Older adults' acceptance of fully automated vehicles: Effects of exposure, driving style, age, and driving conditions

User comfort and naturalness of automated driving: The effect of vehicle kinematic and proxemic factors on subjective response

User comfort in higher-level Automated Vehicles (AVs, SAE Level 4+) is crucial for public acceptance. AV driving styles, characterised by vehicle kinematic and proxemic factors, affect user comfort, with "human-like" driving styles expected to provide natural feelings. We investigated a) how the kinematic and proxemic factors of an AV's driving style affect users' evaluation of comfort and naturalness, and b) how the similarities between automated and users' manual driving styles affect user evaluation. Using a motion-based driving simulator, participants experienced three Level 4 automated driving styles: two human-like (defensive, aggressive) and one machine-like. They also manually drove the same route. Participants rated their comfort and naturalness of each automated controller, across twenty-four varied UK road sections. We calculated maximum absolute values of the kinematic and proxemic factors affecting the AV's driving styles in longitudinal, lateral, and vertical directions, for each road section, to characterise the automated driving styles. The Euclidean distance between AV and manual driving styles, in terms of kinematic and proxemic factors, was calculated to characterise the human-like driving style of the AV. We used mixed-effects models to examine a) the effect of AV's kinematic and proxemic factors on the evaluation of comfort and naturalness, and b) how similarities between manual and automated driving styles affected the evaluation. Results showed significant effects of lateral and rotational kinematic factors on comfort and naturalness, with longitudinal kinematic factors having a less prominent effect. Similarities in vehicle metrics, such as speed, longitudinal jerk, lateral offset, and yaw, between manual and automated driving styles, enhanced user comfort and naturalness. This research facilitates an understanding of how control features of AVs affect user experience, contributing to the design of user-centred controllers and better acceptance of higher-level AVs.

[ADAS and automation-a relevant contribution to maintaining mobility of older drivers?]

Driving is the most important and safest form of mobility for the majority of senior citizens. However, physical and mental performance gradually decline with age, which can lead to more problems, critical situations or even accidents. Vehicle technology innovations such as advanced driver assistance systems (ADAS) have the potential to increase the road safety of older people and maintain their individual mobility for as long as possible.This overview article aims to identify ADAS that have the greatest potential to reduce the number of accidents involving older drivers. For this purpose, the accident and damage occurrence as well as the driving behaviour and compensation strategies of older people are examined in more detail. Suitable ADAS should compensate for typical driver errors, reduce information deficiencies and have a high level of acceptance. For older drivers, emergency braking, parking assistance, navigation, intersection assistance and distance speed control systems as well as systems for detecting blind spots and obstacles appear to be particularly suitable.Some of the disadvantages of ADAS are the lack of market penetration, acceptance problems and interface designs that have not yet been optimally adapted to the needs of older users. For older drivers in particular, it appears to be a priority to develop coherent and integrated solutions in the sense of cooperative assistance instead of pushing ahead with high and full automation with many system limits and exceptions, which can place high demands on attention, for example if the vehicle has to be taken over in a critical situation.

Age-related differences in takeover performance: A comparative analysis of older and younger drivers in prolonged partially automated driving

OBJECTIVE

Vehicle automation technologies have the potential to address the mobility needs of older adults. However, age-related cognitive declines may pose new challenges for older drivers when they are required to take back or "takeover" control of their automated vehicle. This study aims to explore the impact of age on takeover performance under partially automated driving conditions and the interaction effect between age and voluntary non-driving-related tasks (NDRTs) on takeover performance.

METHOD

A total of 42 older drivers (M = 65.5 years, SD = 4.4) and 40 younger drivers (M = 37.2 years, SD = 4.5) participated in this mixed-design driving simulation experiment (between subjects: age [older drivers vs. younger drivers] and NDRT engagement [road monitoring vs. voluntary NDRTs]; within subjects: hazardous event occurrence time [7.5th min vs. 38.5th min]).

