Safety through Disruption (SAFE-D) University Transportation Center (UTC)

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Browsing Safety through Disruption (SAFE-D) University Transportation Center (UTC) by Author "Alambeigi, Hananeh"

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    Data Mining Twitter to Improve Automated Vehicle Safety
    McDonald, Anthony D.; Huang, Bert; Wei, Ran; Alambeigi, Hananeh; Arachie, Chidubem; Smith, Alexander Charles; Jefferson, Jacelyn (SAFE-D: Safety Through Disruption National University Transportation Center, 2021-02)
    Automated vehicle (AV) technologies may significantly improve driving safety, but only if they are widely adopted and used appropriately. Adoption and appropriate use are influenced by user expectations, which are increasingly being driven by social media. In the context of AVs, prior studies have observed that major news events such as crashes and technology announcements influence user responses to AVs; however, the exact impact and dynamics of this influence are not well understood. The goals of this project were to develop a novel search method to identify AV-relevant user comments on Twitter, mine these tweets to understand the influence of crashes and news events on user sentiment about AVs, and finally translate these findings into a set of guidelines for reporting about AV crashes. In service of these goals, we developed a novel semi-supervised constrained-level learning machine search approach to identify relevant tweets and demonstrated that it outperformed alternative methods. We used the relevant tweets identified to develop a topic model of AV events which illustrated that crashes, fault and safety, and technology companies were the most discussed topics following major events. While the sentiment among these topics was mostly neutral, tweets about crashes and fault and safety were negatively biased. We combined these findings with a series of interviews with Public Information Officers to develop a set of five basic guidelines for AV communication. These guidelines should aid proper public calibration and subsequent acceptance and use of AVs.
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    Identifying Deviations from Normal Driving Behavior
    Alambeigi, Hananeh; McDonald, Anthony D.; Shipp, Eva; Manser, Michael (SAFE-D: Safety Through Disruption National University Transportation Center, 2022-01)
    One of the critical circumstances in automated vehicle driving is transition of control between partially automated vehicles and drivers. One approach to enhancing the design of transition of control is to predict driver behavior during a takeover by analyzing a driver’s state before the takeover occurs. Although there is a wealth of existing driver behavior model prediction literature, little is known regarding takeover performance prediction (e.g., driver error) and its underlying data structure (e.g., window size). Thus, the goal of this study is to predict driver error after a takeover event using supervised machine learning algorithms on various window sizes. Three machine learning algorithms—decision tree, random forest, and support vector machine with a radial basis kernel—were applied to driving performance, physiological, and glance data from a driving simulator experiment examining automated vehicle driving. The results showed that a random forest algorithm with an area under the receiver operating curve of 0.72, trained on a 3 s window before the takeover time, had the highest performance for accurately classifying driver error. In addition, we identified the 10 most critical predictors that resulted in the best error prediction performance. The results of this study can be beneficial for developing driver state algorithms that could be integrated into automated driving systems.
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    Modeling Driver Behavior During Automated Vehicle Platooning Failures
    McDonald, Anthony D.; Sarkar, Abhijit; Hickman, Jeffrey S.; Alambeigi, Hananeh; Vogelpohl, Tobias; Markkula, Gustav (SAFE-D: Safety Through Disruption National University Transportation Center, 2021-01)
    Automated vehicles (AVs) promise to revolutionize driving safety. Driver models can aid in achieving this promise by providing a tool for designers to ensure safe interactions between human drivers and AVs. In this project, we performed a literature review to identify important factors for AV takeover safety and promising models to capture these factors. We also conducted a driving simulation experiment to address a research gap in silent automation failures. Finally, we developed a series of models to predict driver decision-making, braking,and steering responses using crash/near-crash data from the SHRP 2 naturalistic driving study and a driving simulation experiment. The analyses highlight the importance of visual parameters (in particular, visual looming) in driver responses and interactions with AVs. The modeling analysis suggested that models based on visual looming captured driver responses better than traditional baseline reaction time and closed-loop models. Further,the analysis of SHRP 2 data showed that gaze eccentricity of the last glance plays a critical role in driver decision-making. With further development, including the integration of important factors in takeover performance identified in the literature review and refinement of the role of gaze eccentricity, these models could be a valuable tool for AV software designers.