Decision-making of autonomous vehicles in interactions with jaywalkers: A risk-aware deep reinforcement learning approach

Zhang, Ziqian; Li, Haojie; Chen, Tiantian; Sze, N.N.; Yang, Wenzhang; Zhang, Yihao; Ren, Gang

doi:10.1016/j.aap.2024.107843

Accident Analysis & Prevention

2025

DOI: 10.1016/j.aap.2024.107843

|View full text |Cite

Decision-making of autonomous vehicles in interactions with jaywalkers: A risk-aware deep reinforcement learning approach

Ziqian Zhang,

Haojie Li,

Tiantian Chen

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Factors Influencing Consumer Willingness to Use AI-Driven Autonomous Taxis

Liu,

Zhang,

Zhang

et al. 2024

Behavioral Sciences

View full text Add to dashboard Cite

The advancement of autonomous driving technology, particularly Tesla’s launch of its new Robotaxi, marks a transformation in transportation. Understanding the theoretical mechanisms that drive consumers’ intention to use autonomous taxis is essential. This study develops a structural equation model (SEM), extending the applicability of the TAM and TPB model, and incorporates external factors like attitudes, subjective norms, traffic efficiency, and perceived cost–benefit into the model to analyze their impact on consumers’ perceived characteristics (perceived usefulness and perceived ease of use). A survey of 427 valid responses revealed that attitudes, subjective norms, and perceived cost–benefit all have significant positive impacts on perceived usefulness and ease of use, which, in turn, are the primary drivers of consumers’ intention to use. Additionally, perceived risk significantly weakens the positive effects of perceived usefulness and ease of use on the intention to use, underscoring its critical moderating role in the technology acceptance process. This paper suggests strategies to enhance consumer acceptance, including strengthening user perception through marketing and public experience activities, optimizing technology to improve user experience, reinforcing safety and privacy measures to reduce perceived risk, and highlighting the insurance mechanism, convenience, and economic benefits of autonomous taxis in marketing.

show abstract

Factors Influencing Consumer Willingness to Use AI-Driven Autonomous Taxis

Liu,

Zhang,

Zhang

et al. 2024

Behavioral Sciences

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Decision-making of autonomous vehicles in interactions with jaywalkers: A risk-aware deep reinforcement learning approach

Cited by 1 publication

References 34 publications

Factors Influencing Consumer Willingness to Use AI-Driven Autonomous Taxis

Factors Influencing Consumer Willingness to Use AI-Driven Autonomous Taxis

Contact Info

Product

Resources

About