Preventing Crashes in Mixed Traffic With Automated and Human-Driven Vehicles

Talebpour, Alireza; Lord, Dominique; Manser, Michael; Machiani, Sahar Ghanipoor

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Preventing Crashes in Mixed Traffic With Automated and Human-Driven Vehicles

2020-11-01
By Talebpour, Alireza ; Lord, Dominique ; Manser, Michael

File Language:

English

Details

Creators:

Talebpour, Alireza ; Lord, Dominique ; Manser, Michael ; Machiani, Sahar Ghanipoor
Corporate Creators:

Safety through Disruption (Safe-D) University Transportation Center (UTC) ; Texas A & M Transportation Institute
Corporate Contributors:

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

03-072
Resource Type:

Tech Report
Geographical Coverage:

United States
Edition:

Final Research Report
Corporate Publisher:

Safety through Disruption (Safe-D) University Transportation Center (UTC)
Abstract:

Reducing crash counts on saturated road networks is one of the most significant benefits of autonomous vehicle(AV) technology. To date, many researchers have studied how AVs maneuver in different traffic situations, but less attention has been paid to car-following scenarios between AVs and human drivers. Braking and accelerating decision mismatches in this car-following scenario can lead to rear-end near-crashes and therefore warrant further study. This project aims to investigate the behavior of human drivers following an AV leader vehicle in a car-following situation and compare the results with a scenario in which the leader is a vehicle with human-modeled braking behavior. In this study, speed trajectory data was collected from 48 participants using a driving simulator. The results indicated a significant difference between the overall deceleration rates and braking speeds of the participants and the designated AV lead vehicle; however, no such difference was found between the participants and the human-modeled lead vehicle.
Format:

PDF
Funding:

69A3551747115/03-072
Download URL:

https://rosap.ntl.bts.gov/view/dot/55867/dot_55867_DS1.pdf
File Type:

[PDF - 1.15 MB]
Collection(s):

Autonomous Vehicles ; University Transportation Centers ; US Transportation Collection
Main Document Checksum:

urn:sha-512:3787adfb035d1c16540e728f34c81757ecbe037fa7e0c42b84ee6ee37d238b9e89dc1c3804ceee1936a9a2b29983ac85ec1a9b8116f03cea50934e95a1aa8ff1

File Language:

English

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