Machine Learning-based Trajectory Optimization of Connected and Autonomous Vehicles

Fan, Wei (David); Liu, Pengfei

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Machine Learning-based Trajectory Optimization of Connected and Autonomous Vehicles

2021-09-01
By Fan, Wei (David) ; Liu, Pengfei

File Language:

English

Details

Creators:

Fan, Wei (David) ; Liu, Pengfei
Corporate Creators:

University of North Carolina at Charlotte. Center for Advanced Multimodal Mobility Solutions and Education
Corporate Contributors:

United States. Department of Transportation. University Transportation Centers (UTC) Program
Subject/TRT Terms:
Publication/ Report Number:

2020 Project 04 ; CAMMSE-UNCC-2020-UTC-Project-04
Resource Type:

Tech Report
Geographical Coverage:

United States
Edition:

Final Report
Corporate Publisher:

University of North Carolina at Charlotte. Center for Advanced Multimodal Mobility Solutions and Education
Abstract:

Connected and autonomous vehicle (CAV) technologies provide solutions to the existing problems of the transportation systems. As widely known, CAVs can communicate with each other so that they can have coordinated accelerating or decelerating movements. In this manner, CAVs only need a smaller headway which will lead to a higher roadway capacity. For signalized intersections, CAVs can communicate with the signal lights to adjust their speeds when approaching the intersection, so that they can arrive at the intersection during green light. CAVs bring with them many benefits including improving safety, reducing emissions and increasing mobility of the transportation system.
Format:

PDF
Funding:

69A3551747133
Download URL:

https://rosap.ntl.bts.gov/view/dot/58263/dot_58263_DS1.pdf
File Type:

[PDF - 827.49 KB]
Collection(s):

Artificial Intelligence Deployment Center of Excellence (AI COE) ; Autonomous Vehicles ; University Transportation Centers ; US Transportation Collection
Main Document Checksum:

urn:sha-512:49c8fa55c82d448602ab31940bc4747ffa3e6b1dd400bb33a0598bd1ac43d867f717684a7a6ee80c6386cd49d91bd56897635adeb3798b1f2fadcd9ad5bbeb0b

File Language:

English

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