Comparing Subjective Similarity of Automated Driving Styles to Objective Distance-Based Similarity.

OBJECTIVE: This study explores subjective and objective driving style similarity to identify how similarity can be used to develop driver-compatible vehicle automation. BACKGROUND: Similarity in the ways that interaction partners perform tasks can be measured subjectively, through questionnaires, or objectively by characterizing each agent's actions. Although subjective measures have advantages in prediction, objective measures are more useful when operationalizing interventions based on these measures. Showing how objective and subjective similarity are related is therefore prudent for aligning future machine performance with human preferences. METHODS: A driving simulator study was conducted with stop-and-go scenarios. Participants experienced conservative, moderate, and aggressive automated driving styles and rated the similarity between their own driving style and that of the automation. Objective similarity between the manual and automated driving speed profiles was calculated using three distance measures: dynamic time warping, Euclidean distance, and time alignment measure. Linear mixed effects models were used to examine how different components of the stopping profile and the three objective similarity measures predicted subjective similarity. RESULTS: Objective similarity using Euclidean distance best predicted subjective similarity. However, this was only observed for participants' approach to the intersection and not their departure. CONCLUSION: Developing driving styles that drivers perceive to be similar to their own is an important step toward driver-compatible automation. In determining what constitutes similarity, it is important to (a) use measures that reflect the driver's perception of similarity, and (b) understand what elements of the driving style govern subjective similarity.

Assessing Drivers' Trust of Automated Vehicle Driving Styles With a Two-Part Mixed Model of Intervention Tendency and Magnitude.

OBJECTIVE: This study examines how driving styles of fully automated vehicles affect drivers' trust using a statistical technique-the two-part mixed model-that considers the frequency and magnitude of drivers' interventions. BACKGROUND: Adoption of fully automated vehicles depends on how people accept and trust them, and the vehicle's driving style might have an important influence. METHOD: A driving simulator experiment exposed participants to a fully automated vehicle with three driving styles (aggressive, moderate, and conservative) across four intersection types (with and without a stop sign and with and without crossing path traffic). Drivers indicated their dissatisfaction with the automation by depressing the brake or accelerator pedals. A two-part mixed model examined how automation style, intersection type, and the distance between the automation's driving style and the person's driving style affected the frequency and magnitude of their pedal depression. RESULTS: The conservative automated driving style increased the frequency and magnitude of accelerator pedal inputs; conversely, the aggressive style increased the frequency and magnitude of brake pedal inputs. The two-part mixed model showed a similar pattern for the factors influencing driver response, but the distance between driving styles affected how often the brake pedal was pressed, but it had little effect on how much it was pressed. CONCLUSION: Eliciting brake and accelerator pedal responses provides a temporally precise indicator of drivers' trust of automated driving styles, and the two-part model considers both the discrete and continuous characteristics of this indicator. APPLICATION: We offer a measure and method for assessing driving styles.

Designing for the Extremes: Modeling Drivers' Response Time to Take Back Control From Automation Using Bayesian Quantile Regression.

OBJECTIVE: Understanding the factors that affect drivers' response time in takeover from automation can help guide the design of vehicle systems to aid drivers. Higher quantiles of the response time distribution might indicate a higher risk of an unsuccessful takeover. Therefore, assessments of these systems should consider upper quantiles rather than focusing on the central tendency. BACKGROUND: Drivers' responses to takeover requests can be assessed using the time it takes the driver to take over control. However, all the takeover timing studies that we could find focused on the mean response time. METHOD: A study using an advanced driving simulator evaluated the effect of takeover request timing, event type at the onset of a takeover, and visual demand on drivers' response time. A mixed effects model was fit to the data using Bayesian quantile regression. RESULTS: Takeover request timing, event type that precipitated the takeover, and the visual demand all affect driver response time. These factors affected the 85th percentile differently than the median. This was most evident in the revealed stopped vehicle event and conditions with a longer time budget and scenes with lower visual demand. CONCLUSION: Because the factors affect the quantiles of the distribution differently, a focus on the mean response can misrepresent actual system performance. The 85th percentile is an important performance metric because it reveals factors that contribute to delayed responses and potentially dangerous outcomes, and it also indicates how well the system accommodates differences between drivers.

Naturalistic driving analysis of situational, behavioral, and psychosocial determinants of speeding.

The present analysis used full-trip naturalistic driving data along with driver behavioral and psychosocial surveys to understand the individual and contextual predictors of speeding. The data were collected over a three-week period from 44 drivers and contain 3,798 full trips, with drivers speeding 7.8 % of the time. Speeding events were identified as periods when participants traveled at a velocity greater than five mph over the speed limit for at least five seconds. Data were analyzed using the Comprehensive Driver Profile (CDP) framework which uses principal component analysis (dimensionality reduction), random forest (predictive modeling), k-means clustering (grouping and profiling), and bootstrapping (profile stability) to decompose the predictive variables and driver characteristics. The final dataset included 188 candidate independent variables from the CDP framework and one dependent variable (speeding). Nine variables emerged as significant predictors of speeding onset with an AUC of 0.88, including the percent of trip time spent idling and speeding, highway driving in low traffic conditions, and positive attitudes toward phone use. Percent of trip speeding was associated with a higher likelihood of speeding by up to 42 percent, and percent trip idling was associated with it by up to 30 percent. Driver profile clusters revealed four types: Traffic & Idling Speeders, Infrequent Speeders, Frequent Speeders, and Situational Speeders. The present analysis demonstrates the importance of situational factors and individual differences in motivating speeding behavior. Countermeasures targeting speeding may be more effective if they address the root causes of the behavior in addition to the behavior itself.

The use of adaptation to reduce simulator sickness in driving assessment and research.

The technical advancement of driving simulators has decreased their cost and increased both their accuracy and fidelity. This makes them a useful tool for examining driving behavior in risky or unique situations. With the approaching increase of older licensed drivers due to aging of the baby boomers, driving simulators will be important for conducting driving research and evaluations for older adults. With these simulator technologies, some people may experience significant effects of a unique form of motion sickness, known as simulator sickness. These effects may be more pronounced in older adults. The present study examined the feasibility of an intervention to attenuate symptoms of simulator sickness in drivers participating in a study of a driving evaluation protocol. Prior to beginning the experiment, the experimental groups did not differ in subjective simulator sickness scores as indicated by Revised Simulator Sickness Questionnaire scores (all p>0.5). Participants who experienced a two-day delay between an initial acclimation to the driving simulator and the driving session experienced fewer simulator sickness symptoms as indicated by RSSQ total severity scores than participants who did not receive a two-day delay (F(1,88)=4.54, p=.036, partial η(2)=.049). These findings have implications for improving client well-being and potentially increasing acceptance of driving simulation for driving evaluations and for driving safety research.