Autonomous vehicles (AVs) are closer to becoming a reality in changing the landscape of commercial and
personal transportation. The launch of these vehicles come with the promise of improved road safety, reduced
traffic fatalities, and enhanced mobility. However, there are questions as to whether the design of AVs will
meet the needs of everyone, including people with disabilities and older adults. We argue that there exists
no conceptual model that guide sthe inclusive design of autonomous vehicles to benefit all intended users.
This paper proposes such a model, called the User Transportation-Activity Technology (UTT) model, which
supports the inclusive design of AVs. We present a review of current models of assistive technology design
and their drawbacks followed by an introduction of the UTT model and its application in AV design. This
paper may benefit researchers, designers, and developers of autonomous vehicles interested in addressing
accessible design issues in such vehicles.
more »
« less
Lights, Camera, Autonomy! Exploring the Opinions of Older Adults Regarding Autonomous Vehicles Through Enactment
Autonomous vehicles (AV), one of the transportation industry’s biggest innovations of the past few decades,
bring the promise of safer roads and significantly lower vehicle-related fatalities. While many studies have
found largely positive consumer opinions regarding operating and owning such a vehicle, older adults (55+)
tend to express concerns about the safety and operational risks of a vehicle with unknown capabilities. To
investigate how older adults and AVs may interact, we conducted an improv- style enactment-based participatory design pilot study. We found that initial concerns about trust and safety can be diminished through
training and repetitive successful vehicle operation. Additionally, our participants provided insights into the
AV design considerations, needs, and interactions for older adults. These findings add to the collective body
of autonomous vehicle research by demonstrating that the needs of this growing population, who may benefit
significantly from access to AVs, should be considered by manufacturers.
more »
« less
- Award ID(s):
- 1849924
- NSF-PAR ID:
- 10328668
- Date Published:
- Journal Name:
- Proceedings of the Human Factors and Ergonomics Society Annual Meeting
- ISSN:
- 2169-5067
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Autonomous Vehicles (AVs) are an emerging and highly impactful technology on today's roads. When assessing the performance of AVs, it is useful to study their improvement relative to common metrics such as fuel economy/emissions, safety, and congestion. But metrics of the vehicle's performance alone may not be complete; an AV that is affecting and reacting to a smart traffic light, for example, may improve its own performance, but may cause the same intersection to degrade the performance of other vehicles around the AV. Similar concerns arise in nearly all AV topics: platooning, light pre-emption, lane tracking, etc. Thus, the assessment of the vehicle's impacts on surrounding traffic is important, possibly even more important than the improvements enabled on the AV alone. But what boundary, or factors, define the vehicles, equipment, etc. “surrounding” an AV? The goal of this work is to characterize the boundary of vehicles “surrounding” an AV, referred to as Region of Influence, or ROI. Specifically, this work focuses on the problem that considering a perturbation is exerted into a traffic system, how far in time and space the perturbation from an AV’s decision can influence the surrounding system’s behavior. To achieve the goal, we utilized AIMSUN, a microscopic traffic simulator, to perform baseline and perturbed simulations. The ROI was evaluated by comparing trajectories of traffic surrounding the ego vehicle using different metrics, including difference in trajectories, Euclidian distance, rate of change of Euclidian distance, total number of lane changes over the whole simulation space versus time and total number of lane changes over the whole simulation time versus distance to ego vehicle. The results show that the ROI can be viewed from different perspectives using these metrics, and it is dependent on speed variance of the traffic.more » « less
-
A. Ghate, K. Krishnaiyer (Ed.)Deaths due to road traffic accidents are one of the leading causes of death in the United States. Furthermore, the economic impact of road traffic accidents accounts for about 3% of the United States' annual gross domestic product (GDP). In the past decade, extensive research has focused on autonomous vehicles (AVs). This technology is said to help prevent traffic accidents while promoting road traffic safety. This study aims to investigate the safety performance of AVs and identify the significant risk factors associated with the AV collisions. The study considers more than 200 crashes involving AVs and includes vehicle factors, environmental factors, collision type and crash severity. Multinomial logistic regression was conducted with collision type. The results showed no statistically significant risk factors to crash severity. However, movement preceding to collision contributes significantly to collision type. When both vehicles are moving, there's a higher likelihood of an angled collision, 47% to be exact. The other scenario which demonstrates a high probability of an angled collision is when the AV vehicle is not moving while the other is moving. The highest probability for a rear-end collision to occur is when the AV vehicle is not moving while the other is moving. This scenario makes up 55% of the entire rear-end collisions. As for the second-highest proportion, both vehicles moving, it consists of 42%. The research shall help reduce AV involved collisions and increase driving safety.more » « less
-
null (Ed.)In this paper we provide a proof of principle of a new method for addressing the ethics of autonomous vehicles (AVs), the Data-Theories Method, in which vehicle crash data is combined with philosophical ethical theory to provide a guide to action for AV algorithm design. We use this method to model three scenarios in which an AV is exposed to risk on the road, and determine possible actions for the AV. We then examine how different philosophical perspectives on agent partiality, or the degree to which one can act in one’s own self-interest, might address each scenario. This method shows why modelling the ethics of AVs using data is essential. First, AVs may sometimes have options that human drivers do not, and designing AVs to mimic the most ethical human driver would not ensure that they do the right thing. Second, while ethical theories can often disagree about what should be done, disagreement can be reduced and compromises found with a more complete understanding of the AV’s choices and their consequences. Finally, framing problems around thought experiments may elicit preferences that are divergent with what individuals might prefer once they are provided with information about the real risks for a scenario. Our method provides a principled and empirical approach to productively address these problems and offers guidance on AV algorithm design.more » « less
-
null (Ed.)Autonomous Vehicle (AV) technology has the potential to significantly improve driver safety. Unfortunately, driver could be reluctant to ride with AVs due to the lack of trust and acceptance of AV’s driving styles. The present study investigated the impact of driver’s driving style (aggressive/defensive) and the designed driving styles of AVs (aggressive/defensive) on driver’s trust, acceptance, and take-over behavior in fully autonomous vehicles. Thirty-two participants were classified into two groups based on their driving styles using the Aggressive Driving Scale and experienced twelve scenarios in either an aggressive AV or a defensive AV. Results revealed that drivers’ trust, acceptance, and takeover frequency were significantly influenced by the interaction effects between AV’s driving style and driver’s driving style. The findings implied that driver’s individual differences should be considered in the design of AV’s driving styles to enhance driver’s trust and acceptance of AVs and reduce undesired take over behaviors.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
