no motion) to assess the ability of a utility triplet of analyses to be able to quantify the differences between the real world and the different simulator configurations. The results suggest that the utility triplet is effective in measuring the differences in simulator configurations and that the developed “Virtual Sweden” environment achieved rather good behavioural fidelity in the sense of preserving absolute levels of many measures of behaviour. The main limitation in the simulated environment seemed to be the poor match of the dynamic lateral friction limit on snow and ice when compared to the real world.