Abstract
We propose an artificial deep neural network-(ANN-) based automatic parking controller that overcomes a stubborn restriction prevalent in traditional approaches. The proposed ANN learns human-like control laws for automatic parking through supervised learning from a training database generated by computer-Aided optimizations or real experiments. By learning the relationships between the instantaneous vehicle states and the corresponding maneuver parameters, the proposed twin controller yields lateral and longitudinal maneuvering parameters for executing automatic parking tasks in confined spaces. The proposed automatic parking controller exhibits a twin architecture comprising a main agent and its cloned agent. Before the main agent assumes a maneuvering action, the cloned agent predicts the consequences of the maneuvering action through a Collision Checking and Adjustment (CCA) system. The proposed parking agent operates like a human driver in a manner that is characterized by an unplanned trajectory. In addition, the kinematics of the subject vehicle is not exactly modelled for parking control. The simulation results demonstrate that the proposed twin agent emulates the attributes of a human driver such as adaptive control and determines the consequences of the tentative maneuvering action under varying kinematic models of the subject vehicle. We validate the proposed parking controller by simulating the software-in-The-loop architecture using a PreScan simulator in which the dynamics of the virtual vehicle's behavior resemble a real vehicle.
| Original language | English |
|---|---|
| Article number | 4801985 |
| Journal | Mathematical Problems in Engineering |
| Volume | 2019 |
| DOIs | |
| Publication status | Published - 2019 Jan 1 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Mathematics(all)
- Engineering(all)
Cite this
}
Automatic parking controller with a twin artificial neural network architecture. / Moon, Jaeyoung; Bae, Il; Kim, Shiho.
In: Mathematical Problems in Engineering, Vol. 2019, 4801985, 01.01.2019.Research output: Contribution to journal › Article
TY - JOUR
T1 - Automatic parking controller with a twin artificial neural network architecture
AU - Moon, Jaeyoung
AU - Bae, Il
AU - Kim, Shiho
PY - 2019/1/1
Y1 - 2019/1/1
N2 - We propose an artificial deep neural network-(ANN-) based automatic parking controller that overcomes a stubborn restriction prevalent in traditional approaches. The proposed ANN learns human-like control laws for automatic parking through supervised learning from a training database generated by computer-Aided optimizations or real experiments. By learning the relationships between the instantaneous vehicle states and the corresponding maneuver parameters, the proposed twin controller yields lateral and longitudinal maneuvering parameters for executing automatic parking tasks in confined spaces. The proposed automatic parking controller exhibits a twin architecture comprising a main agent and its cloned agent. Before the main agent assumes a maneuvering action, the cloned agent predicts the consequences of the maneuvering action through a Collision Checking and Adjustment (CCA) system. The proposed parking agent operates like a human driver in a manner that is characterized by an unplanned trajectory. In addition, the kinematics of the subject vehicle is not exactly modelled for parking control. The simulation results demonstrate that the proposed twin agent emulates the attributes of a human driver such as adaptive control and determines the consequences of the tentative maneuvering action under varying kinematic models of the subject vehicle. We validate the proposed parking controller by simulating the software-in-The-loop architecture using a PreScan simulator in which the dynamics of the virtual vehicle's behavior resemble a real vehicle.
AB - We propose an artificial deep neural network-(ANN-) based automatic parking controller that overcomes a stubborn restriction prevalent in traditional approaches. The proposed ANN learns human-like control laws for automatic parking through supervised learning from a training database generated by computer-Aided optimizations or real experiments. By learning the relationships between the instantaneous vehicle states and the corresponding maneuver parameters, the proposed twin controller yields lateral and longitudinal maneuvering parameters for executing automatic parking tasks in confined spaces. The proposed automatic parking controller exhibits a twin architecture comprising a main agent and its cloned agent. Before the main agent assumes a maneuvering action, the cloned agent predicts the consequences of the maneuvering action through a Collision Checking and Adjustment (CCA) system. The proposed parking agent operates like a human driver in a manner that is characterized by an unplanned trajectory. In addition, the kinematics of the subject vehicle is not exactly modelled for parking control. The simulation results demonstrate that the proposed twin agent emulates the attributes of a human driver such as adaptive control and determines the consequences of the tentative maneuvering action under varying kinematic models of the subject vehicle. We validate the proposed parking controller by simulating the software-in-The-loop architecture using a PreScan simulator in which the dynamics of the virtual vehicle's behavior resemble a real vehicle.
UR - http://www.scopus.com/inward/record.url?scp=85072974963&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85072974963&partnerID=8YFLogxK
U2 - 10.1155/2019/4801985
DO - 10.1155/2019/4801985
M3 - Article
AN - SCOPUS:85072974963
VL - 2019
JO - Mathematical Problems in Engineering
JF - Mathematical Problems in Engineering
SN - 1024-123X
M1 - 4801985
ER -