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Title: Evaluating the mobility performance of autonomous vehicles at a signalized traffic intersection
Recent developments in autonomous vehicle (AV) or connected AVs (CAVs) technology have led to predictions that fully self-driven vehicles could completely change the transportation network over the next decades. However, at this stage, AVs and CAVs are still in the development stage which requires various trails in the field and machine learning through autonomous driving miles on real road networks. Until the complete market adoption of autonomous technology, a long transition period of coexistence between conventional and autonomous cars would exist. It is important to study and develop the expected driving behavior of future autonomous cars and the traffic simulation platforms provide an opportunity for researchers and technology developers to implement and assess the different behaviors of self-driving vehicle technology before launching it to the actual ground. This study utilizes PTV VISSIM microsimulation platform to evaluate the mobility performance of unmanned vehicles at a 4-way signalized traffic intersection. The software contains three different AV-ready driving logics such as AV-cautious, AV-normal, and AV-aggressive which were tested against the performance of the conventional vehicles, and the results of the study revealed that the overall network operational performance improves with the progressive introduction of AVs using AV-normal, and AV-aggressive driving behaviors while the AV-cautious driving behavior stays conservative and deteriorates the traffic performance.  more » « less
Award ID(s):
2224135 1953102
NSF-PAR ID:
10328700
Author(s) / Creator(s):
; ; ; ; ;
Editor(s):
Zonta, Daniele; Su, Zhongqing; Glisic, Branko
Date Published:
Journal Name:
Proceedings Volume 12046, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2022
Page Range / eLocation ID:
57
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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