An Autonomous Parking System of Optimally Integrating Bidirectional Rapidly-Exploring Random Trees and Parking-Oriented Model Predictive Control

Jhang, Jyun-Hao; Lian, Feng‐Li

doi:10.1109/access.2020.3020859

Cited by 22 publications

(14 citation statements)

References 32 publications

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“…RRT* [22] can keep the parking path from varying through a treerewiring step, but this step requires further planning time. There are bidirectional approaches [23]- [26] that reduce the planning time by growing another tree from the parking spot and connecting the two trees. These approaches can search for a narrow parking spot in advance.…”

Section: Related Workmentioning

confidence: 99%

Continuous-Curvature Target Tree Algorithm for Path Planning in Complex Parking Environments

Kim¹,

Ahn²,

Park³

2022

Preprint

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Rapidly-exploring random tree (RRT) has been applied for autonomous parking due to quickly solving highdimensional motion planning and easily reflecting constraints. However, planning time increases by the low probability of extending toward narrow parking spots without collisions. To reduce the planning time, the target tree algorithm was proposed, substituting a parking goal in RRT with a set (target tree) of backward parking paths. However, it consists of circular and straight paths, and an autonomous vehicle cannot park accurately because of curvature-discontinuity. Moreover, the planning time increases in complex environments; backward paths can be blocked by obstacles. Therefore, this paper introduces the continuous-curvature target tree algorithm for complex parking environments. First, a target tree includes clothoid paths to address such curvature-discontinuity. Second, to reduce the planning time further, a cost function is defined to construct a target tree that considers obstacles. Integrated with optimalvariant RRT and searching for the shortest path among the reached backward paths, the proposed algorithm obtains a nearoptimal path as the sampling time increases. Experiment results in real environments show that the vehicle more accurately parks, and continuous-curvature paths are obtained more quickly and with higher success rates than those acquired with other sampling-based algorithms.

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Section: Related Workmentioning

confidence: 99%

Continuous-Curvature Target Tree Algorithm for Path Planning in Complex Parking Environments

Kim¹,

Ahn²,

Park³

2022

Preprint

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show abstract

“…Finally, after identifying an empty parking lot, the autonomous vehicle completes the final task by maneuvering itself into the lot. Although there are several different types of parking lots such as parallel, perpendicular, and fishbone parking [111,112], autonomous parking usually consists of two steps: (1) optimal path planning and (2) path following/tracking [112][113][114]. Using data collected from a combination of sensors, the optimal path planning algorithm begins by generating a suitable collision-free path from a given starting point to a required position within the parking lot that satisfies all kinematic constraints.…”

Section: Parkingmentioning

confidence: 99%

Review of Autonomous Intelligent Vehicles for Urban Driving and Parking

Chan

Chin

2021

Electronics

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With the concept of Internet-of-Things, autonomous vehicles can provide higher driving efficiency, traffic safety, and freedom for the driver to perform other tasks. This paper first covers enabling technology involving a vehicle moving out of parking, traveling on the road, and parking at the destination. The development of autonomous vehicles relies on the data collected for deployment in actual road conditions. Research gaps and recommendations for autonomous intelligent vehicles are included. For example, a sudden obstacle while the autonomous vehicle executes the parking trajectory on the road is discussed. Several aspects of social problems, such as the liability of an accident affecting the autonomous vehicle, are described. A smart device to detect abnormal driving behaviors to prevent possible accidents is briefly discussed.

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“…The single-track kinematics model is simple, and it is often applied to the MPC controller design. The optimal steering wheel angle and longitudinal force can be obtained by using the standard quadratic cost function based on the design constraints [16][17][18]. Under different speed ranges, the prediction model needs to be robust enough to describe the overall vehicle motion behaviors; otherwise, the prediction error will significantly increase [19].…”

Section: Related Workmentioning

confidence: 99%

Integrating Vehicle Positioning and Path Tracking Practices for an Autonomous Vehicle Prototype in Campus Environment

Yang

Kuo

2021

Electronics

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This paper presents the implementation of an autonomous electric vehicle (EV) project in the National Taiwan University of Science and Technology (NTUST) campus in Taiwan. The aim of this work was to integrate two important practices of realizing an autonomous vehicle in a campus environment, including vehicle positioning and path tracking. Such a project is helpful to the students to learn and practice key technologies of autonomous vehicles conveniently. Therefore, a laboratory-made EV was equipped with real-time kinematic GPS (RTK-GPS) to provide centimeter position accuracy. Furthermore, the model predictive control (MPC) was proposed to perform the path tracking capability. Nevertheless, the RTK-GPS exhibited some robust positioning concerns in practical application, such as a low update rate, signal obstruction, signal drift, and network instability. To solve this problem, a multisensory fusion approach using an unscented Kalman filter (UKF) was utilized to improve the vehicle positioning performance by further considering an inertial measurement unit (IMU) and wheel odometry. On the other hand, the model predictive control (MPC) is usually used to control autonomous EVs. However, the determination of MPC parameters is a challenging task. Hence, reinforcement learning (RL) was utilized to generalize the pre-trained datum value for the determination of MPC parameters in practice. To evaluate the performance of the RL-based MPC, software simulations using MATLAB and a laboratory-made, full-scale electric vehicle were arranged for experiments and validation. In a 199.27 m campus loop path, the estimated travel distance error was 0.82% in terms of UKF. The MPC parameters generated by RL also achieved a better tracking performance with 0.227 m RMSE in path tracking experiments, and they also achieved a better tracking performance when compared to that of human-tuned MPC parameters.

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An Autonomous Parking System of Optimally Integrating Bidirectional Rapidly-Exploring Random Trees and Parking-Oriented Model Predictive Control

Cited by 22 publications

References 32 publications

Continuous-Curvature Target Tree Algorithm for Path Planning in Complex Parking Environments

Continuous-Curvature Target Tree Algorithm for Path Planning in Complex Parking Environments

Review of Autonomous Intelligent Vehicles for Urban Driving and Parking

Integrating Vehicle Positioning and Path Tracking Practices for an Autonomous Vehicle Prototype in Campus Environment

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