Using Virtual Reality Techniques to Investigating Interactions Between Fully Autonomous Vehicles and Vulnerable Road Users

John, Ajuna P; Sabu, Jelin; Dong, Jiqian; Li, Yujie; Chen, Sikai; Labi, Samuel

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Using Virtual Reality Techniques to Investigating Interactions Between Fully Autonomous Vehicles and Vulnerable Road Users

2023-06-01
By John, Ajuna P ; Sabu, Jelin ; Dong, Jiqian

File Language:

English

Details

Alternative Title:

Using Virtual Reality Techniques to Investigate Interactions Between Fully Autonomous Vehicles and Vulnerable Road Users
Creators:

John, Ajuna P ; Sabu, Jelin ; Dong, Jiqian ; Li, Yujie ; Chen, Sikai ; Labi, Samuel
Corporate Creators:

Center for Connected and Automated Transportation (CCAT) ; Purdue University ; University of Michigan
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:
Publication/ Report Number:

Report No. 62
Resource Type:

Tech Report
Geographical Coverage:

United States
Edition:

Final rep., Jan 2021-June 2023
Corporate Publisher:

Center for Connected and Automated Transportation. Purdue University
Abstract:

At roadway ecosystems with frequent movement conflicts among vehicles, pedestrians, and other road users, a road user entering the location immediately triggers a vibrant exchange of informal or formal cues with other road users and the traffic environment to ensure safe and efficient movement for all the road users at that location and at that time. For such communication, the non-vehicle road users typically use informal communication mechanisms to interpret other road users’ intentions for making movement decisions. In the prospective era of FAVs (fully autonomous vehicles) that self-drive in any environment and conditions), these informal communication mechanisms will become obviated because the FAV has no human driver.
Format:

PDF
Funding:

69A3551747105
Download URL:

https://rosap.ntl.bts.gov/view/dot/73994/dot_73994_DS1.pdf
File Type:

[PDF - 5.39 MB]
Collection(s):

Autonomous Vehicles ; University Transportation Centers
Main Document Checksum:

urn:sha-512:68b05871b780e5a03c0798ed90e66391fda71906bcaba0285c0103f7727e64ae550dfb3cb3f19256720164f11c1ffd3c88d6609f8e73f9feef5dc59880b1b1b3

File Language:

English

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