Real-time near-optimal path and maneuver planning in automatic parking using a simultaneous dynamic optimization approach

Moon, Jaeyoung; Bae, Il; Kim, Shiho

doi:10.1109/ivs.2017.7995719

Cited by 25 publications

(20 citation statements)

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“…We generated a dataset for training the ANN by applying the inverse vehicle model to the open dataset [23]. The open dataset was generated by a simultaneous dynamic optimization technique based on an interior-point method (IPM) [28,29] for offline near-optimal automatic paths and maneuver-planning for automatic parking [12,13]. The reader can refer to [12,13] Electronics 2019, 8, 1452 7 of 16 for details of the IPM-based simultaneous dynamic optimization to solve parking problems.…”

Section: Application To An Ann-based Automatic Parking Controllermentioning

confidence: 99%

“…The open dataset was generated by a simultaneous dynamic optimization technique based on an interior-point method (IPM) [28,29] for offline near-optimal automatic paths and maneuver-planning for automatic parking [12,13]. The reader can refer to [12,13] Electronics 2019, 8, 1452 7 of 16 for details of the IPM-based simultaneous dynamic optimization to solve parking problems. We adopted the definition of parking problems and the results of the dataset of automatic parking profiles in various parking scenarios from the authors' previous research [23].…”

Section: Application To An Ann-based Automatic Parking Controllermentioning

confidence: 99%

“…In the geometric planning methods, some form of curves such as β-spline [3], Bézier [4,5], clothoid [6], or polynomial curves [7,8] are generated as a set of reference trajectories. The mathematical optimization approaches, firstly formulating a cost function of the automatic parking problem and then minimizing the objective cost function [9][10][11][12][13]. Besides car-like vehicles, reference trajectory is feasible in the case of parking N-trailer vehicles when complicated kinematic constraints are taken into account in planning the trajectory [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…the control input of the gas and brake pedals). Figure 2 shows a simulated example of a conventional automatic parking control system using the optimal reference planning method [13] that does not regard the latency of longitudinal response. The simulated trajectory deviates from that of the planned reference, as shown in Figure 2a.…”

Section: Introductionmentioning

confidence: 99%

“…The mismatch of the temporal synchronization between the velocity and steering control signals brought by longitudinal latency eventually leads to a discrepancy between the planned and actual trajectories, making precise automatic parking control in a confined space difficult. Figure 2 shows a simulated example of a conventional automatic parking control system using the optimal reference planning method [13] that does not regard the latency of longitudinal response. The simulated trajectory deviates from that of the planned reference, as shown in Figure 2a.…”

Section: Introductionmentioning

confidence: 99%

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

2019

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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...

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