Making Crosswalks Smarter: Using Sensors and Learning Algorithms to Safeguard Heterogenous Road Users

Zhang, Yunchang; Fricker, Jon D.

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Making Crosswalks Smarter: Using Sensors and Learning Algorithms to Safeguard Heterogenous Road Users

2022-04-01
By Zhang, Yunchang ; Fricker, Jon D.

File Language:

English

Details

Creators:

Zhang, Yunchang ; Fricker, Jon D.
Corporate Creators:

Center for Connected and Automated Transportation (CCAT) ; Purdue University. Lyles Department of Civil Engineering
Corporate Contributors:

United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
Subject/TRT Terms:

Autonomous Vehicle Guidance Congestion Management Systems Connected Vehicles Human Factors Engineering Policy Traffic Control Vulnerable Road Users
Resource Type:

Other
Geographical Coverage:

United States
Corporate Publisher:

Center for Connected and Automated Transportation. Purdue University
Abstract:

Poster from 2022 Global Symposium on Connected and Automated Vehicles and Infrastructure, April 14, 2022, Ann Arbor, Michigan.
Format:

PDF
Funding:

69A3551747105
Collection(s):

University Transportation Centers Autonomous Vehicles
Main Document Checksum:

urn:sha-512:82073528d1f1dee4d3ad092bd7b3d0a3893a2d7dd1723ae2069967be7baf8e7f0b25be1e95fd9ae11e3234273ad263d8b8e5a8bc9cb56c1c594764931f21798e
Download URL:

https://rosap.ntl.bts.gov/view/dot/72954/dot_72954_DS1.pdf
File Type:

[PDF - 1.10 MB ]

File Language:

English

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