The majority of currently used automatic parking systems exploit the planning-andtracking approach that involves planning the reference trajectory first and then tracking the desired reference trajectory. However, the response delay of longitudinal velocity prevents the parking controller from tracing the desired trajectory because the vehicle’s velocity and other state parameters are not synchronized, while the controller maneuvers the vehicle according to the planned desired velocity and steering profiles. We propose an inverse vehicle model to provide a neural-network-based integrated lateral and longitudinal automatic parking controller. We approximated the relationship of the planned velocity to the vehicle’s velocity using a second-order difference equation that involves the response characteristic of the vehicle’s longitudinal delay. The adjusted desired velocity to track the origin-planned velocity is calculated using the inverse vehicle model. Furthermore, we proposed an integrated longitudinal and lateral parking controller using an artificial neural network (ANN) model trained on a dataset applying the inverse vehicle model. By learning the control laws between the vehicle’s states and the corresponding actions, the proposed ANN-based controller could yield a steering angle and the adjusted desired velocity to complete automatic parking in a confined space.
|Publication status||Published - 2019 Dec|
Bibliographical noteFunding Information:
Acknowledgments: The authors performed this work as a part of research projects of SKT-Yonsei Cooperative Autonomous Driving Research Center under the SKT-Yonsei Global Talent Fostering Program supported by the SK Telecom ICT R&D Center.
Funding: This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the “ICT Consilience Creative Program” (IITP-2019-2017-0-01015) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation).
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ?ICT Consilience Creative Program? (IITP-2019-2017-0-01015) supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation).
© 2019 by the authors. Licensee MDPI, Basel, Switzerland.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Signal Processing
- Hardware and Architecture
- Computer Networks and Communications
- Electrical and Electronic Engineering