Inventing Mobility For All: Mastering Mobility-as-a-Service with Self-Driving Vehiclesdescribes Mobility-as-a-Service and explains the impact of this mobility concept on social and societal life as ...well as on people's travel behavior.
•High validity of findings through physical experience of automated vehicles.•Application of automated vehicles in a realistic traffic environment.•Full transparency with pre-registration and ...definitions, measures, and dataset.•Analysis of attitudes and emotions towards self-driving cars.•Acceptance, perceived safety, and age predict intention to use automated vehicles.
Automated vehicles (AVs) have left the laboratories and can be experienced in several projects, e.g. at the premises of a clinic in Germany. With this transition, research on AV attitudes no longer needs to rely on questionnaires with hypothetical scenarios and simulations. Previous research – limited by the unavailability of AVs – has provided ambivalent results regarding age and gender differences in attitudes towards AVs. We present research results about the role of age and gender in connection with attitudes such as acceptance, perceived safety, and trust, as well as intention to use. We additionally demonstrate relationships between those constructs and emotions such as amusement, fear, and surprise. Data were collected from participants (n = 125) after having experienced an AV ride with level 4 automation on two campuses of a clinic in Berlin, Germany. Results reveal strong correlations between all attitudes (0.55 ≤ r ≤ 0.71; p < 0.01) and show acceptance and perceived safety to be solid predictors of intention to use AVs. We also found age to be a significant predictor for usage intention even when other attitudes are considered (β = −0.22; p < 0.01). MANOVA results point to gender differences in all constructs, but with limited confidence (5.40 ≤ F ≤ 18.34; p ≤ 0.02). However, we reject our hypothesis that young men are highly accepting, trusting, and intending to use AVs compared to other combinations of age and gender. We recommend using a mix of attitude, emotion, and behavioural (intention) measures in future research on AVs together with more transparency regarding construct definitions and study materials.
In this paper, we provide a decentralized theoretical framework for coordination of connected and automated vehicles (CAVs) at different traffic scenarios. The framework includes: (1) an upper-level ...optimization that yields for each CAV its optimal time trajectory and lane to pass through a given traffic scenario while alleviating congestion; and (2) a low-level optimization that yields for each CAV its optimal control input (acceleration/deceleration). We provide a complete, analytical solution of the low-level optimization problem that includes the rear-end, speed-dependent safety constraint. Furthermore, we provide a problem formulation for the upper-level optimization in which there is no duality gap. The latter implies that the optimal time trajectory for each CAV does not activate any of the state, control, and safety constraints of the low-level optimization, thus allowing for online implementation. Finally, we present a geometric duality framework with hyperplanes to derive the condition under which the optimal solution of the upper-level optimization always exists. We validate the effectiveness of the proposed theoretical framework through simulation.
•Develop a novel instrument for initial trust between pedestrians and AVs.•Identify seven key constructs to predict initial trust in the pedestrian-AV context.•Consider individual's trust propensity, ...different trust/trustworthiness attributes.•Emphasize the importance of incorporating social expectations into AV design.
Considering that a significant portion of the current pedestrian population has limited exposure to automated vehicles (AVs), it is crucial to have a reliable instrument for assessing pedestrians’ initial trust in AVs. Using a survey of 436 pedestrians, this study developed and validated a PITQA (Pedestrians’ Initial Trust Questionnaire for AVs) scale using partial least squares structural equation modeling (PLS-SEM). The proposed scale will be valuable in monitoring the progression of trust over time and considering trust-related factors during the design process. The results revealed that seven key constructs significantly contribute to predicting initial trust between pedestrians and AVs. These constructs include propensity to trust, perceived statistical reliability, dependability and competence, perceived predictability, familiarity, authority/subversion, care/harm, and sanctity/degradation. These shed light on how the trust propensity of individuals, different trust/trustworthiness attributes might constitute different aspects of initial trust in the pedestrian-AV context. The developed scale can be a potentially useful tool for future research endeavors concerning trust calibration and the design of AVs specifically tailored for vulnerable road users.
