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1. Augmented driver behavior models for high‐fidelity simulation study of crash detection algorithms

   https://doi.org/10.1049/itr2.12373  

   Jami, Ahura ; Razzaghpour, Mahdi ; Alnuweiri, Hussein ; Fallah, Yaser P. ( May 2023 , IET Intelligent Transport Systems)

   Developing safety and efficiency applications for Connected and Automated Vehicles (CAVs) requires a great deal of testing and evaluation. The need for the operation of these systems in critical and dangerous situations makes the burden of their evaluation very costly, possibly dangerous, and time‐consuming. As an alternative, researchers attempt to study and evaluate their algorithms and designs using simulation platforms. Modeling the behavior of drivers or human operators in CAVs or other vehicles interacting with them is one of the main challenges of such simulations. While developing a perfect model for human behavior is a challenging task and an open problem, a significant augmentation of the current models used in simulators for driver behavior is presented. In this paper, a simulation framework for a hybrid transportation system is presented that includes both human‐driven and automated vehicles. In addition, the human driving task is decomposed and a modular approach is offered to simulate a large‐scale traffic scenario, allowing for a thorough investigation of automated and active safety systems. Such representation through Interconnected modules offers a human‐interpretable system that can be tuned to represent different classes of drivers. Additionally, a large driving dataset is analyzed to extract expressive parameters that would best describe different driving characteristics. Finally, a similarly dense traffic scenario is recreated within the simulator and a thorough analysis of various human‐specific and system‐specific factors is conducted, studying their effect on traffic network performance and safety.

    

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2. Continuous Authentication based on Hand Micro-movement during Smartphone Form Filling by Seated Human Subjects [Continuous Authentication based on Hand Micro-movement during Smartphone Form Filling by Seated Human Subjects]

   https://doi.org/10.5220/0010225804240431  

   Ray, Aratrika ; Hou, Daqing ; Schuckers, Stephanie ; Barbir, Abbie ( January 2021 , 7th International Conference on Information Systems Security and Privacy)

   Mobile devices typically rely on entry-point and other one-time authentication mechanisms such as a password, PIN, fingerprint, iris, or face. But these authentication types are prone to a wide attack vector and worse 1 INTRODUCTION Currently smartphones are predominantly protected a patterned password is prone to smudge attacks, and fingerprint scanning is prone to spoof attacks. Other forms of attacks include video capture and shoulder surfing. Given the increasingly important roles smartphones play in e-commerce and other operations where security is crucial, there lies a strong need of continuous authentication mechanisms to complement and enhance one-time authentication such that even if the authentication at the point of login gets compromised, the device is still unobtrusively protected by additional security measures in a continuous fashion. The research community has investigated several continuous authentication mechanisms based on unique human behavioral traits, including typing, swiping, and gait. To this end, we focus on investigating physiological traits. While interacting with hand-held devices, individuals strive to achieve stability and precision. This is because a certain degree of stability is required in order to manipulate and interact successfully with smartphones, while precision is needed for tasks such as touching or tapping a small target on the touch screen (Sitov´a et al., 2015). As a result, to achieve stability and precision, individuals tend to develop their own postural preferences, such as holding a phone with one or both hands, supporting hands on the sides of upper torso and interacting, keeping the phone on the table and typing with the preferred finger, setting the phone on knees while sitting crosslegged and typing, supporting both elbows on chair handles and typing. On the other hand, physiological traits, such as hand-size, grip strength, muscles, age, 424 Ray, A., Hou, D., Schuckers, S. and Barbir, A. Continuous Authentication based on Hand Micro-movement during Smartphone Form Filling by Seated Human Subjects. DOI: 10.5220/0010225804240431 In Proceedings of the 7th International Conference on Information Systems Security and Privacy (ICISSP 2021), pages 424-431 ISBN: 978-989-758-491-6 Copyrightc 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved still, once compromised, fail to protect the user’s account and data. In contrast, continuous authentication, based on traits of human behavior, can offer additional security measures in the device to authenticate against unauthorized users, even after the entry-point and one-time authentication has been compromised. To this end, we have collected a new data-set of multiple behavioral biometric modalities (49 users) when a user fills out an account recovery form in sitting using an Android app. These include motion events (acceleration and angular velocity), touch and swipe events, keystrokes, and pattern tracing. In this paper, we focus on authentication based on motion events by evaluating a set of score level fusion techniques to authenticate users based on the acceleration and angular velocity data. The best EERs of 2.4% and 6.9% for intra- and inter-session respectively, are achieved by fusing acceleration and angular velocity using Nandakumar et al.’s likelihood ratio (LR) based score fusion. 

