Objective This study investigated drivers’ subjective feelings and decision making in mixed traffic by quantifying driver’s driving style and type of interaction. Background Human-driven vehicles (HVs) will share the road with automated vehicles (AVs) in mixed traffic. Previous studies focused on simulating the impacts of AVs on traffic flow, investigating car-following situations, and using simulation analysis lacking experimental tests of human drivers. Method Thirty-six drivers were classified into three driver groups (aggressive, moderate, and defensive drivers) and experienced HV-AV interaction and HV-HV interaction in a supervised web-based experiment. Drivers’ subjective feelings and decision making were collected via questionnaires. Results Results revealed that aggressive and moderate drivers felt significantly more anxious, less comfortable, and were more likely to behave aggressively in HV-AV interaction than in HV-HV interaction. Aggressive drivers were also more likely to take advantage of AVs on the road. In contrast, no such differences were found for defensive drivers indicating they were not significantly influenced by the type of vehicles with which they were interacting. Conclusion Driving style and type of interaction significantly influenced drivers’ subjective feelings and decision making in mixed traffic. This study brought insights into how human drivers perceive and interact with AVs and HVs on the road and how human drivers take advantage of AVs. Application This study provided a foundation for developing guidelines for mixed transportation systems to improve driver safety and user experience.
more »
« less
Investigating the Effects of Automated Driving Styles and Driver’s Driving Styles on Driver Trust, Acceptance, and Take Over Behaviors
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
- Award ID(s):
- 1850002
- PAR ID:
- 10215578
- Date Published:
- Journal Name:
- Proceedings of the Human Factors and Ergonomics Society Annual Meeting
- Volume:
- 64
- Issue:
- 1
- ISSN:
- 2169-5067
- Page Range / eLocation ID:
- 2001 to 2005
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
ObjectiveThis study explores subjective and objective driving style similarity to identify how similarity can be used to develop driver-compatible vehicle automation. BackgroundSimilarity 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. MethodsA 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. ResultsObjective similarity using Euclidean distance best predicted subjective similarity. However, this was only observed for participants’ approach to the intersection and not their departure. ConclusionDeveloping 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.more » « less
-
In highly and fully automated vehicles (AV), drivers could divert their attention to non-driving-related activities. Drivers may also take over AVs if they do not trust the way AVs drive in specific driving scenarios. Existing models have been developed to predict drivers’ takeover performance in responding to takeover requests initiated by AVs in semi-AVs. However, few models predicted driver-initiated takeover behavior in highly and fully AVs. The present study develops an attention-based multiple-input Convolutional Neural Network (CNN) to predict drivers’ takeover intention in fully AVs. The results indicated that the developed model successfully predicted takeover intentions of drivers with a precision of 0.982 and an F1-Score of.989, which were found to be substantially higher than other machine learning algorithms. The developed CNN model could be applied in improving the driving algorithms of the AV by considering drivers’ driving styles to reduce drivers’ unnecessary takeover behaviors.more » « less
-
Autonomous vehicles are expected to improve road safety and efficiency in future transportation systems. A driving simulator study was designed to identify driving styles and the cooperation between human drivers and other AVs. The study captured driver’s following behavior in a fully autonomous driving environment at unsignalized intersections. Participants were asked to make a series of maneuvers (straight through intersection, left turn, and right turn) in two different speed conditions (30, 40 mph) and two different traffic density conditions (with or without other traffic). Analysis of Variance showed that drivers had a significantly larger deviation (defined as the area between two trajectories) during left turn maneuvers when they were traveling at higher speeds. Moreover, the first turning operation had smaller deviation than the second turning operation. The findings have implications for the design of driver-assistance guidance systems in future mixed autonomous and non-autonomous traffic flows.more » « less
-
Zonta, Daniele; Su, Zhongqing; Glisic, Branko (Ed.)Recent developments in autonomous vehicle (AV) or connected AVs (CAVs) technology have led to predictions that fully self-driven vehicles could completely change the transportation network over the next decades. However, at this stage, AVs and CAVs are still in the development stage which requires various trails in the field and machine learning through autonomous driving miles on real road networks. Until the complete market adoption of autonomous technology, a long transition period of coexistence between conventional and autonomous cars would exist. It is important to study and develop the expected driving behavior of future autonomous cars and the traffic simulation platforms provide an opportunity for researchers and technology developers to implement and assess the different behaviors of self-driving vehicle technology before launching it to the actual ground. This study utilizes PTV VISSIM microsimulation platform to evaluate the mobility performance of unmanned vehicles at a 4-way signalized traffic intersection. The software contains three different AV-ready driving logics such as AV-cautious, AV-normal, and AV-aggressive which were tested against the performance of the conventional vehicles, and the results of the study revealed that the overall network operational performance improves with the progressive introduction of AVs using AV-normal, and AV-aggressive driving behaviors while the AV-cautious driving behavior stays conservative and deteriorates the traffic performance.more » « less
An official website of the United States government

