Creating a qualitative typology of electric vehicle driving: EV journey-making mapped in a chronological framework

‘Range anxiety’ is considered a barrier to market acceptance of electric vehicles (EVs), but perceptions of inadequate recharging infrastructure and limited battery range may be part of a more complex set of consumer concerns; experiences and practices of existing EV drivers – with reference to wider transport and energy systems – need to be understood in greater detail. Qualitative material from semi-structured interviews [n = 88] is used to formulate a chronological Typology of Electric Vehicle Driving which synthesises drivers’ decisions, actions and dynamic considerations before, during and after a journey. The typology outlines and tabulates spatio-temporal elements specific to driving an EV; describes adaptations and compromises in household routines and travel habits; draws out how or when drivers engage with external digital information sources – i.e. smartphone apps, social media – during journey-making; plots their intervention in behavioural changes; and could inform development of infrastructure, tools or services, policy or interventions to support EV uptake.     

What Would It Take to Get You into an Electric Car? Consumer Perceptions and Decision Making about Electric Vehicles

Americans buy millions of vehicles every year, but research on how those decisions are made, including personality and individual difference factors, has rarely taken full advantage of psychological insights. This is an increasingly important topic because, for instance, decisions to buy electric vehicles (EVs) rather than gasoline-powered vehicles have environmental and geopolitical consequences. A series of studies reveal the very different perceptions of EVs and gas vehicles. Although vehicle choices at the aggregate level were strongly correlated with economic considerations, individual consumer choices were correlated with delay discounting rates in Study 1 but not Study 2. These studies also did not find significant correlations between EV purchase decisions and a number of other individual difference traits often thought to be factors in such decisions (i.e., social value orientation, political attitudes, environmental attitudes, preference for novel products, or an array of core social values). Regression models indicate that factors which did predict vehicle choices concerns were about the performance and range of EVs, EV prevalence in general, and beliefs about what statements different vehicle types made about their owners and the owners’ values. More attention to the vehicle-associated values and immediate performance/use issues can help to promote EV purchase decisions and subsequent improvements in environmental and political stability.     

Electric vehicle drivers’ reported interactions with the public: Driving stereotype change?

Image and symbolic meanings of cars play a key role in their desirability and in consumers’ purchasing behaviour. Drivers in the United Kingdom Technology Strategy Board funded Ultra Low Carbon Vehicle trial were interviewed regarding their interactions with the general (non-EV driving) public. EV drivers’ accounts suggest that EVs are particularly susceptible to stereotyping, but that the stereotyping is in a state of flux. Three different categories of meaning emerged. A Traditional, affective-based negative stereotype exists, despite reflecting outdated associations with milk floats and older EV models. However, the current period of widespread EV trialling in the UK sees the emergence of additional EV stereotypes. Drivers encountered categories of meaning that were Ambivalent: cognitively-based, ambivalence showed people to hold negative views and reservations but also to reveal a willingness to assess the current capabilities of EVs. Finally, drivers encountered people holding Positive meanings of EVs. Greater contact with drivers of contemporary EVs helped to develop these further. Based on a combination of affect, cognition and behaviour, it shows a segment of non-EV drivers to be engaged with the new technology and to see EVs as well developed now and also as cars of the future. We discuss the key factors underpinning each of the categories of meaning and indicate the ramifications for the likely future success of EV uptake.     

A driving simulator study to assess driver performance during a car-following maneuver after switching from automated control to manual control

