Investigation of Safe, Reliable and Collision Free Autonomous Public Transportation Systems via Guaranteed Sequential Trajectory Optimization

The main objective of this project is the investigation of "GuSTO: Guaranteed Sequential Trajectory Optimization via Sequential Convex Programming" applied to autonomous public transportation systems / vehicles. We aim to leverage a new methodology and an innovative processes to achieve a better, optimized and better performing transportation system that integrates with other “smart city” investments. This research also will target to optimize passenger and freight movements of autonomous public transportation vehicles to improve mobility of people and goods through development of advanced application of analytical tools, such as GuSTO. To achieve the objectives of this research, we will utilize the specific sequential trajectory optimization methodology applied to autonomous public transportation vehicles, in presence of dynamic obstacles. We will target to investigate the safe, reliable, and robust trajectory generation for public transport vehicles by avoiding collision and obstacles, by providing safety guarantees. 

University: 

Mineta Consortium for Transportation Mobility
San José State University

Principal Investigator: 

Kamran Turkoglu, Ph.D.

PI Contact Information: 

Mineta Transportation Institute
San José State University
210 N. 4th St., 4th Floor
San Jose, CA 95112
kamran.turkoglu@sjsu.edu

Funding Source(s) and Amounts Provided (by each agency or organization): 

U.S. Department of Transportation, Office of the Assistant Secretary for Research and Technology – $6,550

Total Project Cost: 

$6,550

Agency ID or Contract Number: 

69A3551747127

Dates: 

January 2019 to March 2020

Project Number: 

1877