RESULTS

Older drivers exhibited poorer visual exploration performance (i.e., longer fixation point duration and smaller saccade amplitude), lower use of advanced driving assistance systems (ADAS; e.g., lower percentage of time adaptive cruise control activated [ACCA]) and poorer takeover performance (e.g., longer takeover time, larger maximum resulting acceleration, and larger standard deviation of lane position) compared to younger drivers. Furthermore, older drivers were less likely to experience driving drowsiness (e.g., lower percentage of time the eyes are fully closed and Karolinska Sleepiness Scale levels); however, this advantage did not compensate for the differences in takeover performance with younger drivers. Older drivers had lower NDRT engagement (i.e., lower percentage of fixation time on NDRTs), and NDRTs did not significantly affect their drowsiness but impaired takeover performance (e.g., higher collision rate, longer takeover time, and larger maximum resulting acceleration).

CONCLUSIONS

These findings indicate the necessity of addressing the impaired takeover performance due to cognitive decline in older drivers and discourage them from engaging in inappropriate NDRTs, thereby reducing their crash risk during automated driving.

Quantification of safety improvements and human-machine tradeoffs in the transition to automated driving

The assumption of reduced human error-related crashes with increasing levels of automation in pursuing Level 5 automation lacks empirical evidence. As automation levels rise, human error-induced safety hazards are anticipated to decrease, while machine error-induced hazards will increase. However, a quantitative index capturing this tradeoff is absent. Additionally, theoretical modeling of safety improvements during the transition to automated driving remains unexplored, particularly concerning reducing human error-related hazards. These limitations impede the understanding of safety from human and machine perspectives for Automated Vehicle (AV) specialists and manufacturers. This research addresses these gaps by investigating safety performance associations between human and machine factors using the "Human-Machine conflict reduction ratio" (H/M ratio), a novel metric. The study aims to establish safety improvements related to human errors under various automation levels. Sixty participants completed driving tasks on a driving simulator at Levels 0, 4, 3, and 2. Safety performance measures, including conflict frequency and severity, were computed. As a result, Level 4 exhibits the largest decrease (93.3%) compared to manual driving, followed by Level 2 (70.7%) and Level 3 (40.5%). The H/M ratio measures the tradeoff between reducing human and machine error-induced hazards, with Level 2 demonstrating the highest ratio, followed by Levels 4 and 3. Safety performance is evaluated by considering all possible types of human errors at each automation level. Theoretical models from a human factor's perspective are employed to estimate safety improvements at each level. This research contributes to a comprehensive understanding of safety in the "human-machine cooperative driving" phase, offering insights to AV industry practitioners and stakeholders.

Personalizing automated driving speed to enhance user experience and performance in intermediate-level automated driving

In the context of high-level driving automation (SAE levels 4-5), several studies have shown that a personalized automated driving style, i.e., mimicking that of the human behind the wheel, can improve his experience. The objective of this simulator study was to examine the potential transfer of these benefits in the context of intermediate-level driving automation (SAE levels 2-3), focusing on driving speed personalization. In the first phase of the study, the driving speed of 52 participants was recorded. In the second phase, the same participants were driven by an automated car on a highway twice, and sometimes had to takeover during the drive because of a stationary vehicle on the lane. On these two drives, the automated car drove either at the same speed as them (personalized) or 20 km/h faster. The results showed that using a personalized speed driving style led to higher comfort, and that this effect was fully mediated by automated driving perceived safety. Although driving speed predicted automated driving perceived safety, this effect was actually moderated by trust in automated cars. Regarding takeover performance, the results showed that the brake use and maximum force were lower with the personalized speed driving style, leading to lower resulting maximum negative longitudinal acceleration and speed variability. Overall, the results of this study suggest that the benefits of automated driving style personalization in terms of speed extend to SAE levels 2-3. In addition to the experience benefits, this personalization approach could also improve traffic flow and safety.

