The influence of time headway on subjective driver states in adaptive cruise control

There is no agreement on the relation between driving parameters and drivers’ subjective states. A linear as well as a threshold relationship for different subjective variables and driving parameters has been put forward. In this study we investigate the relationship between time headway and the ratings of risk, task difficulty, effort, and comfort. Knowledge about this interrelation may advance the development of adaptive cruise control and autonomous driving and can add to the discussion about driver behavior models. An earlier study (Lewis-Evans, De Waard, & Brookhuis, 2010) found a threshold effect for drivers’ ratings of subjective variables for time headways between 0.5 and 4.0 s at a speed of 50 km/h. This study aims to replicate the threshold effect and to expand the findings to time headways at different speeds. A new measure for criticality was added as a categorical variable, indicating the controllability of a driving situation to give indications for the appliance of time headway in adaptive cruise control systems. Participants drove 24 short routes in a driving simulator with predefined speed and time headway to a leading vehicle. Time headway was varied eightfold (0.5–4 s in 0.5 s increments) and speed was varied threefold (50, 100, 150 km/h). A threshold effect for the ratings of risk, task difficulty, effort, and comfort was found for all three different speeds. Criticality proved to be a useful variable in assessing the preferred time headway of drivers

Introducing a multivariate model for predicting driving performance: The role of driving anger and personal characteristics

Introduction: Maladaptive driving is an important source of self-inflicted accidents and this driving style could include high speeds, speeding violations, and poor lateral control of the vehicle. The literature suggests that certain groups of drivers, such as novice drivers, males, highly motivated drivers, and those who frequently experience anger in traffic, tend to exhibit more maladaptive driving patterns compared to other drivers. Remarkably, no coherent framework is currently available to describe the relationships and distinct influences of these factors. Method: We conducted two studies with the aim of creating a multivariate model that combines the aforementioned factors, describes their relationships, and predicts driving performance more precisely. The studies employed different techniques to elicit emotion and different tracks designed to explore the driving behaviors of participants in potentially anger-provoking situations. Study 1 induced emotions with short film clips. Study 2 confronted the participants with potentially anger-inducing traffic situations during the simulated drive. Results: In both studies, participants who experienced high levels of anger drove faster and exhibited greater longitudinal and lateral acceleration. Furthermore, multiple linear regressions and path-models revealed that highly motivated male drivers displayed the same behavior independent of their emotional state. The results indicate that anger and specific risk characteristics lead to maladaptive changes in important driving parameters and that drivers with these specific risk factors are prone to experience more anger while driving, which further worsens their driving performance. Driver trainings and anger management courses will profit from these findings because they help to improve the validity of assessments of anger related driving behavior

Design and Field Test of a Mobile Augmented Reality Human-Machine Interface for Virtual Stops in Shared Automated Mobility On-demand

Shared automated mobility on-demand (SAMOD) is considered as promising mobility solution in the future. Users book trips on-demand via smartphone and service algorithms set up virtual stops (vStop) where users then need to walk to board the automated shuttle. Navigation and identification of the virtual pick-up location, which has no references in the real world, can be challenging. Providing users with an intuitive information system in that situation is essential to achieve high user acceptance of new automated mobility services. Our novel vStop human-machine-interface (HMI) prototype for mobile augmented reality (AR) supports users with information in reference to the street environment. This work firstly presents results of an online interview study (N = 21) to conceptualize an HMI. Secondly, the HMI was prototyped with means of AR and evaluated (N = 45) regarding user experience (UX), workload and acceptance. Results show that the AR-prototype provided high rates of UX especially in terms of high pragmatic quality. Furthermore, cognitive workload when using the HMI was low and acceptance ratings were high. Results show the positive perception of AR for navigation tasks in general and the highly assistive character of the vStop prototype in particular. In the future SAMOD services could provide customers with vStop HMIs to foster user acceptance and smooth operation of their service

The exact determination of subjective risk and comfort thresholds in car following

In this study the location of vehicle to vehicle distance thresholds for self-reported subjective risk and comfort was researched. Participants were presented with ascending and descending time headway sequences in a driving simulator. This so called method of limits of ascending and descending stimuli (Gouy, Diels, Reed, Stevens, & Burnett, 2012) was refined to efficiently determine individual thresholds for stable time headways with a granularity of 0.1 seconds. Time headway thresholds were researched for 50, 100, and 150 km/h in a city, rural, and highway setting. Furthermore, thresholds for self-driving (level 0 automation: NHTSA, 2013) were compared with thresholds for the experience of subjective risk and comfort in assisted driving, similar to adaptive cruise control (level 1 automation). Results show that preferred individual time headways vary between subjects. Within subjects however, time headway thresholds do not significantly differ for different speeds. Furthermore we found that there was no significant difference between time headways of self-driving and distance-assisted driving. The relevance of these findings for the development of adaptive cruise control systems, autonomous driving and driver behavior modelling is discussed

Why drivers are frustrated: results from a diary study and focus groups.

