Reduction of police vehicle accidents through mechaniically aided supervision

Tachograph recorders were installed in 224 vehicles of a metropolitan police department to monitor vehicle operation in an attempt to reduce the rate of accidents. Police sergeants reviewed each tachograph chart and provided feedback to officers regarding their driving performance. Reliability checks and additional feedback procedures were implemented so that upper level supervisors monitored and controlled the performance of field sergeants. The tachograph intervention and components of the feedback system nearly eliminated personal injury accidents and sharply reduced accidents caused by officer negligence. A cost-benefit analysis revealed that the savings in vehicle repair and injury claims outweighed the equipment and operating costs.     

How,and for whom,will activity patterns be modified by self-driving cars? Expectations from the state of Georgia

Many studies have explored travelers’ perceptions of self-driving cars (or autonomous vehicles, AVs) and their potential impacts. However, medium-term modifications in activity patterns (such as increasing trip frequencies and changing destinations) have been less explored. Using 2017–2018 survey data collected in the US state of Georgia, this paper (1) measures (at a general level) how people expect their activity patterns to change in a hypothetical all-AV era; (2) identifies population segments having similar profiles of expected changes; and (3) further profiles each segment on the basis of attitudinal, sociodemographic, and geographic characteristics. In the survey, respondents were asked to express their expectations regarding 16 potential activity modifications induced by AVs. We first conducted an exploratory factor analysis (EFA) to reduce the dimensionality of the activity-change vector characterizing each individual, and estimated non-mean-centered (NMC) factor scores (which have been rarely used in applied psychology). The EFA solution identified four dimensions of activity change: distance, time flexibility, frequency, and long distance/leisure. Next, we clustered Georgians with respect to these four-dimensional expectation vectors. The cluster solution uncovered six segments: no change, change unlikely, more leisure/long distance, longer trips, more travel, and time flexibility & more leisure/long distance. Using NMC factor scores identified considerably more inertia with respect to expectations for change than would have been apparent from the usual mean-centered scores. Finally, the various segments exhibit distinctive demographics and general attitudes. For example, those in the more leisure/long distance cluster tend to be higher income and are more likely to be Atlanta-region residents compared to other clusters, while those in the no change and change unlikely clusters tend to be older and are more likely to be rural residents.     

Suicide by crashing into a heavy vehicle: A one-year follow-up study of professional drivers

Train and heavy vehicle drivers can experience a traumatic event caused by people attempting suicide by crashing into their vehicles or jumping in front of them. While there are a number of studies on train drivers showing the negative consequences these events can have on their well-being, there are no studies on heavy vehicle drivers involved in these types of crashes. In the current study, we surveyed Finnish heavy vehicle drivers (N = 15) involved in a suicide crash in the year 2017 regarding their experiences and coping approximately one month (T1) and one year (T2) after the crash. Ten of these drivers reported one or various combinations of measurable consequences such as minor physical injuries, shorter or longer sickness absences, significant posttraumatic stress symptoms (measured using the Impact of Events Scale-Revised) and requiring psychological help. Posttraumatic stress symptoms decreased over time; however, three out of the four drivers who had a high IES-R score at T1 were still around the IES-R cut-off score at T2. This research raises questions whether and what kind of support heavy vehicle drivers who have been involved in a suicide crash should be given.     

Cyclists’ intentions to yield for automated cars at intersections when they have right of way: Results of an experiment using high-quality video animations

What will cyclists do in future conflict situations with automated cars at intersections when the cyclist has the right of way? In order to explore this, short high-quality animation videos of conflicts between a car and a cyclist at five different intersections were developed. These videos were ‘shot’ from the perspective of the cyclist and ended when a collision was imminent should the car or the bicyclist not slow down. After each video participants indicated whether they would slow down or continue cycling, how confident they were about this decision, what they thought the car would do, and how confident they were about what the car would do. The appearance of the approaching car was varied as within-subjects variable with 3 levels (Car type): automated car, automated car displaying its intentions to the cyclists, and traditional car. In all situations the cyclist had right of way. Of each conflict, three versions were made that differed in the moment that the video ended by cutting off fractions from the longest version, thus creating videos with an early, mid, and late moment for the cyclist to decide to continue cycling or to slow down (Decision moment). Before the video experiment started the participants watched an introductory video about automated vehicles that served as prime. This video was either positive, negative, or neutral about automated vehicles (Prime type). Both Decision moment and Prime type were between subject variables. After the experiment participants completed a short questionnaire about trust in technology and trust in automated vehicles. 1009 participants divided in nine groups (one per Decision moment and Prime) completed the online experiment in which they watched fifteen videos (5 conflicts × 3 car types). The results show that participants more often yielded when the approaching car was an automated car than when it was a traditional car. However, when the approaching car was an automated car that could communicate its intentions, they yielded less often than for a traditional car. The earlier the Decision moment, the more often participants yielded but this increase in yielding did not differ between the three car types. Participants yielded more often for automated cars (both types) after they watched the negative prime video before the experiment than when they watched the positive video. The less participants trusted technology, and the capabilities of automated vehicles in particular, the more they were inclined to slow down in the conflict situations with automated cars. The association between trust and yielding was stronger for trust in the capabilities of automated vehicles than for trust in technology in general.     

