An Inverse Vehicle Model for a Neural-Network-Based Integrated Lateral and Longitudinal Automatic Parking Controller

Abstract

The majority of currently used automatic parking systems exploit the planning-and-tracking 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.Fuzzy-based and knowledge-based approaches have been presented [17][18][19]. These methods are capable of providing solutions within a range of designed rules with an advantage in easy implements and practical usages.Recently, methods based on deep neural networks have been expected to solve the drawbacks of the mentioned step-by-step approaches by maneuvering vehicles without prior offline trajectory planning [20][21][22][23]. By training an artificial neural network (ANN) using a dataset generated by simulation or experiment, the ANN learns hyper-dimensional relationships between the current vehicle states and the appropriate vehicle maneuvering signals. Instead of calculating the parking trajectory offline, the ANN-based parking controller can yield a direct maneuvering signal of the steering angle and velocity online, while the vehicle is moving into a parking space. Liu et al. presented a method to enumerate all the possible parking trajectories and corresponding steering actions, and then have the parking controller learn the relationship between the given initial-and-final state pairs and the corresponding sequence of steering actions using an ANN [20]. Li et al. proposed an end-to-end neural-network-based automatic parking controller [21]. Rathour also proposed an encoder-decoder architecture for automatic parking [22]. Moon et al. developed an automatic parking controller with a twin ANN architectures [23].However, neither the planning-based methods nor ANN-based controllers have taken account of longitudinal control delay for a vehicle under automatic parking. Figure 1 shows a typical block diagram of a conventional automatic parking contr...

Fast Planning and Tracking of Complex Autonomous Parking Maneuvers With Optimal Control and Pseudo-Neural Networks

“…3) Most of the optimal control-based papers used direct collocation approaches, while indirect optimal control methods -based on the Pontryagin's Maximum Principle (PMP)-are still unexplored in the field of autonomous parking. 4) Some papers, like [28], used neural network controllers for motion tracking, but they required very large training datasets, and they focused on a limited set of parking scenarios.…”

“…The controller has a pseudo-neural physics-driven formulation (pNN), which uses few learnable parameters to accurately model the nonlinear steering dynamics. In comparison with other papers using neural networks for motion tracking of parking maneuvers [28], the specific structure devised for pNN requires smaller training datasets to produce accurate predictions. The structure of pNN is partly inspired by the neural models that we presented in [31], [42], [43], but the model of this paper is designed to capture the nonlinear steering dynamics at low speed.…”

“…A Lyapunov-based feedback controller was presented [27], for both speed and path tracking. In [28], an integrated longitudinal-lateral neural network controller for autonomous parking was developed. However, their approach requires a very large training dataset, since the neural network structure is generic, and they show only parallel parking examples.…”

A Road Condition Classification Algorithm for a Tire Acceleration Sensor using an Artificial Neural Network

“…The automotive industry is experiencing a period of innovation, represented by the term CASE (connected, autonomous, shared, and electric). This term symbolizes the application of technologies from various fields to automobiles [1][2][3]. Considerable effort has also been directed toward improving driving stability and convenience by utilizing various vehicle technologies, such as sensors and wired/wireless communications [4][5][6].…”

Research on automatic parking system based on linear quadratic regulator

“…Jhang and Lian (2020) proposed a sampling-based motion planner consisting of optimizing bidirectional rapidly exploring random trees* (Bi-RRT*) and parking-oriented model predictive control (MPC) is proposed to properly deal with various parking scenarios. Moon and Kim (2019) proposed an inverse vehicle model to provide a neural network-based integrated lateral and longitudinal automatic parking controller; furthermore, an integrated longitudinal and lateral parking controller using an artificial neural network (ANN) model trained on a dataset applying the inverse vehicle model. Su et al (2019), a secondary parallel automatic parking method of endpoint regionalization based on genetic algorithm, have proposed to improve the accuracy of parallel parking in tight spaces by estimating the minimum parking length and designing a reasonable terminal area for parking.…”