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3. All-in-One Urban Mobility Mapping Application with Optional Routing Capabilities

   Thompson, R. ; Stovall, J. ; Valasquez, D. ; Anne, V. ; Samoylov, A. V. ; & Sartipi, M. ( December 2018 , Big data)

   To create safer and less congested traffic operating environments researchers at the University of Tennessee at Chattanooga (UTC) and the Georgia Tech Research Institute (GTRI) have fostered a vision of cooperative sensing and cooperative mobility. This vision is realized in a mobile application that combines visual data extracted from cameras on roadway infrastructure with a user’s coordinates via a GPS-enabled device to create a visual representation of the driving or walking environment surrounding the application user. By merging the concepts of computer vision, object detection, and mono-vision image depth calculation, this application is able to gather absolute Global Positioning System (GPS) coordinates from a user’s mobile device and combine them with relative GPS coordinates determined by the infrastructure cameras and determine the position of vehicles and pedestrians without the knowledge of their absolute GPS coordinates. The joined data is then used by an iOS mobile application to display a map showing the location of other entities such as vehicles, pedestrians, and obstacles creating a real-time visual representation of the surrounding area prior to the area appearing in the user’s visual perspective. Furthermore, a feature was implemented to display routing by using the results of a traffic scenario that was analyzed by rerouting algorithms in a simulated environment. By displaying where proximal entities are concentrated and showing recommended optional routes, users have the ability to be more informed and aware when making traffic decisions helping ensure a higher level of overall safety on our roadways. This vision would not be possible without high speed gigabit network infrastructure installed in Chattanooga, Tennessee and UTC’s wireless testbed, which was used to test many functions of this application. This network was required to reduce the latency of the massive amount of data generated by the infrastructure and vehicles that utilize the testbed; having results from this data come back in real-time is a critical component. 

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4. Does the Intelligent Driver Model Adequately Represent Human Drivers?

   https://doi.org/10.5220/0000173600003479  

   Mu, Zeyu ; Jahedinia, Fatemeh ; Park, B. Brian ( April 2023 , Proceedings of the 9th International Conference on Vehicle Technology and Intelligent Transport Systems)

   The Intelligent Driver Model (IDM) is one of the widely used car-following models to represent human drivers in mixed traffic simulations. However, the standard IDM performs too well in energy efficiency and comfort (acceleration) compared with real-world human drivers. In addition, many studies assessed the performance of automated vehicles interacting with human-driven vehicles (HVs) in mixed traffic where IDM serves as HVs based on the assumption that the IDM represents an intelligent human driver that performs not better than automated vehicles (AVs). When a commercially available control system of AVs, Adaptive Cruise Control (ACC), is compared with the standard IDM, it is found that the standard IDM generally outperforms ACC in fuel efficiency and comfort, which is not logical in an evaluation of any advanced control logic with mixed traffic. To ensure the IDM reasonably mimics human drivers, a dynamic safe time headway concept is proposed and evaluated. A real-world NGSIM data set is utilized as the human drivers for simulation-based comparisons. The results indicate that the performance of the IDM with dynamic time headway is much closer to human drivers and worse than the ACC system as expected. 

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5. Predicting Driver Takeover Decisions in Conditionally Automated Vehicles with a Gaze-based Deep Learning Model

   https://doi.org/10.1177/21695067231192914  

   Gupta, Shantanu ; Tan, Xiaomei ; Zhang, Yiqi ( October 2023 , Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   This study proposes a novel methodology for modeling driver takeover behavior in conditionally automated vehicles (AVs) when exiting a freeway using deep learning (DL) network architectures. While previous research has focused on modeling takeover time in emergency scenarios, which require quick responses, these models may not be applicable to scheduled, non-time-critical takeovers. In such situations, drivers may employ varying strategies and take longer to resume control of the vehicle when there is no time pressure. To address this problem, a deep learning architecture based on a convolutional neural network (CNN) was implemented to predict drivers’ takeover behaviors in scheduled takeovers. The model was trained on drivers’ driving data and eye gaze with varying time windows, facilitating an analysis of drivers’ takeover decisions to various takeover request designs. The model achieved good performance metrics, with an F1 Score of 0.993, a recall of 0.996, and a precision of 0.991. The application of these models holds substantial potential for refining the design of the human-machine interface, specifically in calibrating the takeover request (ToR) lead time, thereby promoting safe freeway exiting takeovers in conditionally AVs.

    

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