The utility of psychological measures in evaluating perceived usability of automated vehicle interfaces – A study with older adults

The design of the traditional vehicle human-machine interfaces (HMIs) is undergoing major change as we move towards fully connected and automated vehicles (CAVs). Given the diversity of user requirements and preferences, it is vital for designers to gain a deeper understanding of any underlying factors that could impact usability. The current study employs a range of carefully selected psychological measures to investigate the relationship with self-report usability of an in-CAV HMI integrated into a fully automated Level 5 simulator, during simulated journeys. Twenty-five older adults (65-years+) participated and were exposed to four journeys in a virtual reality fully automated CAV simulator (with video recorded journeys) into which our HMI was integrated. Participants completed a range of scales and questionnaires, as well as computerized cognitive tests. Key measures were: perceived usability of the HMI, cognitive performance, personality, attitudes towards computers, trust in technology, simulator sickness, presence and emotion. HMI perceived usability correlated positively with cognitive performance (e.g., working memory) and some individual characteristics such as trust in technology and negatively with neuroticism anxiety. Simulator sickness was associated negatively with CAV HMI perceived usability. Positive emotions correlated positively with reported usability across all four journeys, while negative emotions were negatively associated with usability only in the case of the last two journeys. Increased sense of presence in the virtual CAV simulator was not associated with usability. Implications for design are critically discussed. Our research is highly relevant in the design of high-fully automated vehicle HMIs, particularly for older adults, and in informing policy-makers and automated mobility providers of how to improve older people’s uptake of this technology.     

Attitudes towards privately-owned and shared autonomous vehicles

Research on attitudes towards autonomous vehicles (AVs) shows variation across gender, age, and socio-economic factors. While previous research has emphasized specific features and qualities of AVs, little is known about how attitudinal factors shape AV acceptance across a range of AV “modes” from privately-owned AVs to AV taxis shared with strangers. With an online panel of 834 US-based participants, we examine attitudes towards AVs and sharing. An exploratory factor analysis establishes four attitudinal dimensions: technology acceptance, risk-taking, traffic regulation, and driving enjoyment. We estimate multinomial logistic regression models to examine the impact of these four factors on attitudes toward AVs, willingness to purchase AVs, willingness to use AVs as a taxi service, and willingness to share AV taxis with strangers. We find a complex relationship between psychological factors and AV attitudes. “Early adopters” of technology and those who support stricter traffic regulations are more likely to have a positive attitude about AVs, whereas those who avoid risky behavior were more likely to have a negative attitude instead of a neutral attitude. Similar patterns were found across models of purchasing, using, and sharing AVs. The results imply that people who support traffic regulations may perceive AVs as a safer transport mode than human-driven cars, while those who avoid risk-taking behavior may perceive AVs to be more dangerous. However, we find that a large fraction of the population is not yet ready to use an AV with no driver, and overall reluctance to share a ride in an AV taxi.     

International study on the importance of communication between automated vehicles and pedestrians

The market launch of automated vehicles will take place in complex mixed traffic containing pedestrians and other non-automated drivers. Hand gestures, eye contact or similar informal communication strategies to human road users will not be available without a human car driver. Road crossing studies show that people are feeling confused and unsafe without such feedback. Additional external signaling devices have the aim to increase the perception of safety by providing auditory or visual feedback for road users. Due to the international relevance of this topic, we surveyed participants from six countries on the importance of communication. Our results reveal that intention messages are more important than the status signal. The importance of communication is independent of the time of day, traffic density and number of pedestrians. Cross-group analysis indicates a match of 72.97% between the tested nationalities on which kind of message types an automated vehicle should be able to communicate.     