Designing emotion-aware systems has become a manageable aim through recent developments in computer vision and machine learning. In the context of driver behaviour, especially negative emotions like frustration have shifted into the focus of major car manufacturers. Recognition and mitigation of the same could lead to safer roads in manual and more comfort in automated driving. While frustration recognition and also general mitigation methods have been previously researched, the knowledge of reasons for frustration is necessary to offer targeted solutions for frustration mitigation. However, up to the present day, systematic investigations about reasons for frustration behind the wheel are lacking. Therefore, in this work a combination of diary study and user focus groups was employed to shed light on reasons why humans become frustrated during driving. In addition, participants of the focus groups were asked for their usual coping methods with frustrating situations. It was revealed that the main reasons for frustration in driving are related to traffic, in-car reasons, self-inflicted causes, and weather. Coping strategies that drivers use in everyday life include cursing, distraction by media and thinking about something else, amongst others. This knowledge will help to design a frustration-aware system that monitors the driver’s environment according to the spectrum of frustration causes found in the research presented here

How to Communicate with Pedestrians: Exploration of the Interplay of Dynamic HMI and External HMI for Different Sized Automated Vehicles

This study deals with the interplay of an external HMI to transmit explicit communication signals via 360° LED light-band and a dynamic HMI to transmit implicit communication signals via vehicle dynamics. Previous results on the interplay of dHMI and eHMI indicated that pedestrians tended to rely on the explicit communication via eHMI to indicate their willingness to cross. Nevertheless, this was investigated in a traffic scenario in which the automated vehicles approached from the left-hand side and the traffic rule “right before left” might have affected pedestrian's willingness. To rule this out, pre-recorded video sequences were shown in which the automated vehicles approached from the right-hand side. This study is work-in-progress and the data-collection is still ongoing. Results and further implications for future studies will be discussed

Toward a Holistic Communication Approach to an Automated Vehicle's Communication With Pedestrians: Combining Vehicle Kinematics With External Human-Machine Interfaces for Differently Sized Automated Vehicles

Future automated vehicles (AVs) of different sizes will share the same space with other road users, e. g., pedestrians. For a safe interaction, successful communication needs to be ensured, in particular, with vulnerable road users, such as pedestrians. Two possible communication means exist for AVs: vehicle kinematics for implicit communication and external human-machine interfaces (eHMIs) for explicit communication. However, the exact interplay is not sufficiently studied yet for pedestrians' interactions with AVs. Additionally, very few other studies focused on the interplay of vehicle kinematics and eHMI for pedestrians' interaction with differently sized AVs, although the precise coordination is decisive to support the communication with pedestrians. Therefore, this study focused on how the interplay of vehicle kinematics and eHMI affects pedestrians' willingness to cross, trust and perceived safety for the interaction with two differently sized AVs (smaller AV vs. larger AV). In this experimental online study (N = 149), the participants interacted with the AVs in a shared space. Both AVs were equipped with a 360° LED light-band eHMI attached to the outer vehicle body. Three eHMI statuses (no eHMI, static eHMI, and dynamic eHMI) were displayed. The vehicle kinematics were varied at two levels (non-yielding vs. yielding). Moreover, “non-matching” conditions were included for both AVs in which the dynamic eHMI falsely communicated a yielding intent although the vehicle did not yield. Overall, results showed that pedestrians' willingness to cross was significantly higher for the smaller AV compared to the larger AV. Regarding the interplay of vehicle kinematics and eHMI, results indicated that a dynamic eHMI increased pedestrians' perceived safety when the vehicle yielded. When the vehicle did not yield, pedestrians' perceived safety still increased for the dynamic eHMI compared to the static eHMI and no eHMI. The findings of this study demonstrated possible negative effects of eHMIs when they did not match the vehicle kinematics. Further implications for a holistic communication strategy for differently sized AVs will be discussed

Inequality restricted and pre-test estimation in a mis-specified econometric model

