A Caltech Library Service

MPC-Based Connected Cruise Control with Multiple Human Predecessors

Dollar, R. Austin and Molnár, Tamás G. and Vahidi, Ardalan and Orosz, Gábor (2021) MPC-Based Connected Cruise Control with Multiple Human Predecessors. In: 2021 American Control Conference (ACC). IEEE , Piscataway, NJ, pp. 405-411. ISBN 978-1-6654-4197-1.

Full text is not posted in this repository. Consult Related URLs below.

Use this Persistent URL to link to this item:


Model predictive control is applied to regulate the longitudinal motion of a connected automated vehicle in mixed traffic scenarios. A prediction method is proposed to enable model predictive control in low-automation, medium-connectivity situations using instantaneous motion information from multiple predecessor vehicles. This includes detection of unconnected vehicles that may be mixed between connected ones. Simulations using real human driver data for the predecessors show that, if the drivers are well-characterized on average, a hidden unconnected vehicle can be detected over 90 % of the time. Moreover, the resulting preview can recover 46 % of the gap in energy performance between single-predecessor prediction and ideal preview. Results are also compared to a classical controller that utilizes instantaneous information from multiple predecessors.

Item Type:Book Section
Related URLs:
URLURL TypeDescription
Molnár, Tamás G.0000-0002-9379-7121
Orosz, Gábor0000-0002-9000-3736
Additional Information:© 2021 AACC.
Record Number:CaltechAUTHORS:20210826-161429625
Persistent URL:
Official Citation:R. A. Dollar, T. G. Molnár, A. Vahidi and G. Orosz, "MPC-Based Connected Cruise Control with Multiple Human Predecessors," 2021 American Control Conference (ACC), 2021, pp. 405-411, doi: 10.23919/ACC50511.2021.9483272
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:110574
Deposited By: Tony Diaz
Deposited On:26 Aug 2021 16:37
Last Modified:26 Aug 2021 16:37

Repository Staff Only: item control page