Automated Lane Change and Robust Safety

Jiang, Zhong-Ping; Ozbay, Kaan; Chakraborty, Sayantan; Cui, Leilei; Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART); New York University

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Automated Lane Change and Robust Safety

2023-03-01
By Jiang, Zhong-Ping ; Ozbay, Kaan ; Chakraborty, Sayantan ; ...

[PDF-2.01 MB]

English

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Details:

Creators:

Jiang, Zhong-Ping ; Ozbay, Kaan ; Chakraborty, Sayantan ; Cui, Leilei

Jiang, Zhong-Ping ; Ozbay, Kaan ; Chakraborty, Sayantan ; Cui, Leilei Less -
Corporate Creators:

Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART) ; New York University

Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART) ; New York University Less -
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 ; United States. Department of Transportation. Federal Highway Administration

United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology ; United States. Department of Transportation. Federal Highway Administration Less -
Subject/TRT Terms:

[+]

Autonomous Vehicles Decision Making Intelligent Vehicles Lane Changing Passengers Predictive Models Safety
Resource Type:

Tech Report
Geographical Coverage:

United States
Edition:

Final Report: 3/1/22- 2/28/23
Corporate Publisher:

Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART)
Abstract:

Firstly, to guarantee stability and robustness in the face of parametric uncertainties, non-linearities, and modeling errors , we have proposed a data-driven optimal control algorithm to solve the lane-changing problem of AVs which is inspired by reinforcement learning and adaptive dynamic programming. Secondly, we have developed a lane change decision-making algorithm to ensure safe and efficient lane change. Thirdly, the lane change risk index (LCRI) is used to evaluate the AV lane change safety obtained by using the proposed data-driven optimal control algorithm. Fourthly, we have combined the data-driven optimal controller with the lane change decision-making algorithm by using control barrier functions (CBFs). Lastly, we have developed an experimental setup that includes prototypes of AV and highway lanes.
Format:

PDF
Funding:

69A3551747119
Collection(s):

University Transportation Centers
Main Document Checksum:

[+]

urn:sha256:5619497e7476e7367c894b4b3f35e22fca340059f212a927fe7f4f2c08ff6fcb
Download URL:

https://rosap.ntl.bts.gov/view/dot/67943/dot_67943_DS1.pdf
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Automated Lane Change and Robust Safety

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