This thesis is concerned with the finite sample properties of some estimators of the unknown parameters in a linear model which is (possibly) mis-specified through the exclusion of relevant regressors. We assume that in addition to sample information, prior information regarding the unknown parameters is available in the form of a linear inequality constraint imposed on the regression coefficients. The combination of this type of prior information and sample information in specifying the corresponding statistical model leads to what has been identified in the literature as the inequality restricted estimator. If the statistical significance of the inequality constraint is tested prior to the estimation process, then the estimator thereby generated is called the inequality pre-test estimator. The properties of these estimators of the coefficient vector in a properly specified model have been examined rather thoroughly in the literature. In this thesis, we extend the results reported in the literature to the case where the underlying regression model is underfitted. We also investigate the sampling performance of the corresponding estimators for the model's disturbance variance, as well as the choice of an optimal size for the pre-test. The general background and motivation for this study are given in Chapter 1. Much of the earlier research on inequality restricted and pre-test estimation are built on results from studies that assume that the prior information is in the form of linear equality restrictions. We survey the relevant literature in this area in Chapter 2. Chapter 3 reviews the literature on inequality restricted and pre-test estimation. We focus on this problem in the context of the standard linear model with a single linear inequality constraint on the coefficient vector, as this is directly related to the theme of this thesis. In Chapter 4, we derive and evaluate the risk, under quadratic loss, of the inequality restricted and pre-test estimators for the regression prediction vector in an underfitted model. This analysis takes the established literature further by allowing for mis-specification in the regressor matrix. We consider the risk of the prediction vector, rather than the coefficient vector itself, so that our results are data independent. The risk functions of the corresponding estimators for the regression disturbance variance in the properly specified and underfitted models are derived in Chapters 6 and 7 respectively. As in the case where the prior information exists as linear equality restrictions, our results show that when the model is underfitted, the use of valid prior information does not necessarily guarantee a reduction in risk. This result holds for the estimation of both the prediction vector and the scale parameter. When one is estimating the regression disturbance variance, with an appropriate choice of test size, the inequality pre-test estimator can uniformly dominate the estimator that uses sample information only. We also find that the risk functions of the estimators of the error variance are affected more by mis-specification than are the corresponding predictive risks. In the case where no strictly dominating estimator exists, the question of the choice of an optimal critical value of the pre-test remains. Chapters 5 and 8 explore this issue when one is estimating the prediction vector and scale parameter respectively. We find that most of our results concur qualitatively with those reported in the literature when the prior information exists as exact equality restrictions. Chapter 9 contains some concluding remarks and a summary of the major results obtained in earlier chapters. We also outline some possible future research topics in this general area

Transparent Passenger Communication during Minimal Risk Maneuvers in Highly Automated Vehicles (L4)

Highly automated vehicles (HAVs, SAE 4) promise efficient, safe and inclusive transportation at an affordable price. Advanced concepts were developed without any fallback driver inside the vehicle. Though this is necessary to improve efficiency and affordability, it creates an unknown situation for future passengers. They no longer have a human driver present to reassure in uncertain situations, for example in minimal risk maneuvers (MRMs). MRMs are triggered when the vehicle automation encounters situations it cannot handle. In these situations, the HAV needs further assistance, which can be realized by the use of remote operation (RO). RO incorporates a human operator, who supports the HAV remotely (e.g. from distance) during MRMs and gives instructions to resolve these unknown situations. The combination of new situations, and the lack of a driver who could interact with passengers results in the need for new informational concepts inside the HAVs. The design and information richness of these concepts most likely influence passenger’s experience and acceptance of HAVs. Additionally, a basic and more intuitive understanding of the automation and its functionalities might be beneficial for passengers’ experience. Previous studies indicate that providing transparency by design might improve passengers understanding of the automation systems and increase trust. In theory, transparency improves when information about the actions and reasoning of a systems behavior is presented alongside its behavior. Yet, the specific design of informational interfaces in order to be transparent about the reasoning, especially in MRMs, is still unclear. Therefore, we investigated the impact of promising factors in an online study. Participants of the study evaluated different interfaces in multiple scenarios, where the HAV has to perform an MRM. The presented interfaces varied in displayed information richness to systematically manipulate transparency in the vehicle automation. The information varied concerning the vehicle´s behavior and reasoning. The design with the highest information richness also added expected consequences, like delay time, to the interface. The results of this study indicate that improvements of passenger’s understanding in MRMs are linked with increased transparency of provided information. Understanding regarding those MRMs scored significantly higher in the information richest design compared to the design without MRM specific information