Search

The assumption of reduced human error-related crashes with increasing levels of automation in pursuing Level 5 automation lacks empirical evidence. As automation levels rise, human error-induced safety hazards are anticipated to decrease, while machine error-induced hazards will increase. However, a quantitative index capturing this tradeoff is absent. Additionally, theoretical modeling of safety improvements during the transition to automated driving remains unexplored, particularly concerning reducing human error-related hazards. These limitations impede the understanding of safety from human and machine perspectives for Automated Vehicle (AV) specialists and manufacturers. This research addresses these gaps by investigating safety performance associations between human and machine factors using the "Human-Machine conflict reduction ratio" (H/M ratio), a novel metric. The study aims to establish safety improvements related to human errors under various automation levels. Sixty participants completed driving tasks on a driving simulator at Levels 0, 4, 3, and 2. Safety performance measures, including conflict frequency and severity, were computed. As a result, Level 4 exhibits the largest decrease (93.3%) compared to manual driving, followed by Level 2 (70.7%) and Level 3 (40.5%). The H/M ratio measures the tradeoff between reducing human and machine error-induced hazards, with Level 2 demonstrating the highest ratio, followed by Levels 4 and 3. Safety performance is evaluated by considering all possible types of human errors at each automation level. Theoretical models from a human factor's perspective are employed to estimate safety improvements at each level. This research contributes to a comprehensive understanding of safety in the "human-machine cooperative driving" phase, offering insights to AV industry practitioners and stakeholders., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Existing studies identified targeted audiences showing increases in Automated Vehicles (AV) acceptance after experiencing automated driving. However, there is still uncertainty regarding the reasons. Although some studies cited safety as the primary reason, there is no objective evidence from safety performance in verifying its impact on AV acceptance. This study contributes to the literature by quantitatively revealing why AV acceptance is changed after experiencing automated driving via a Structural Equation Modeling (SEM) method and objectively validating that safety is the primary factor in determining AV acceptance. Sixty drivers completed driving tasks on a driving simulator under Levels 0, 4, 3, and 2 and survey questions in between. As a result, the safety-related perceptions of AV were identified as reasons for affecting AV acceptance. Particularly, the evaluation of traffic conflicts and conflict severity validates the results from SEM, proving that safety is the primary and significant reason for influencing AV acceptance., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Automated vehicles (AV) testing on the public roads is ongoing in several states in the US as well as in Europe and Asia. As long as the automated vehicle technology has not achieved full automation (Level 5), human drivers are still expected to take over the steering wheel and throttles when there is an automated vehicle disengagement. However, contributing factors and the mechanism about automated vehicle-initiated disengagement has not been quantitatively and comprehensively explored and investigated due to the lack of field test data. Besides, understanding human drivers' perception and promptness of reaction to the AV disengagement is essential to ensure safety transition between automated and manual driving. By harnessing California's Autonomous Vehicle Disengagement Report Database, which includes the AV disengagement data from field tests in 2016-2017, this paper quantitatively investigated the AV disengagement using multiple statistical modeling approaches that involve statistical modeling and classification tree. Specifically, the paper identifies the contributing factors impacting human drivers' promptness to AV disengagements, and quantitatively investigates the underlying causes to AV disengagements. Results indicate that current AV disengagement on public roads is dominated by causes due to a planning issue. The cause of an AV disengagement is significantly induced by lacking certain numbers of radar and LiDAR sensors installed on the automated vehicles. These thresholds of these sensors needed are revealed. Cause of disengagement and roadway characteristics significantly impact drivers' take-over time when facing an AV disengagement. AV perception or control issue-based disengagement can significantly extend drivers' perception-reaction time to take over the driving. The quantitative knowledge obtained ultimately facilitates revealing the mechanisms of the automated vehicle disengagements to ensure safe AV operations on public roads., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Autonomous Vehicles (AV) technology is emerging. Field tests on public roads have been on going in several states in the US as well as in Europe and Asia. During the US public road tests, crashes with AV involved happened, which becomes a concern to the public. Most previous studies on AV safety relied heavily on assessing drivers' performance and behaviors in a simulation environment and developing automated driving system performance in a closed field environment. However, contributing factors and the mechanism of AV-related crashes have not been comprehensively and quantitatively investigated due to the lack of field AV crash data. By harnessing California's Report of Traffic Collision Involving an Autonomous Vehicle Database, which includes the AV crash data from 2014 to 2018, this paper investigates by far the most current and complete AV crash database in the US using statistical modeling approaches that involve both ordinal logistic regression and CART classification tree. The quantitative analysis based on ordinal logistic regression and CART models has successfully explored the mechanism of AV-related crash, via both perspectives of crash severity and collision types. Particularly, the CART model reveals and visualize the hierarchical structure of the AV crash mechanism with knowledge of how these traffic, roadway, and environmental contributing factors can lead to crashes of various serveries and collision types. Statistical analysis results indicate that crash severity significantly increases if the AV is responsible for the crash. The highway is identified as the location where severe injuries are likely to happen. AV collision types are affected by whether the vehicle is on automated driving mode, whether the crashes involve pedestrians/cyclists, as well as the roadway environment. The method used in this research provides a proven approach to statistically analyze and understand AV safety issues. And this benefit is potential be even enhanced with an increasing sample size of AV-related crashes records in the future. The comprehensive knowledge obtained ultimately facilitates assessing and improving safety performance of automated vehicles., Competing Interests: The authors have declared that no competing interests exist.

