Model Predictive Control-Based Trajectory Shaper for Safe and Efficient Adaptive Cruise Control
By:
Zhou, Anye ; Wang, Zejiang ; Cook, Adian S
Page Number:
1-7
Issue Number:
10328117
Book Title:
2023 IEEE International Automated Vehicle Validation Conference (IAVVC)
Publication Date:
October 2023
Publisher Location:
IEEE, 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
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.