A review of the literature on autonomous vehicles has shown that they offer several benefits, such as reducing traffic congestion and emissions, and improving transport accessibility. Until the highest level of automation is achieved, humans will remain an important integral of the driving cycle, which necessitates to fully understand their role in automated driving. A difficult research topic involves an understanding of whether a period of automated driving is likely to reduce driver fatigue rather than increase the risk of distraction, particularly when drivers are involved in a secondary task while driving. The main aim of this research comprises assessing the effects of an automation period on drivers, in terms of driving performance and safety implications. A specific focus is set on the car-following maneuver. A driving simulator experiment has been designed for this purpose. In particular, each participant was requested to submit to a virtual scenario twice, with level-three driving automation: one drive consisting of Full Manual Control Mode (FM); the other comprising an Automated Control Mode (AM) activated in the midst of the scenario. During the automation mode, the drivers were asked to watch a movie on a tablet inside the vehicle. When the drivers had to take control of the vehicle, two car-following maneuvers were planned, by simulating a slow-moving vehicle in the right lane in the meanwhile a platoon of vehicles in the overtaking lane discouraged the passing maneuver. Various driving performances (speeds, accelerations, etc.) and surrogate safety measures (PET and TTC) were collected and analysed, focusing on car-following maneuvers. The overall results indicated a more dangerous behavior of drivers who were previously subjected to driving automation; the percentage of drivers who did not apply the brakes and headed into the overtaking lane despite the presence of a platoon of fast-moving vehicles with unsafe gaps between them was higher in AM drive than in FM drive. Conversely, for drivers who preferred to brake, it was noted that those who had already experienced automated driving, adopted a more careful behavior during the braking maneuver to avoid a collision. Finally, with regard to drivers who had decided to overtake the braking vehicle, it should be noted that drivers who had already experienced automated driving did not change their behavior whilst overtaking the stopped lead vehicle.     

Audiovisual time-to-collision estimation for accelerating vehicles: The acoustic signature of electric vehicles impairs pedestrians' judgments

To avoid collisions, pedestrians intending to cross a road need to accurately estimate the time-to-collision (TTC) of an approaching vehicle. For TTC estimation, auditory information can be considered particularly relevant when the approaching vehicle accelerates. The sound of vehicles with internal combustion engine (ICEVs) provides characteristic auditory information about the acceleration state (increasing rotational speed and engine load). However, for electric vehicles (EVs), the acoustic signature during acceleration is less salient. Although the auditory detection of EVs has been studied extensively, there is no research on potential effects of the altered acoustic signature of EVs on TTC estimation. To close this gap, we compared TTC estimates for ICEVs and for EVs with and without activated acoustic vehicle alerting system (AVAS). We implemented a novel interactive audiovisual virtual-reality system for studying the human perception of approaching vehicles. Using acoustic recordings of real vehicles as source signals, the dynamic spatial sound field corresponding to a vehicle approaching in an urban setting is generated based on physical modeling of the sound propagation between vehicle and pedestrian (listener) and is presented via sound field synthesis (higher-order Ambisonics). In addition to the auditory simulations, the scene was visually presented on a head-mounted display with head tracking. Participants estimated the TTC of vehicles that either approached at a constant speed or accelerated positively. In conditions with constant speed, TTC estimates for EVs with and without AVAS were similar to those for ICEVs. In contrast, for accelerating vehicles, there was a substantial effect of the vehicle type on the TTC estimates. For the EVs, the mean TTC estimates showed a significant overestimation. Thus, subjects on average perceived the time of arrival of the EV at their position as longer than it actually was. The extent of overestimation increased with acceleration and presented TTC. This pattern is similar to a first-order TTC estimation representing a failure to consider the acceleration, which is consistently reported in the literature for visual-only presentations of accelerating objects. In comparison, the overestimation of TTC was largely reduced for the accelerating ICEVs. The AVAS somewhat improved the TTC estimates for the accelerating EVs, but without reaching the same level of accuracy as for the ICEVs. In real traffic scenarios, overestimations of the TTC of approaching vehicles might lead to risky road-crossing decisions. Therefore, our finding that pedestrians are significantly less able to use the acoustic information emitted by accelerating EVs for their TTC judgments, compared to accelerating ICEVs, has important implications for road safety and for the design of AVAS technologies.     