This study is motivated by the need to quantitatively understand the dynamic nature of Automated Vehicle (AV) education's effectiveness, given the fact that existing research has identified factors that associated with the change of AV acceptance between before and after education. To address this research gap, an online state-wide survey study was conducted in Kentucky, USA, offering the opportunity to receive AV education via watching a video and assessing survey respondents' AV acceptance before and after education. By employing a Structural Equation Modeling (SEM) approach with multi-layer endogenous variables included, what makes AV education effective and the underlying reasons were identified and quantitatively revealed with focusing on respondents' socio-demographics, affordability, travel needs, built environment, and exposure level to AV knowledge. As a result, significant factors that impact the probability of "AV acceptance being increased after education" were identified from the aforementioned perspectives. Overall, older generations are less likely to increase their AV acceptance after education for two particular reasons: (a) concerning the comfortableness of Level 5 (full) automation and (b) living in the rural setting, which leads to fewer chances of experiencing automated driving. From a gender perspective, women are more likely to be affected by AV education because they are less familiar with AV technology than men. Also, our results suggest that Level 5 comfort rating plays the most influential role in determining the likelihood of increasing AV acceptance after education. The likelihood is improved by 33% if there is a one-unit increase in the Level 5 comfort rating. As such, the research informs AV policymakers of identities that make AV education effective for future AV education considerations. • Theorized the mechanism of whether AV education is effective in enhancing AV acceptance. • Video-based education is offered, assessing AV acceptance before and after education. • Women are more likely to be affected by education because they are less familiar with AV. • Level 5 comfort rating improves the odds of enhancing acceptance after education by 33%. • Informed policymakers of identities that make AV education effective for future AV education. [ABSTRACT FROM AUTHOR]

• Measured the magnitude of HVI-induced visual distraction from the non-binary perspective. • Added the directional measurement of visual distraction by utilizing Polar Coordinate System. • Quantified distraction in automated driving from spatiotemporal and directional perspectives. • Theoretically investigated how the quantified visual distraction influences driving safety. • Established a "distraction-safety" system in assessing safety and distraction concurrently. Traditional methodologies for measuring visual distraction have been limiting in their approaches, treating distraction as a one-dimensional variable. This has been accomplished either by categorizing distraction as a binary variable or using surrogate measurements, such as reaction time in response to non-driving related tasks, which follow the procedures of measuring distraction from psychology. Furthermore, as human-vehicle interaction (HVI) under automated driving has the potential to provide safety information and bring visual distraction simultaneously, there lacks an investigation on the quantitative relationship between distraction and safety due to the restrained methodologies in measuring visual distraction. As HVI-induced driver distraction plays a critical role in determining safe driving under automated driving, a methodology is highly needed to comprehensively measure the visual distraction under automated driving so that its impact on safety can be further investigated. Therefore, sticking to the definition of visual distraction, this research aims to (1) improve the existing methodology in measuring HVI-induced driver distraction under automated driving by focusing on visual distraction and mathematically describing it from spatiotemporal and directional dimensions; (2) theoretically investigate how the quantified visual distraction influences driving safety and (3) quantify the relationship between distraction and safety with introducing the "distraction-safety" ratio. Drivers' fixation behaviors are used in quantifying visual distraction, which is measured by spatiotemporal and directional relationships between drivers' visual attention and the attention that indicates "zero distraction". Three newly added performance measures in quantifying HVI-induced visual distraction are real-time magnitudes, real-time directions, and intensities (cumulation of magnitudes over time). To validate the proposed methods, a verification study was conducted by recruiting drivers to test automated driving under Level 3 automation. Drivers are required to wear an eye-tracker and go through two scenarios interacting with jaywalkers where takeover actions are needed with two takeover warnings ("visual-only" and "visual & audible"). Per past studies, takeover time was measured in representing distraction level. As a result, this study confirms the validity of the proposed methods by revealing the significant and positive correlations between the measured distraction intensity and the takeover time. Discussions of the quantified visual distraction from the spatiotemporal and directional perspective further enhance the understanding of HVI-induced driver distraction under automated driving through multiple dimensions. Furthermore, this research reveals how distracted driving affects safety under automated driving through the takeover performance. Thresholds under visual distraction magnitudes and degrees that lead to traffic conflicts were identified. More importantly, a "distraction-safety" ratio that quantifies the relationship between visual distraction and safety benefits is proposed. The results suggest that the "visual & audible" is more effective in significantly enhancing safety while aggregating a significantly smaller amount of visual distraction. The contribution of this research is to re-define the methodological approach of measuring visual distraction by (1) measuring distraction multi-dimensionally and (2) establishing a "distraction-safety" system in quantitatively assessing the balance of safety benefits and the HVI-induced visual distraction magnitude under automated driving environment. [ABSTRACT FROM AUTHOR]