Challenges and Opportunities for the Intersection of Vulnerable Road Users (VRU) and Automated Vehicles (AVs)

Owens, Justin M.; Sandt, Laura; Morgan, Justin F.; Sundararajan, Sudharson; Clamann, Michael; Manocha, Dinesh; Steinfeld, Aaron; Maheshwari, Tanvi; Cooper, Jill F

doi:10.1007/978-3-319-94896-6_18

Cited by 7 publications

(3 citation statements)

References 3 publications

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“…The camera system on AVs depends on the brightness of the scene to determine the intensity of image pixels [ 66 ]. Since night vision images have fewer texture details and low contrast, dim light in the evening would reduce the ability of AVs to recognize the surrounding scene and lack sufficient reaction time to avoid serious crashes [ 67 , 68 ]. Moreover, rain may obscure the edges of objects, making them difficult to recognizable.…”

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

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“…Hence, there is a need for research aimed at exploring how AI-based machine learning can be employed to address mobility inclusion and safety among senior citizens of all ages represented (65-74 years and 75 years and older). Although several projects have been carried out to improve senior citizens' mobility [48], there are fewer studies that have employed AI or machine learning to improve the inclusive and safe mobility of senior citizens. These proposed solutions do not always consider the real needs and requirements of senior citizens, as the technologies are developed for senior citizens and not with senior citizens, and consequently, they are not suitable to address the real mobility needs of senior citizens [9,49].…”

Section: Ai-based Machine Leaning For Inclusive and Safe Mobilitymentioning

confidence: 99%

Inclusive and Safe Mobility Needs of Senior Citizens: Implications for Age-Friendly Cities and Communities

Bokolo

2023

Urban Science

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Municipalities are concerned with addressing social issues such as mobility inclusion and safety by increasing access to transport facilities and services for all groups in society to create equitable and equal access for all citizens. Moreover, the public transportation systems provided in cities have to be inclusive and safe, driven by emerging technologies such as Artificial Intelligence (AI)-based services that provide personalized recommendation to improve mobility inclusion and safety for all citizens in society, especially vulnerable road users such as senior citizens or older people. But at the moment, there are few studies that have investigated how municipalities can provide inclusive and safe public transportation in general and for senior citizens, particularly those aged 65 and above. Therefore, this study aimed to examine how to provide inclusive and safe mobility for senior citizens to improve out-of-home mobility services for senior citizens towards age-friendly cities and communities. Accordingly, a systematic literature review grounded on secondary data was adopted to investigate inclusive and safe mobility needs for senior citizens. The data were collected from previous research and existing documents, and a descriptive data analysis was carried out to provide insights on urban transportation policies related to senior citizens. Furthermore, case studies were adopted to present polices and strategies employed in Norway, Canada, the United States of America, the United Kingdom, Sweden, and Northern Ireland to identify measures employed to address the public transportation needs of an aging society, focusing on the provision of inclusive and safe mobility to senior citizens. Further findings from this study included the possible use of emerging technologies such as AI-based machine learning for inclusive and safe mobility.

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“…Yet there is more at stake with autonomous vehicles (AVs). While it is true that questions remain as to AV's ultimate use and benefits (Martínez-Díaz and Soriguera, 2018;Mueller et al, 2020;Litman, 2023)especially as they relate to vulnerable road users (Owens et al, 2019), as well as to pedestrians and cyclists (Sandt and Owens, 2017) many argue that AVs are a key disruptor technology (Medrano-Berumen and Akbaş, 2021;Nikitas et al, 2021;Othman, 2022). For some AVs offer the enticing prospect of decongested traffic, lowering public transport costs, ecological advantages, not to mention the prospect of radically reduced road deaths (Etienne, 2021).…”

Section: Introductionmentioning

confidence: 99%

Believe me! Why tesla’s recent alleged malfunction further highlights the need for transparent dialogue

Milford¹,

Elger²,

Shaw³

2023

Front. Future Transp.

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On November the 13th, 2022 video footage was released purportedly showing a Tesla Model Y malfunctioning by speeding through the streets of a Chinese city killing two people. Video footage such as this has the potential to undermine trust in AVs. While Tesla has responded by stating they will get to the “truth,” there are questions as to how this truth is to be decided, and perhaps more importantly how the public can trust either Tesla or negative press. We explore the “facts” of the incident and discuss the challenges of building trust in new AVs systems based on transparency. In this article we argue that transparency is more than simply getting to the “truth.” It is fostering a relational dialogue between the facts and stakeholder. Using O’Brien’s window metaphor, this article explores the need for AV manufacturers to consider the content of such incidents, the different perceptions of stakeholders, and the medium through which the content is presented. Apart from the need for independent crash investigators, there is a need for AV manufacturers to go beyond simply’ getting to the truth’ and to engage with the public responsibly.

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Challenges and Opportunities for the Intersection of Vulnerable Road Users (VRU) and Automated Vehicles (AVs)

Cited by 7 publications

References 3 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

Inclusive and Safe Mobility Needs of Senior Citizens: Implications for Age-Friendly Cities and Communities

Believe me! Why tesla’s recent alleged malfunction further highlights the need for transparent dialogue

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