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Model Predictive Control-Based Trajectory Shaper for Safe and Efficient Adaptive Cruise Control

by Anye Zhou, Zejiang Wang, Adian S Cook
Publication Type
Conference Paper
Book Title
2023 IEEE International Automated Vehicle Validation Conference (IAVVC)
Publication Date
Page Numbers
1 to 7
Publisher Location
New Jersey, United States of America
Conference Name
IEEE International Automated Vehicle Validation Conference 2023 (IAVVC)
Conference Location
Austin, Texas, United States of America
Conference Sponsor
IEEE Instrumentation and Measurement Society, the IEEE Standards Association, IEEE Transportation Electrification Community, International Alliance for Mobility Testing and Standardization
Conference Date

Recent studies show that commercially-available adaptive cruise control (ACC) systems are string-unstable, indicating that ACC-driven vehicles amplify speed fluctuations from downstream traffic and induce stop-and-go waves. Moreover, it is challenging to revise the original control algorithm of an ACC system to achieve string stability due to its internal complexity and powertrain uncertainties. To achieve desired control performance given a string-unstable ACC system and circumvent revising the original control algorithm, this study proposes a model predictive control-based trajectory shaper (MPC-TS), which only modifies the sensor-measured trajectory information (i.e., position and speed) of the preceding vehicle. The proposed MPC-TS leverages the input shaping technique to generate reference trajectory to improve string stability, while incorporating tracking errors and vehicle acceleration/deceleration magnitude in the MPC cost function and constraining fluctuations of vehicle speed and spacing to ensure desired car-following performance. Numerical experiments validate the control performance of ACC with the proposed MPC-TS in terms of string stability, safety, traffic efficiency, and comfort.