Evidence of automated vehicle safety's influence on people's acceptance of the automated driving technology

Existing studies identified targeted audiences showing increases in Automated Vehicles (AV) acceptance after experiencing automated driving. However, there is still uncertainty regarding the reasons. Although some studies cited safety as the primary reason, there is no objective evidence from safety performance in verifying its impact on AV acceptance. This study contributes to the literature by quantitatively revealing why AV acceptance is changed after experiencing automated driving via a Structural Equation Modeling (SEM) method and objectively validating that safety is the primary factor in determining AV acceptance. Sixty drivers completed driving tasks on a driving simulator under Levels 0, 4, 3, and 2 and survey questions in between. As a result, the safety-related perceptions of AV were identified as reasons for affecting AV acceptance. Particularly, the evaluation of traffic conflicts and conflict severity validates the results from SEM, proving that safety is the primary and significant reason for influencing AV acceptance.

A novel autonomous vehicle interface for older adults with cognitive impairment

The population of older Americans with cognitive impairments, especially memory loss, is growing. Autonomous vehicles (AVs) have the potential to improve the mobility of older adults with cognitive impairment; however, there are still concerns regarding AVs' usability and accessibility in this population. Study objectives were to (1) better understand the needs and requirements of older adults with mild and moderate cognitive impairments regarding AVs, and (2) create a prototype for a holistic, user-friendly interface for AV interactions. An initial (Generation 1) prototype was designed based on the literature and usability principles. Based on the findings of phone interviews and focus group meetings with older adults and caregivers (n = 23), an enhanced interface (Generation 2) was developed. This generation 2 prototype has the potential to reduce the mental workload and anxiety of older adults in their interactions with AVs and can inform the design of future in-vehicle information systems for older adults.

Should my automated car drive as I do? Investigating speed preferences of drivengers in various driving conditions

Studies investigating the question of how automated cars (ACs) should drive converge to show that a personalized automated driving-style, i.e., mimicking the driving-style of the human behind the wheel, has a positive influence on various aspects of his experience (e.g., comfort). However, few studies have investigated the fact that these benefits might vary with respect to driver-related variables, such as trust in ACs, and contextual variables of the driving activity, such as weather conditions. Additionally, the context of intermediate levels of automation, such as SAE level 3, remains largely unexplored. The objective of this study was to investigate these points. In a scenario-based experimental protocol, participants were exposed to written scenarios in which a character is driven by a SAE level 3 AC in different combinations of conditions (i.e., types of roads, weather conditions and traffic congestion levels). For each condition, participants were asked to indicate how fast they would prefer their AC to drive and how fast they would manually drive in the same situation. Through analyses of variance and equivalence tests, results showed a tendency for participants to overall prefer a slightly lower AC speed than their own. However, a linear regression analysis showed that while participants with the lowest levels of trust preferred an AC speed lower than theirs, those with the highest levels preferred an AC speed nearly identical to theirs. Overall, the results of this study suggest that it would be more beneficial to implement a personalization approach for the design of automated driving-styles rather than a one for all approach.

How Older Drivers Perceive Warning Alerts? Insights for the Design of Driver-Car Interaction

The automotive industry is working toward driving automation and driver-assistance technology is becoming a norm in modern cars. Warning alert systems support the driver-car interaction and inform drivers about automation system status, upcoming obstacles, or dangers ahead. However, older drivers' needs are not always addressed in research studies, although they make up a large segment of drivers. Therefore, we conducted a qualitative three-round formative evaluation of a warning alert system using video prototypes in lab and remote settings. The goal was to evaluate visual-, sound-, and speech-based alerts based on: (a) their efficiency in informing drivers about the road situation ahead, and (b) participants' subjective opinions. We evaluated the system's efficiency using self-reported data measuring participants' cognitive load, usability, UX, and ease of use. Also, we conducted interviews to collect subjective feedback about proposed prototypes. In this article, we describe the design of warning alerts and report on their evaluation results. Our results show that speech-based warnings, especially when coupled with visual warnings, are efficient and accepted well by the participants. This article illustrates older drivers' attitude toward the use of different warning modalities in the driving context.

