Automated Lane Change and Robust Safety

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

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Automated Lane Change and Robust Safety

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

File Language:

English

Details

Creators:

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

Connected Cities for Smart Mobility toward Accessible and Resilient Transportation Center (C2SMART) ; New York University
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
Subject/TRT Terms:
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
Download URL:

https://rosap.ntl.bts.gov/view/dot/67943/dot_67943_DS1.pdf
File Type:

[PDF - 2.01 MB]
Collection(s):

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

urn:sha-512:fc6543e01f87d065060398b1b8624dd3826ce653c7f6c5f6e778de2a9c4c4fafcb4adf26dfaba7336d8631aad01d0dadccdf3be08cd39f775a2235ba4a7bbc8b

File Language:

English

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