Exploring the benefits of conversing with a digital voice assistant during automated driving: A parametric duration model of takeover time

Vehicle automation allows drivers to disengage from driving causing a potential decline in their alertness. One of the major challenges of highly automated vehicles is to ensure a timely (with respect to safety and situation awareness) takeover in such conditions. For this purpose, the current study investigated the role of an in-vehicle digital voice-assistant (VA) in conditionally automated vehicles, offering spoken discourse relating specifically to contextual factors, such as the traffic situation and road environment. The study involved twenty-four participants, each taking two drives (counterbalanced): with VA and without VA, in a driving simulator. Participants were required to takeover vehicle control following the issuance of a takeover request (TOR) near the end of each drive. A parametric duration model was adopted to find the key factors determining takeover time (TOT). Paired comparisons showed higher alertness and higher active workload (mean NASA-TLX rating) during automation when accompanied by the VA. Paired t-test comparison of gaze behavior prior to takeover showed significantly higher instances of checking traffic signal, roadside objects, and the roadway during the drive with VA, indicating higher situation awareness. The parametric model indicated that the VA increased the likelihood of making a timely takeover by 39%. There was also some evidence suggesting that male drivers are likely to resume control 1.21 times earlier than female drivers. The study findings highlight the benefits of adopting a digital voice assistant to keep the drivers alert and aware about the recent traffic environment in partially automated vehicles.     

Concept evaluation of intersection decision support (IDS) system interfaces to support drivers’ gap acceptance decisions at rural stop-controlled intersections

Rural stop-controlled intersections pose a crash risk for drivers turning or crossing the intersection from the minor road. In particular, elderly drivers are at the highest risk of a collision in this situation. Errors made during gap detection, perception and acceptance are the main factors that influence crashes at this type of intersection. This study investigated young (20–40 years) and old (55–75 years) drivers’ gap acceptance performance in simulated day and night driving conditions in a Baseline condition (STOP sign only) and four intersection decision support (IDS) conditions. The four IDS conditions were initial infrastructure-based design concepts that provided varying levels of dynamic information about traffic conditions on the major road to crossing minor-road drivers. Signs that provided detailed gap information (i.e., time-to-arrival values, warning levels for gaps) as well as advisory information about unsafe conditions resulted in the best performance among old and young drivers in comparison to signs that did not provide specific gap-related information (i.e., detected vehicles approaching, but not size of gap or safety of gap). Comprehension, acceptance and usability ratings of the IDS signs were also highest for signs providing detailed gap and advisory information on the same sign. Recommendations for further design and development of the IDS system interface based on driver performance and acceptance of the technology is discussed.     

Driver responses to motorcycle and lead vehicle braking events: The effects of motorcycling experience and novice versus experienced drivers

In many countries, motorcyclists are over-represented in traffic collision fatalities and injuries compared to vehicle registrations. Why drivers may violate the right-of-way of motorcyclists traveling as lead vehicles in front of drivers is empirically examined in two studies that were conducted with a moderate-fidelity driving simulator. The purpose of the first study was to determine if drivers, who also held a motorcycle license (N = 16), drove cars differently than regular drivers (N = 16) around motorcycles. The two groups did not differ on responses to motorcycling braking events, which was consistent with previous research on car following. The second study compared the driving performance of sixteen novice teenage drivers (M = 16.2 years of age) to 15 experienced drivers (M = 32.9) over the span of six monthly simulator sessions. Novice drivers’ perception response times (PRT) to the braking events were significantly longer than those of the experienced drivers. PRTs to motorcycle and lead vehicle braking events decreased over sessions. For all participants, PRTs to the motorcycle events were longer than to the car events. The implications of these results for motorcyclists and drivers with different levels of experience are discussed.     

Cross-cultural effects on the perception and appraisal of approaching motorcycles at junctions

Both perceptual errors (failing to perceive) and appraisal errors (failing to make a correct judgment about safety) could explain the relatively high number of pulling out at the junctions involving approaching motorcycles in relation to cars. Two experiments were conducted to investigate the effect of exposure to motorcycles on these types of errors by comparing drivers from Malaysia where motorcycles are very common with drivers from the UK where motorcycles are rare. Experiment 1 investigated drivers’ ability to perceive approaching vehicles (car or motorcycle) located at different distances (near, intermediate and far) on UK and Malaysian roads. There was no difference between Malaysian and UK drivers in overall ability to perceive the approaching vehicles but Malaysian drivers were relatively good at perceiving motorcycles at further distances. Experiment 2 investigated drivers’ judgments about whether or not it was safe to pull out on the same roads and found that Malaysian drivers were more likely to judge it was safe to pull out as compared to UK drivers. Findings suggest that high exposure to motorcycles may reduce vehicle effects on perception for Malaysian drivers. However they may make more risky appraisals about safety of pulling out, which might contribute to the high accident and fatality rates in Malaysia.     

Experiencing Range in an Electric Vehicle: Understanding Psychological Barriers

Range of electric vehicles (EVs) has long been considered a major barrier in acceptance of electric mobility. We examined the nature of how range is experienced in an EV and whether variables from other adaptation contexts, notably stress, have explanatory power for inter‐individual differences in what we term comfortable range. Forty EVs were leased to a sample of users for a 6‐month field study. Qualitative and quantitative analyses of range experiences were performed, including regression analyses to examine the role of stress‐buffering personality traits and coping skills in comfortable range. Users appraised range as a resource to which they could successfully adapt and that satisfied most of their daily mobility needs. However, indicators were found that suggested suboptimal range utilisation. Stress‐buffering personality traits (control beliefs, ambiguity tolerance) and coping skills (subjective range competence, daily range practice) were found to play a substantial role in comfortable range. Hence, it may be possible to overcome perceived range barriers with the assistance of psychological interventions such as information, training, and interface design. Providing drivers with a reliable usable range may be more important than enhancing maximal range in an electric mobility system.     

Investigating driver reactions to movements of autonomous vehicle in permitted right turn through driving simulator experiments

Traffic safety has always been a hot topic for human-driven (HDV) and autonomous vehicles (AV) mixed flow. The conflict between permitted right-turn vehicles (PRT) and opposing through vehicles (TH) at signalized intersections (left-handed traffic) is extraordinarily critical. AVs with aggressive behaviors are able to accept short gap time without losing safety. However, such a turning maneuver may lead to dangerous feelings and cause unexpected reactions of approaching drivers. This study aims to investigate and model drivers’ reactions in TH movements to PRT AVs considering the trust degree of drivers to AVs. Questionnaire surveys and driving simulator experiments were conducted for 41 participants. Results reveal that the right turn timing of PRT AV will significantly influence drivers’ reactions. Basically, TH drivers will brake with a high probability under the situation of small expected post encroachment time (PET). It is also found that female drivers and drivers with low trust in AVs are more vigilant to PRT AVs than other drivers. Based on this finding a two-layer model for reproducing TH drivers’ reactions to PRT AVs is proposed. The first layer is to determine the braking decision and the second layer is to calculate the parameters of braking behaviors (brake lag, braking time, and speed drop). The significance and coefficients variables in these models proved that the trust in AV will influence drivers’ decisions and braking behaviors (brake lag and braking time). The more the drivers trust AVs, the smaller the expected PET to AVs they can accept for passing without braking, and the more gently they will brake (longer brake lag and shorter braking time) due to the cutting in of PRT AVs. This effect will become significant after drivers have experienced several interactions with AVs.     

Effects of different non-driving-related-task display modes on drivers’ eye-movement patterns during take-over in an automated vehicle

Highly automated vehicles relieve drivers from driving tasks, allowing them to engage in non-driving-related-tasks (NDRTs). However, drivers are required to take over control in certain circumstances due to the limitations of highly automated vehicles. This study focused on drivers’ eye-movement patterns during take-overs when an NDRT (watching videos) was presented via a head-up-display (HUD) and a mobile device display (MDD), compared to no NDRT as the baseline. The experiment was conducted in a high-fidelity driving simulator with real-world driving videos scenarios. Forty-six participants took part in the experiment by completing three drives in three counterbalanced conditions (HUD, MDD and baseline). A take-over-request was issued towards the end of automated driving requesting drivers to stop the NDRT and take over control. Eye-movement data including pupil diameter, blinks, glance duration and number of AOI (Area of Interest) were collected and analysed. The results show that during automated driving, drivers were more engaged in the MDD NDRT with smaller pupil diameter and shorter glance duration on the front scenario compared to the HUD and baseline modes. The number of AOI was reduced during automated driving in both MDD and HUD modes. The take-over-request redirected drivers’ visual attention back to the driving task from NDRT by increasing drivers’ pupil diameter, glance duration and number of AOI. However, the effect of MDD NDRT on pupil diameter and glance duration continued even after the take-over-request when the NDRT was terminated. The study demonstrated HUD is a better display to help maintain drivers’ attention on the road.     

The role of drivers’ social interactions in their driving behavior: Empirical evidence and implications for car-following and traffic flow

Models for describing the microscopic driving behavior rarely consider the “social effects” on drivers’ driving decisions. However, social effect can be generated due to interactions with surrounding vehicles and affect drivers’ driving behavior, e.g., the interactions result in imitating the behavior of peer drivers. Therefore, social environment and peer influence can impact the drivers’ instantaneous behavior and shift the individuals’ driving state. This study aims to explore empirical evidence for existence of a social effect, i.e., when a fast-moving vehicle passes a subject vehicle, does the driver mimic the behavior of passing vehicle? High-resolution Basic Safety Message data set (N = 151,380,578) from the Safety Pilot Model Deployment program in Ann Arbor, Michigan, is used to explore the issue. The data relates to positions, speeds, and accelerations of 63 host vehicles traveling in connected vehicles with detailed information on surrounding environment at a frequency of 10 Hz. Rigorous random parameter logit models are estimated to capture the heterogeneity among the observations and to explore if the correlates of social effect can vary both positively and negatively. Results show that subject drivers do mimic the behavior of passing vehicles –in 16 percent of passing events (N = 18,099 total passings occurred in freeways), subject vehicle drivers are observed to follow the passing vehicles accelerating. We found that only 1.2 percent of drivers normally sped up (10 km/hr in 10 s) during their trips, when they were not passed by other vehicles. However, if passed by a high speed vehicle the percentage of drivers who sped up is 16.0 percent. The speed change of at least 10 km/hr within 10 s duration is considered as accelerating threshold. Furthermore, the acceleration of subject vehicle is more likely if the speed of subject driver is higher and more surrounding vehicles are present. Interestingly, if the difference with passing vehicle speed is high, the likelihood of subject driver’s acceleration is lower, consistent with expectation that if such differences are too high, the subject driver may be minimally affected. The study provides new evidence that drivers’ social interactions can change traffic flow and implications of the study results are discussed.     

Exploring consumer adoption of a high involvement eco‐innovation using value‐belief‐norm theory

Environmental problems are increasingly becoming everyday issues of international organizations, national governments, and individual consumers. In consumer behavior research considerable effort has been focused on understanding environmentally significant behaviors. One such research stream uses the value‐belief‐norm theory (VBN) to explain and predict a number of relatively low involvement proenvironmental consumer behaviors such as household energy use. However, many consumer behaviors with significant impact on the environment are categorized as high involvement behaviors where VBN theory has not yet been employed. The aim of this paper is to arrive at a better understanding of consumer adoption of a high involvement eco‐innovation using VBN theory. As an example of a high involvement eco‐innovation the alternative fuel vehicle (AFV) which runs on fossil oil‐alternative fuels such as electricity and biofuels is used. A representative sample of adopters and non‐adopters of these vehicles in Sweden were surveyed. Differences between adopters and non‐adopters on sociodemographic and VBN factors were analyzed and the explanatory ability of the different factors on adoption was analyzed using logistic regression. The results showed that early adopters had a higher level of education and were much more likely to live in multi‐person households compared to non‐adopters. In terms of attitudinal factors, adopters exhibited higher levels of proenvironmental values, beliefs, and personal norms (PNs). Furthermore the results established that VBN factors were successful in explaining the early adoption of a high involvement eco‐innovation such as the AFV. The implications for consumer research, public policymakers, and for marketers of eco‐innovations are discussed. Copyright © 2011 John Wiley & Sons, Ltd.     

The relationship between leisure time and driving style in two groups of male drivers

Despite overall improvements in traffic safety levels, young male drivers continue to be over represented in accidents. In this study the association between driving style and leisure time was examined in two groups of drivers. The sample consisted of 4000 male drivers of which 2000 were 18 years old and 2000 were 28 years old. A posted questionnaire was used. The overall response rate was 51% (N = 2018). A similar factor structure of three factors with regard to driving style was identified in both groups of drivers. The factors were named Thrill, Anxiety and Anger. Despite significant differences regarding specific leisure time activities, a similar relationship between leisure time and driving style was found in the two groups of drivers particularly in relation to problem behaviours. In both groups driving related interaction with friends as well as a high paced leisure time was found to be related to a high score on the factors Thrill and Anger. In the older group of drivers cruising with friends was associated with problem behaviours such as smoking pot and doing drugs. Results indicate a need for early intervention enhancing safe behavioural choices thereby preventing unsafe behaviours to become permanent behavioural strategies in life.     

Keep on cruising: Changes in lifestyle and driving style among male drivers between the age of 18 and 23

Despite recent improvements in general road safety levels, young male drivers in most western countries continue to be overrepresented in road traffic accidents. Lifestyle related motivational factors are a key element in the young male driver problem. Based on 379 posted questionnaires completed by the same male drivers at the age of 18 and again at the age of 23, this study examined changes in the relationship between lifestyle and driving style over a 5 year period. A number of changes in car use, driving style and engagement in different leisure time activities were found. Cruising was related to an extrovert social life as well as problem behaviours such as drink driving. At the age of 18 cruising was a part of the normal social life of the majority of the participants. However, while most drivers reduced their level of cruising as well as related problem behaviour over time, a smaller group still showed a similar life style at the age of 23. The study confirmed the importance of lifestyle related motivational factors for driving behaviour among young drivers.     

Driver injury severity study for truck involved accidents at highway-rail grade crossings in the United States

Although trucks only account for approximately 4% of all the vehicles based on the Federal Railway Administration (FRA) database, about 25% involved truck accidents happen at highway-rail grade crossings. This study applied an ordered probit model to explore the determinants of injury severity of truck drivers at highway-rail grade crossing in the United States. Given the importance of trucking to the economics of a country and the safety concerns posed by the trucks (as a result of their large size and weight making them difficult to control, maneuver, and stop), a comprehensive research on truck accidents is critical. Based on data analysis results, the strong effects of driver-, environmental-, weather- characteristics on the injury severities in truck accidents happened at highway-rail grade crossings are found. The findings reveal that better speed control for trucks will significantly reduce driver injury severity in accidents occurring at highway-rail grade crossings. In addition, several truck driver behavior characteristics (such as driving under influence of fatigue during peak hour) were found to be statistically significant predictors of high-level injury severity. Thus, education and enforcement targeted to truck drivers could facilitate safety improvements. Moreover, environmental factor (such as area type and roadway pavement) is found to be statistically significant. Truck drivers are more likely to have severe injury in open space area with low traffic volume compared with other areas. The bad weather and visibility condition is found to increase the probability of truck drivers’ high level injury severity.     

Narrow lanes and their effect on drivers’ behaviour at motorway roadworks

In recent years, more use of narrow lanes as a temporary traffic management scheme (TTMS) on UK motorway roadwork sections has been made. The rationale is to free up carriageway space, especially for sites with high traffic demands needing repairs. What remains to be determined is the impact of this work on traffic operation. This is important due to the need to manage traffic operational turbulence which could affect the capacity as well as safety levels in roadwork sections. Site observations (mainly using camcorders from overhead bridges) were made which uncovered two discernible patterns of driving behaviour where narrow lanes are implemented at roadworks, especially when heavy goods vehicles (HGVs) are present: (i) “avoiding” passing/overtaking HGVs travelling in the adjacent lanes and (ii) lane “repositioning” while passing/overtaking which might cause some turbulence to traffic operation. From one site, almost half of the passenger car drivers (including small vans) who were following a HGV on the adjacent lanes were avoiding passing that HGV. Also, nearly three quarters of the observed passenger cars, the passenger car drivers tried to position their vehicles as far away as possible, laterally, from the adjacent HGV while passing/overtaking that HGV in order to widen the lateral gap between their vehicles and the HGV. This resulted in driving too close to the edge of the road markings of their current lane away from the HGV. Therefore, this paper aims to report on the “avoiding” and lane “repositioning” behaviours to help inform traffic management teams/designers using narrow lanes as TTMS and make them aware of such behaviours (especially for motorway sections carrying high percentages of HGVs). Also, the finding from these observations were used in the development of a new micro-simulation model in order to evaluate the effect of such turbulence.     

How does green suit me? Consumers mentally match perceived product attributes with their domain‐specific motives when making green purchase decisions

Nowadays, environment‐friendly products are widely accepted by society. However, market share of sustainable technologies demanding higher investments or behavioral changes is rather small, indicating that consumers seem to generally approve of sustainable products, but rarely perceive them as being compatible with their needs. The present research introduces a model emphasizing the importance of consumers perceiving the product as being suitable to match their motives relevant to the product domain. In investigating a sample of 531 car drivers, the hypothesized model was tested in the realm of a highly relevant sustainable product: electric vehicles (EVs). Findings based on structural equation modeling showed that participants' perceived matching with EV attributes was related to their domain‐specific motives (hedonic, freedom, ecological, and financial motives) and a global preference towards the product. Specific to sustainable products, ecological motives strongly influenced participants' matching processes by positively affecting perceived matching with all product attributes. Participants' purchase intentions, in turn, were strongly affected by their perceived matching of the product with their motives. The added value of the model with respect to self‐image theories and implications for sustainable product marketing are examined through discussion. Copyright © 2014 John Wiley & Sons, Ltd.     

How to identify the take-over criticality in conditionally automated driving? An examination using drivers’ physiological parameters and situational factors

Autonomous vehicles and advanced driver assistance technology are growing exponentially, and vehicles equipped with conditional automation, which has features like Traffic Jam Pilot and Highway Assist, are already available in the market. And this could expose the driver to a stressful driving condition during the takeover mission. To identify stressful takeover situations and better interact with automated systems, the relationship and effect between drivers’ physiological responses, situational factors (e.g., takeover request [TOR] lead time, takeover frequencies, and scenario types), and takeover criticality were investigated.34 participants were involved in a series of takeover events in a simulated driving environment, which are varied by different TOR lead time conditions and driving scenes. The situational factors, drivers’ skin conductance (SC), heart rate (HR), gaze behaviors, and takeover criticality ratings were collected and analyzed. The results indicated that drivers had a higher takeover criticality rating when they experienced a shorter TOR lead time level or at first to fourth take-overs. Besides, drivers who encountered a dynamic obstacle reported higher takeover criticality ratings when they were at the same Time to collision (TTC). We also observed that the takeover situations of higher criticality have larger driver’s maximum HR, mean pupil size, and maximum change in the SC (relative to the initial value of a takeover stage). Those findings of situational factors and physiological responses can provide additional support for the designing of adaptive alert systems and environmental soothing technology in conditionally automated driving, which will improve the takeover performances and drivers’ experience.