Age is more than just a number: The relationship among age, non-chronological age factors, self-perceived driving abilities, and autonomous vehicle acceptance

Globally, adults aged 65 and older are a rapidly-growing population. Aging is associated with declines in perceptual, cognitive, and physical abilities, which often creates challenges in completing daily activities, such as driving. Autonomous vehicles (AVs) promise to provide older adults one way to maintain their mobility and independence. However, recent surveys of AV acceptance suggest that older adults have a lower AV acceptance compared to younger generations. One challenge is that most of these assessments have not accounted for the various non-chronological age factors that contribute to how older adults perceive their own driving skills and the utility of AVs. To fill this research gap, this study investigated the effects of non-chronological age factors and rated self-perceived driving abilities on AV acceptance across three age groups. An online survey was conducted using Amazon Mechanical Turk (MTurk), for which 438 valid responses were received. Respondents were categorized into a younger (18-40 years), middle-aged (41-64 years), and older (65-79 years) adult age group. Results showed that drivers of a younger age, with higher educational attainment, who rated themselves to have higher social support, and who have lower rated self-perceived driving abilities, report being more willing to accept AVs. Findings from this work can help to inform models of AV technology acceptance and guide in the development of marketing strategies to promote knowledge of AVs.

Predictors of Simulator Sickness Provocation in a Driving Simulator Operating in Autonomous Mode

Highly autonomous vehicles (HAV) have the potential of improving road safety and providing alternative transportation options. Given the novelty of HAVs, high-fidelity driving simulators operating in an autonomous mode are a great way to expose transportation users to HAV prior to HAV adoption. In order to avoid the undesirable effects of simulator sickness, it is important to examine whether factors such as age, sex, visual processing speed, and exposure to acclimation scenario predict simulator sickness in driving simulator experiments designed to replicate the HAV experience. This study identified predictors of simulator sickness provocation across the lifespan (N = 210). Multiple stepwise backward regressions identified that slower visual processing speed predicts the Nausea and Dizziness domain with age not predicting any domains. Neither sex, nor exposure to an acclimation scenario predicted any of the four domains of simulator sickness provocation, namely Queasiness, Nausea, Dizziness, and Sweatiness. No attrition occurred in the study due to simulator sickness and thus the study suggests that high-fidelity driving simulator may be a viable way to introduce drivers across the lifespan to HAV, a strategy that may enhance future HAV acceptance and adoption.

Older Adults’ Perspectives of Smart Technologies to Support Aging at Home: Insights from Five World Café Forums

Globally, there is an urgent need for solutions that can support our aging populations to live well and reduce the associated economic, social and health burdens. Implementing smart technologies within homes and communities may assist people to live well and ‘age in place’. To date, there has been little consultation with older Australians addressing either the perceived benefits, or the potential social and ethical challenges associated with smart technology use. To address this, we conducted five World Cafés in two Australian states, aiming to capture citizen knowledge about the possibilities and challenges of smart technologies. The participants (n = 84) were aged 55 years and over, English-speaking, and living independently. Grounding our analysis in values-based social science and biomedical ethical principles, we identified the themes reflecting the participants’ understanding, resistance, and acceptance of smart technologies, and the ethical principles, including beneficence, non-maleficence, autonomy, privacy, confidentiality, and justice. Similar to other studies, many of the participants demonstrated cautious and conditional acceptance of smart technologies, while identifying concerns about social isolation, breaches of privacy and confidentiality, surveillance, and stigmatization. Attention to understanding and incorporating the values of older citizens will be important for the acceptance and effectiveness of smart technologies for supporting independent and full lives for older citizens.

A Take-Over Performance Evaluation Model for Automated Vehicles from Automated to Manual Driving

The evaluation of take-over performance and take-over safety performance is critical to improving the take-over performance of conditionally automated driving, and few studies have attempted to evaluate take-over safety performance. This study applied a binary logistic model to construct a take-over safety performance evaluation model. A take-over driving simulator was established, and a take-over simulation experiment was carried out. In the experiment, data were collected from 15 participants who took over the vehicle and performed emergency evasive maneuvers while performing non-driving-related task (NDRT). Then, to calibrate the abnormal trajectory, the Kalman filter is adopted to filter the disturbed vehicle positioning data and the belief rule-based (BRB) method is proposed to warn irregular driving behavior. The results revealed that the accident rate of male participants is higher than that of female participants in the three frequency take-over experiment, and the overall driving performance of female participants is higher than that of male participants. Meanwhile, medium and high take-over frequencies have a significant effect on the prevention of vehicle collisions. In the take-over safety performance evaluation model, the minimum time to collision (TTC) of 2.3 s is taken as the boundary between the dangerous group and the safety group, and the model prediction accuracy rate is 87.7%. In sum, this study enriches existing research on the safety performance evaluation of conditionally automated driving take-over and provides important implications for the design of driving simulators and the performance and safety evaluation of human-machine take-over.

The effects of age and physical exercise on multimodal signal responses: Implications for semi-autonomous vehicle takeover requests

The present study examined whether the non-chronological age factor, engagement in physical exercise, affected responses to multimodal (combinations of visual, auditory, and/or tactile) signals differently between younger and older adults in complex environments. Forty-eight younger and older adults were divided into exercise and non-exercise groups, and rode in a simulated Level 3 autonomous vehicle under four different task conditions (baseline, video watching, headway estimation, and video-headway combination), while being asked to respond to various multimodal warning signals. Overall, bi- and trimodal warnings had faster response times for both age groups across driving conditions, but was more pronounced for older adults. Engagement in physical exercise was associated with smaller maximum braking force for younger participants only, and also corresponded to longer average fixation durations, compared to the non-exercise group. Findings from this research can help to guide decisions about the design of warning and information systems for semi-autonomous vehicles.

The influence of experienced severe road traffic accidents on take-over reactions and non-driving-related tasks in an automated driving simulator study

Road traffic accidents (RTAs) are an ever-existing threat to all road users. Automated vehicles (AVs; SAE Level 3-5) are developed in many countries. They are promoted with numerous benefits such as increased safety yielding less RTAs, less congestion, less greenhouse gas emissions, and the possibility of enabling non-driving related tasks (NDRTs). However, there has been no study which has investigated different NDRT conditions, while comparing participants who experienced a severe RTA in the past with those who experienced no RTA. Therefore, we conducted a driving simulator study (N = 53) and compared two NDRT conditions (i.e., auditory-speech (ASD) vs. heads-up display (HUD)) and an accident (26 participants) with a non-accident group (27; between-subjects design). Although our results did not reveal any interaction effect, and no group difference between the accident and the non-accident group on NDRT, take-over request (TOR), and driving performance, we uncovered for both groups better performances for the HUD condition, whereas a lower cognitive workload was reported for the ASD condition. Nevertheless, there was no difference for technology trust between the two conditions. Albeit we observed higher self-ratings of PTSD symptoms for the accident than for the non-accident group, there were no group differences on depression and psychological resilience self-ratings. We conclude that severe RTA experiences do not undermine NDRT, TOR, and driving performance in a SAE Level 3 driving simulator study, although PTSD symptoms after an RTA may affect the psychological wellbeing.

Influence of traffic context and information presentation on evaluation of autonomous highway journeys

Previous research into perceptions of autonomous vehicles has largely focused on a priori attitudes, with little work on the perception of specific traffic situations, context and driving styles. The present study used three simulator experiments (total N = 150) to examine the combined effects of vehicle speed, lane position, information presentation and traffic context on occupants' levels of satisfaction with autonomous highway journeys. Overall, occupants preferred being in a vehicle that was mostly overtaking compared to being overtaken, regardless of whether the overtaking vehicles were exceeding the speed limit. This finding remained even when occupants were given additional reminders that they themselves were travelling at an appropriate speed (Experiments 1 & 2). Experiment 3 found that occupants preferred overtaking to being overtaken when following another car, but this preference disappeared when they were following a lorry, suggesting that occupants' sensitivity to position amongst the traffic was partially context dependent. Overall, the findings suggest that journey satisfaction is sensitive to overtaking contexts and the inappropriate behaviour of other drivers (e.g., speeding) can reduce journey satisfaction for occupants in autonomous vehicles that drive within the speed limit, depending on the specific traffic situation. Potential implications for the integration of autonomous vehicles with other traffic and the need for in-vehicle presentation of information are discussed.