Verisimilar Percept Sequences Tests for Autonomous Driving Intelligent Agent Assessment

Watanabe, Thomio; Wolf, Denis F.

doi:10.1109/lars/sbr/wre.2018.00048

Cited by 5 publications

(3 citation statements)

References 35 publications

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“…Learning ability and adaptability need to be improved for AVs, enabling them to become a moving “intelligent agent”. Each “intelligent agent” can coordinate their respective routes, speeds, and distances between other vehicles to independently cope with all kinds of road conditions and unexpected situations [ 88 , 89 ]. In addition, the integration of multiple perception systems, such as visual perception systems (cameras and visual sensors), laser perception systems (laser radar), and microwave perception systems (millimeter-wave radar), will also help to reduce autonomous driving crashes; The content of this study has a certain reference value for the research of human-computer interaction.…”

Section: Discussionmentioning

confidence: 99%

Divergent Effects of Factors on Crash Severity under Autonomous and Conventional Driving Modes Using a Hierarchical Bayesian Approach

Ren

Chen

et al. 2022

IJERPH

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Influencing factors on crash severity involved with autonomous vehicles (AVs) have been paid increasing attention. However, there is a lack of comparative analyses of those factors between AVs and human-driven vehicles. To fill this research gap, the study aims to explore the divergent effects of factors on crash severity under autonomous and conventional (i.e., human-driven) driving modes. This study obtained 180 publicly available autonomous vehicle crash data, and 39 explanatory variables were extracted from three categories, including environment, roads, and vehicles. Then, a hierarchical Bayesian approach was applied to analyze the impacting factors on crash severity (i.e., injury or no injury) under both driving modes with considering unobserved heterogeneities. The results showed that some influencing factors affected both driving modes, but their degrees were different. For example, daily visitors’ flowrate had a greater impact on the crash severity under the conventional driving mode. More influencing factors only had significant impacts on one of the driving modes. For example, in the autonomous driving mode, mixed land use increased the severity of crashes, while daytime had the opposite effects. This study could contribute to specifying more appropriate policies to reduce the crash severity of both autonomous and human-driven vehicles especially in mixed traffic conditions.

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Section: Discussionmentioning

confidence: 99%

Divergent Effects of Factors on Crash Severity under Autonomous and Conventional Driving Modes Using a Hierarchical Bayesian Approach

Ren

Chen

et al. 2022

IJERPH

View full text Add to dashboard Cite

show abstract

“…To select the best fitting model, the Watanabe-Akaike Information Criterion (WAIC) [43] and leave-one-out cross-validation (LOO) [44] are used in this study. The model with the lowest WAIC and LOO has the best performance.…”

Section: Bayesian Inferencementioning

confidence: 99%

Comparative Analysis of Influencing Factors on Crash Severity between Super Multi-Lane and Traditional Multi-Lane Freeways Considering Spatial Heterogeneity

Zhang

Chen

et al. 2022

IJERPH

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With the growth of traffic demand, the number of newly built and renovated super multi-lane freeways (i.e., equal to or more than a two-way ten-lane) is increasing. Compared with traditional multi-lane freeways (i.e., a two-way six-lane or eight-lane), super multi-lane freeways have higher design speeds and more vehicle interweaving movements, which may lead to higher traffic risks. However, current studies mostly focus on the factors that affect crash severity on traditional multi-lane freeways, while little attention is paid to those on super multi-lane freeways. Therefore, this study aims to explore the impacting factors of crash severity on two kinds of freeways and make a comparison with traditional multi-lane freeways. The crash data of the Guangzhou-Shenzhen freeway in China from 2016 to 2019 is used in the study. This freeway contains both super multi-lane and traditional multi-lane road sections, and data on 2455 crashes on two-way ten-lane sections and 13,367 crashes on two-way six-lane sections were obtained for further analysis. Considering the effects of unobserved spatial heterogeneity, a hierarchical Bayesian approach is applied. The results show significant differences that influence the factors of serious crashes between these two kinds of freeways. On both two types of freeways, heavy-vehicle, two-vehicle, and multi-vehicle involvements are more likely to lead to serious crashes. Still, their impact on super multi-lane freeways is much stronger. In addition, for super multi-lane freeways, vehicle-to-facility collisions and rainy weather can result in a high possibility of serious crashes, but their impact on traditional multi-lane freeways are not significant. This study will contribute to understanding the impacting factors of crash severity on super multi-lane freeways and help the future design and safety management of super multi-lane freeways.

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“…For example, autonomous vehicles must guarantee failure rates (an indicator of reliability) as low as reasonably practicable to refrain from the risk of death or severe injury to people. 12 The main difference is that reliability is essentially a probability measure, whereas safety is not. Safety is defined as the absence of accidents involving an unplanned and unacceptable loss, 13 which occurs when the processes provide inadequate control and the safety constraints are violated.…”

Section: Introductionmentioning

confidence: 99%

Reliability assessment of autonomous vehicles based on the safety control structure

Wang

Araújo

2022

Proceedings of the Institution of Mechanical Engineers, Part O:

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The recent social trends and accelerated technological progress culminated in the development of autonomous vehicles (AVs). Reliability assessment for AV systems is in high demand before its market launch. In safety-critical systems (SCSs) such as AV systems, the reliability concept should be broadened to consider more safety-related issues. In this paper, reliability is defined as the probability that the system performs satisfactorily for a given period of time under stated conditions. This paper proposes a reliability assessment framework of AV, consisting of three main stages: (i) modeling the safety control structure through the Systems-Theoretic Accident Model and Processes (STAMP); (ii) mapping the control structure and functional relationships to a directed acyclic graph (DAG); and (iii) construct a Bayesian network (BN) on DAG to assess the system reliability. The fully automated (level 5) vehicle system is shown as a numeric example to illustrate how this suggested framework works. A brief discussion on involving human factors in systems to analyze lower levels of automated vehicles is also included, demonstrating the need for further research on real case studies.

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Verisimilar Percept Sequences Tests for Autonomous Driving Intelligent Agent Assessment

Cited by 5 publications

References 35 publications

Divergent Effects of Factors on Crash Severity under Autonomous and Conventional Driving Modes Using a Hierarchical Bayesian Approach

Divergent Effects of Factors on Crash Severity under Autonomous and Conventional Driving Modes Using a Hierarchical Bayesian Approach

Comparative Analysis of Influencing Factors on Crash Severity between Super Multi-Lane and Traditional Multi-Lane Freeways Considering Spatial Heterogeneity

Reliability assessment of autonomous vehicles based on the safety control structure

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