More Like this

1. Fostering Drivers’ Trust in Automated Driving Styles: The Role of Driver Perception of Automated Driving Maneuvers

   https://doi.org/10.1177/00187208231189661  

   Ma, Zheng ; Zhang, Yiqi ( July 2023 , Human Factors: The Journal of the Human Factors and Ergonomics Society)

   This study investigated the impact of driving styles of drivers and automated vehicles (AVs) on drivers’ perception of automated driving maneuvers and quantified the relationships among drivers’ perception of AV maneuvers, driver trust, and acceptance of AVs.

   Previous studies on automated driving styles focused on the impact of AV’s global driving style on driver’s attitude and driving performance. However, research on drivers’ perception of automated driving maneuvers at the specific driving style level is still lacking.

   Sixteen aggressive drivers and sixteen defensive drivers were recruited to experience twelve driving scenarios in either an aggressive AV or a defensive AV on the driving simulator. Their perception of AV maneuvers, trust, and acceptance was measured via questionnaires, and driving performance was collected via the driving simulator.

   Results revealed that drivers’ trust and acceptance of AVs would decrease significantly if they perceived AVs to have a higher speed, larger deceleration, smaller deceleration, or shorter stopping distance than expected. Moreover, defensive drivers perceived significantly greater inappropriateness of these maneuvers from aggressive AVs than defensive AVs, whereas aggressive drivers didn’t differ significantly in their perceived inappropriateness of these maneuvers with different driving styles.

   The driving styles of automated vehicles and drivers influenced drivers’ perception of automated driving maneuvers, which influence their trust and acceptance of AVs.

   This study suggested that the design of AVs should consider drivers’ perceptions of automated driving maneuvers to avoid undermining drivers’ trust and acceptance of AVs.

    

   more » « less
2. Boring But Demanding: Using Secondary Tasks to Counter the Driver Vigilance Decrement for Partially Automated Driving

   https://doi.org/10.1177/00187208231168697  

   Mishler, Scott ; Chen, Jing ( May 2023 , Human Factors: The Journal of the Human Factors and Ergonomics Society)

   Objective We investigated secondary–task–based countermeasures to the vigilance decrement during a simulated partially automated driving (PAD) task, with the goal of understanding the underlying mechanism of the vigilance decrement and maintaining driver vigilance in PAD. Background Partial driving automation requires a human driver to monitor the roadway, but humans are notoriously bad at monitoring tasks over long periods of time, demonstrating the vigilance decrement in such tasks. The overload explanations of the vigilance decrement predict the decrement to be worse with added secondary tasks due to increased task demands and depleted attentional resources, whereas the underload explanations predict the vigilance decrement to be alleviated with secondary tasks due to increased task engagement. Method Participants watched a driving video simulating PAD and were required to identify hazardous vehicles throughout the 45-min drive. A total of 117 participants were assigned to three different vigilance-intervention conditions including a driving-related secondary task (DR) condition, a non-driving-related secondary task (NDR) condition, and a control condition with no secondary tasks. Results Overall, the vigilance decrement was shown over time, reflected in increased response times, reduced hazard detection rates, reduced response sensitivity, shifted response criterion, and subjective reports on task-induced stress. Compared to the DR and the control conditions, the NDR displayed a mitigated vigilance decrement. Conclusion This study provided convergent evidence for both resource depletion and disengagement as sources of the vigilance decrement. Application The practical implication is that infrequent and intermittent breaks using a non-driving related task may help alleviate the vigilance decrement in PAD systems. 

   more » « less
3. Similarity in spatial structure constrains ecosystem relationships: Building a macroscale understanding of lakes

   https://doi.org/10.1111/geb.12781  

   Lapierre, Jean‐Francois ; Collins, Sarah M. ; Seekell, David A. ; Spence Cheruvelil, Kendra ; Tan, Pang‐Ning ; Skaff, Nicholas K. ; Taranu, Zofia E. ; Fergus, C. Emi ; Soranno, Patricia A. ( September 2018 , Global Ecology and Biogeography)

   We aimed to measure the dominant spatial patterns in ecosystem properties (such as nutrients and measures of primary production) and the multi‐scaled geographical driver variables of these properties and to quantify how the spatial structure of pattern in all of these variables influences the strength of relationships among them.

   We studied > 8,500 lakes in a 1.8 million km²area of Northeast U.S.A. Data comprised 10‐year medians (2002–2011) for measured ecosystem properties, long‐term climate averages and recent land use/land cover variables.

   We focused on ecosystem properties at the base of aquatic food webs, including concentrations of nutrients and algal pigments that are proxies of primary productivity.

   We quantified spatial structure in ecosystem properties and their geographical driver variables using distance‐based Moran eigenvector maps (dbMEMs). We then compared the similarity in spatial structure for all pairs of variables with the correlation between variables to illustrate how spatial structure constrains relationships among ecosystem properties.

   The strength of spatial structure decreased in order for climate, land cover/use, lake ecosystem properties and lake and landscape morphometry. Having a comparable spatial structure is a necessary condition to observe a strong relationship between a pair of variables, but not a sufficient one; variables with very different spatial structure are never strongly correlated. Lake ecosystem properties tended to have an intermediary spatial structure compared with that of their main drivers, probably because climate and landscape variables with known ecological links induce spatial patterns.

   Our empirical results describe inherent spatial constraints that dictate the expected relationships between ecosystem properties and their geographical drivers at macroscales. Our results also suggest that understanding the spatial scales at which ecological processes operate is necessary to predict the effects of multi‐scaled environmental changes on ecosystem properties.

    

   more » « less
4. Real-Time Trust Prediction in Conditionally Automated Driving Using Physiological Measures

   https://doi.org/10.1109/TITS.2023.3295783  

   Ayoub, Jackie ; Avetisian, Lilit ; Yang, X. Jessie ; Zhou, Feng ( December 2023 , IEEE Transactions on Intelligent Transportation Systems)

   Trust calibration poses a significant challenge in the interaction between drivers and automated vehicles (AVs) in the context of human-automation collaboration. To effectively calibrate trust, it becomes crucial to accurately measure drivers’ trust levels in real time, allowing for timely interventions or adjustments in the automated driving. One viable approach involves employing machine learning models and physiological measures to model the dynamic changes in trust. This study introduces a technique that leverages machine learning models to predict drivers’ real-time dynamic trust in conditional AVs using physiological measurements. We conducted the study in a driving simulator where participants were requested to take over control from automated driving in three conditions that included a control condition, a false alarm condition, and a miss condition. Each condition had eight takeover requests (TORs) in different scenarios. Drivers’ physiological measures were recorded during the experiment, including galvanic skin response (GSR), heart rate (HR) indices, and eye-tracking metrics. Using five machine learning models, we found that eXtreme Gradient Boosting (XGBoost) performed the best and was able to predict drivers’ trust in real time with an f1-score of 89.1% compared to a baseline model of K -nearest neighbor classifier of 84.5%. Our findings provide good implications on how to design an in-vehicle trust monitoring system to calibrate drivers’ trust to facilitate interaction between the driver and the AV in real time. 

   more » « less
5. Driver-Automated Vehicle Interaction in Mixed Traffic: Types of Interaction and Drivers’ Driving Styles

   https://doi.org/10.1177/00187208221088358  

   Ma, Zheng ; Zhang, Yiqi ( April 2022 , Human Factors: The Journal of the Human Factors and Ergonomics Society)

   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