Understanding the impact of heterogeneous rider preferences on a shared autonomous vehicle system

In a ride pooling system, riders may have varied behaviors in seeking pooled or non-pooled rides. It is important to understand the effect of these rider behaviors on the system performance in order to formulate policies to guide ride pooling implementation. Existing literature modeling ride pooling systems using agent-based models only considers the extreme cases in which riders either all participate or not participate in pooling. However, the pooling behaviors could be more complex. This study segments the rides in the system into five types (non-pooling only, non-pooling preferred, indifferent, pooling preferred, and pooling only). We use an agent-based model to simulate these preferences in a system of pooled autonomous vehicles. We use mixture experiments to vary the proportion of riders within these five types and build models to study the interactions among the rider types in terms of the system’s service quality and environmental performance. The results show that higher service level is achieved when all riders in the system are open to pooling, with 30% of pooling only riders and 70% of pooling preferred or indifferent riders providing the maximum value. The results can help formulate incentives and policies to promote ride pooling participation to improve ride pooling system performance.     

Exploring expert perceptions about the cyber security and privacy of Connected and Autonomous Vehicles: A thematic analysis approach

Connected and Autonomous Vehicles (CAVs) constitute an automotive development carrying paradigm-shifting potential that may soon be embedded into a dynamically changing urban mobility landscape. The complex machine-led dynamics of CAVs make them more prone to data exploitation and vulnerable to cyber attacks than any of their predecessors increasing the risks of privacy breaches and cyber security violations for their users. This can adversely affect the public acceptability of CAVs, give them a bad reputation at this embryonic stage of their development, create barriers to their adoption and increased use, and complicate the business models of their future operations. Therefore, it is vital to identify and create an in-depth understanding of the cyber security and privacy issues associated with CAVs, and of the way these can be prioritised and addressed. This work employs 36 semi-structured elite interviews to explore the diverse dimensions of user acceptance through the lens of the well-informed CAV experts that already anticipate problems and look for their solutions. Our international interviewee sample represents academia, industry and policy-making so that all the key stakeholder voices are heard. Thematic analysis was used to identify and contextualise the factors that reflect and affect CAV acceptance in relation to the privacy and cyber security agendas. Six core themes emerged: awareness, user and vendor education, safety, responsibility, legislation, and trust. Each of these themes has diverse and distinctive dimensions and are discussed as sub-themes. We recommend that mitigating the cyber security and privacy risks embedded in CAVs require inter-institutional cooperation, awareness campaigns and trials for trust-building purposes, mandatory educational training for manufacturers and perhaps more importantly for end-users, balanced and fair responsibility-sharing, two-way dynamic communication channels and a clear consensus on what constitutes threats and solutions.     

Pedestrian interaction with automated vehicles at uncontrolled intersections

Automated Vehicles (AVs) are being developed rapidly and tested on public roads, but pedestrians’ interaction with AV is not comprehensively understood or thoroughly investigated to ensure safe operations and the public’s trust of AVs. In this study, we aimed to provide another research evidence to enhance such understanding with the use of external interfaces for facilitating the interaction between pedestrians and AVs. We developed five external interfaces, including text, symbol, animated-eye, a combination of text and symbol, and speed. These interfaces communicated five types of information, including (1) intent of AV; 2) advice to pedestrians of what to do, (3) AV’s awareness of pedestrians, (4) combination of intent and advice, and (5) vehicle movement (i.e., speed). We tested the interfaces through two field studies at uncontrolled intersections with crosswalks. The Wizard of Oz method was used, in which an experimenter worked as a driver in an instrumented vehicle and wore an outfit to be invisible to the pedestrians, thus rendering the set-up to simulate an AV interacting with a pedestrian. The interfaces were displayed on an LED panel mounted on the AV. Results showed that the AV’s external interface did not change pedestrians’ response time in comparison with the baseline without any interface. There was no statistically significant difference in response time among the external interfaces either. According to the post-experimental interview, vehicle movement pattern (e.g., vehicle speed) continued to be a significant cue for pedestrians to decide when to cross the intersections. Participants perceived the communication of the AV’s intent and vehicle speed as more beneficial than the communication of AV’s awareness. The subjective ratings showed positive effects of those interfaces that were easy to understand (e.g., text interface and speed interface), which also helped pedestrians feel safer when interacting with the AV.