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1. The Impact of Cybersecurity Attacks on Human Trust in Autonomous Vehicle Operations

   https://doi.org/10.1177/00187208241283321  

   Lim, Cherin ; Prendez, David ; Boyle, Linda_Ng ; Rajivan, Prashanth ( September 2024 , Human Factors: The Journal of the Human Factors and Ergonomics Society)

   This study examines the extent to which cybersecurity attacks on autonomous vehicles (AVs) affect human trust dynamics and driver behavior.

   Human trust is critical for the adoption and continued use of AVs. A pressing concern in this context is the persistent threat of cyberattacks, which pose a formidable threat to the secure operations of AVs and consequently, human trust.

   A driving simulator experiment was conducted with 40 participants who were randomly assigned to one of two groups: (1) Experience and Feedback and (2) Experience-Only. All participants experienced three drives: Baseline, Attack, and Post-Attack Drive. The Attack Drive prevented participants from properly operating the vehicle in multiple incidences. Only the “Experience and Feedback” group received a security update in the Post-Attack drive, which was related to the mitigation of the vehicle’s vulnerability. Trust and foot positions were recorded for each drive.

   Findings suggest that attacks on AVs significantly degrade human trust, and remains degraded even after an error-less drive. Providing an update about the mitigation of the vulnerability did not significantly affect trust repair.

   Trust toward AVs should be analyzed as an emergent and dynamic construct that requires autonomous systems capable of calibrating trust after malicious attacks through appropriate experience and interaction design.

   The results of this study can be applied when building driver and situation-adaptive AI systems within AVs.

    

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2. Active Inference Models of AV Takeovers: Relating Model Parameters to Trust, Situation Awareness, and Fatigue

   https://doi.org/10.1177/00187208241295932  

   Wei, Ran ; McDonald, Anthony_D ; Mehta, Ranjana_K ; Garcia, Alfredo ( November 2024 , Human Factors: The Journal of the Human Factors and Ergonomics Society)

   Our objectives were to assess the efficacy of active inference models for capturing driver takeovers from automated vehicles and to evaluate the links between model parameters and self-reported cognitive fatigue, trust, and situation awareness.

   Control transitions between human drivers and automation pose a substantial safety and performance risk. Models of driver behavior that predict these transitions from data are a critical tool for designing safer, human-centered, systems but current models do not sufficiently account for human factors. Active inference theory is a promising approach to integrate human factors because of its grounding in cognition and translation to a quantitative modeling framework.

   We used data from a driving simulation to develop an active inference model of takeover performance. After validating the model’s predictions, we used Bayesian regression with a spike and slab prior to assess substantial correlations between model parameters and self-reported trust, situation awareness, fatigue, and demographic factors.

   The model accurately captured driving takeover times. The regression results showed that increases in cognitive fatigue were associated with increased uncertainty about the need to takeover, attributable to mapping observations to environmental states. Higher situation awareness was correlated with a more precise understanding of the environment and state transitions. Higher trust was associated with increased variance in environmental conditions associated with environmental states.

   The results align with prior theory on trust and active inference and provide a critical connection between complex driver states and interpretable model parameters.

   The active inference framework can be used in the testing and validation of automated vehicle technology to calibrate design parameters to ensure safety.

    

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3. Driver Situation Awareness for Regaining Control from Conditionally Automated Vehicles: A Systematic Review of Empirical Studies

   https://doi.org/10.1177/00187208241272071  

   Tan, Xiaomei ; Zhang, Yiqi ( August 2024 , Human Factors: The Journal of the Human Factors and Ergonomics Society)

   An up-to-date and thorough literature review is needed to identify factors that influence driver situation awareness (SA) during control transitions in conditionally automated vehicles (AV). This review also aims to ascertain SA components required for takeovers, aiding in the design and evaluation of human–vehicle interfaces (HVIs) and the selection of SA assessment methodologies.

   Conditionally AVs alleviate the need for continuous road monitoring by drivers yet necessitate their reengagement during control transitions. In these instances, driver SA is crucial for effective takeover decisions and subsequent actions. A comprehensive review of influential SA factors, SA components, and SA assessment methods will facilitate driving safety in conditionally AVs but is still lacking.

   A systematic literature review was conducted. Thirty-four empirical research articles were screened out to meet the criteria for inclusion and exclusion.

   A conceptual framework was developed, categorizing 23 influential SA factors into four clusters: task/system, situational, individual, and nondriving-related task factors. The analysis also encompasses an examination of pertinent SA components and corresponding HVI designs for specific takeover events, alongside an overview of SA assessment methods for conditionally AV takeovers.

   The development of a conceptual framework outlining influential SA factors, the examination of SA components and their suitable design of presentation, and the review of SA assessment methods collectively contribute to enhancing driving safety in conditionally AVs.

   This review serves as a valuable resource, equipping researchers and practitioners with insights to guide their efforts in evaluating and enhancing driver SA during conditionally AV takeovers.

    

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4. Agent Based Resilient Transportation Infrastructure With Surrogate Adaptive Networks

   https://doi.org/10.1115/DETC2020-22568  

   Apostol, Andre A. ; Turner, Cameron J. ( August 2020 , c. ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference)

   Connected autonomous intelligent agents (AIA) can improve intersection performance and resilience for the transportation infrastructure. An agent is an autonomous decision maker whose decision making is determined internally but may be altered by interactions with the environment or with other agents. Implementing agent-based modeling techniques to advance communication for more appropriate decision making can benefit autonomous vehicle technology.

   This research examines vehicle to vehicle (V2V), vehicle to infrastructure (V2I), and infrastructure to infrastructure (I2I) communication strategies that use gathered data to ensure these agents make appropriate decisions under operational circumstances. These vehicles and signals are modeled to adapt to the common traffic flow of the intersection to ultimately find an traffic flow that will minimizes average vehicle transit time to improve intersection efficiency. By considering each light and vehicle as an agent and providing for communication between agents, additional decision-making data can be transmitted. Improving agent based I2I communication and decision making will provide performance benefits to traffic flow capacities.

    

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5. 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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