Automated vehicle (AV) deployment can bring about transformational changes to transportation and society as a whole. The infrastructure owner-operators (IOOs), who own, maintain, and operate the ...infrastructure, have the opportunity to work jointly with the AV industry to provide safe and efficient operations. A key question for the IOOs is, “What transportation infrastructure improvements do AV manufacturers believe will facilitate and improve AV performance?” This study was designed to address this question through a comprehensive survey approach, including an online survey and follow-up interviews. A list of ten questions was discussed, covering the physical and digital infrastructure, infrastructure maintenance, standards and specifications, policy support, data sharing, and so forth. The researchers reached out to more than 60 entities who hold the AV testing permit in California. In total, 20 companies responded. They were from different sectors and well represented the AV industry. From the results of this study, it is concluded that the most important roadway characteristics that have the potential to benefit the automated driving system (ADS) are: (1) digital mapping and signage; (2) lane markings; (3) work zone and incident information; (4) vehicle-to-everything (V2X) communications; (5) actual traffic signals; (6) general signage; and (7) lighting. The digital features considered most critical to help accelerate ADS deployment include work zone and road closure information, traffic signal phase and timing, and traffic congestion. This study provides diverse voices and in-depth insights into topics that the AV industry and IOOs should engage in to advance AVs’ deployment.
Extended exposure to reliable automation may lead to overreliance as evidenced by poor responses to auto-mation errors. Individual differences in trust may also influence responses. We investigated ...how these factors affect response to automation errors in a driving simulator study comprised of stop-controlled and uncon-trolled intersections. Drivers experienced reliable vehicle automation during six drives where they indicated if they felt the automation was going too slow or too fast by pressing the accelerator or brake pedal. Engage-ment via pedal presses did not affect the automation but offered an objective measure of trust in automation. In the final drive, an error occurred where the vehicle failed to stop at a stop-controlled intersection. Drivers’ response to the error was inferred from brake presses. Mixture models showed bimodal response times and revealed that drivers with high trust were less likely to respond to automation errors than drivers with low trust.
In the future, the role of the human factor in the driving processes is expected to decrease continuously. At the same time, based on the global trends, the role of computer-supported decision ...systems and artificial intelligence (AI)-based control solutions increases in relation to driving processes, which carries a significant safety-enhancing potential. To assess the possible social benefits of automated vehicle systems objectively, it is necessary to analyze the possible negative effects in detail as well. Accordingly, the aim of this article is to present a statistical survey of crashes involving automated vehicles today in order to identify and evaluate the factors that are relevant in the crashes. The analyzed data showed that when the autopilot mode was turned off and the human driver made the control decisions, the severity of crashes on straight roads was greater at <inline-formula> <tex-math notation="LaTeX">\alpha \,\,=0.1 </tex-math></inline-formula> significance level than when the vehicle was in autopilot mode and the vehicle system made the control decisions. In addition, if the <inline-formula> <tex-math notation="LaTeX">\alpha </tex-math></inline-formula> significance level is 0.2, then crashes on plain terrain, during the day, or in the speed range of 80-100 km/h are generally less serious for vehicles driven in autopilot mode than for vehicles with autopilot mode turned off. In light of the considerations above, it is also important to emphasize that this paper only investigates crash severity given occurrence but not the probability of occurrence itself.
•A cycling simulator was used to recreate the cyclists’ interaction scenarios with motorized vehicles.•The effect of the difference in time to arrival at the intersection and visibility distance was ...observed on the interaction.•Cyclists’ behavioral patterns were investigated in response to independent variables.•Eye contact and communication with the driver plays an important role in cyclists decision-making.
Introduction: With cycling gaining more popularity in urban areas, it is vital to obtain accurate knowledge of cyclists’ behavior to develop behavioral models that can predict the cyclist’s intent. Most conflicts between cyclists and vehicles happen at crossings where the road users share the path, especially at unsignalized intersections. However, few studies have investigated and modeled the interaction between cyclists and vehicles at unsignalized intersections. Method: A bike simulator experiment was conducted to scrutinize cyclists’ response process as they interacted with a passenger car at an unsignalized intersection. An existing unsignalized intersection in Gothenburg was simulated for test participants. Two independent variables were varied across trials: the difference in time to arrival at the intersection (DTA) and intersection visibility (IV). Subjective and quantitative data were analyzed to model the cyclists’ behavior. Results: When approaching the intersection, cyclists showed a clear sequence of actions (pedaling, braking, and head turning). The distance from the intersection at which cyclists started braking was significantly affected by the two independent variables. It was also found that DTA, looking duration, and pedaling behavior significantly affected cyclists’ decisions to yield. Finally, the questionnaire outputs show that participants missed eye contact or communication with the motorized vehicle. Conclusions: The kinematic interaction between cyclists and vehicles, along with the cyclist’s response process (visual and kinematic), can be utilized to predict cyclists’ yielding decision at intersections. From the infrastructural perspective, enhancing visibility at intersections has the potential to reduce the severity of interactions between cyclists and vehicles. The analysis of the questionnaire emphasizes the significance of visual communication between cyclists and drivers to support the cyclist’s decision-making process when yielding. Practical applications: The models can be used in threat assessment algorithms so that active safety systems and automated vehicles can react safely to the presence of cyclists in conflict scenarios.
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