Next Article in Journal
Lack of Thermogram Sharpness as Component of Thermographic Temperature Measurement Uncertainty Budget
Previous Article in Journal
Optical Aberration Calibration and Correction of Photographic System Based on Wavefront Coding
Previous Article in Special Issue
Merit-Based Motion Planning for Autonomous Vehicles in Urban Scenarios
Article

How Imitation Learning and Human Factors Can Be Combined in a Model Predictive Control Algorithm for Adaptive Motion Planning and Control

1
Deparment of Electronics and Telecommunications, Politecnico di Torino, 10129 Torino, Italy
2
Centro Ricerche Fiat, 10043 Torino, Italy
*
Author to whom correspondence should be addressed.
Current address: Deparment of Electronics and Telecommunications, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy.
Academic Editors: Jorge Godoy, Antonio Artuñedo and Jorge Villagra
Sensors 2021, 21(12), 4012; https://doi.org/10.3390/s21124012
Received: 7 May 2021 / Revised: 31 May 2021 / Accepted: 3 June 2021 / Published: 10 June 2021
(This article belongs to the Special Issue Smooth Motion Planning for Autonomous Vehicles)
Interest in autonomous vehicles (AVs) has significantly increased in recent years, but despite the huge research efforts carried out in the field of intelligent transportation systems (ITSs), several technological challenges must still be addressed before AVs can be extensively deployed in any environment. In this context, one of the key technological enablers is represented by the motion-planning and control system, with the aim of guaranteeing the occupants comfort and safety. In this paper, a trajectory-planning and control algorithm is developed based on a Model Predictive Control (MPC) approach that is able to work in different road scenarios (such as urban areas and motorways). This MPC is designed considering imitation-learning from a specific dataset (from real-world overtaking maneuver data), with the aim of getting human-like behavior. The algorithm is used to generate optimal trajectories and control the vehicle dynamics. Simulations and Hardware-In-the-Loop tests are carried out to demonstrate the effectiveness and computation efficiency of the proposed approach. View Full-Text
Keywords: trajectory planning; vehicle dynamics control; Model Predictive Control; learning; overtaking maneuver trajectory planning; vehicle dynamics control; Model Predictive Control; learning; overtaking maneuver
Show Figures

Figure 1

MDPI and ACS Style

Karimshoushtari, M.; Novara, C.; Tango, F. How Imitation Learning and Human Factors Can Be Combined in a Model Predictive Control Algorithm for Adaptive Motion Planning and Control. Sensors 2021, 21, 4012. https://doi.org/10.3390/s21124012

AMA Style

Karimshoushtari M, Novara C, Tango F. How Imitation Learning and Human Factors Can Be Combined in a Model Predictive Control Algorithm for Adaptive Motion Planning and Control. Sensors. 2021; 21(12):4012. https://doi.org/10.3390/s21124012

Chicago/Turabian Style

Karimshoushtari, Milad; Novara, Carlo; Tango, Fabio. 2021. "How Imitation Learning and Human Factors Can Be Combined in a Model Predictive Control Algorithm for Adaptive Motion Planning and Control" Sensors 21, no. 12: 4012. https://doi.org/10.3390/s21124012

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop