Laser‐based SLAM automatic parallel parking path planning and tracking for passenger vehicle

Song, Jie; Zhang, Weiwei; Wu, Xuncheng; Cao, Haotian; Gao, Qiaoming; Luo, Suyun

doi:10.1049/iet-its.2019.0049

Cited by 35 publications

(16 citation statements)

References 23 publications

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“…In [13], Wang and Chen proposed a novel global path planning framework based on a hybrid map which consists of three levels: the scene level, the feature level, and the metric level. For path planning in parking lot, Song et al [14] proposed a laser-based simultaneous localisation and mapping automatic parallel parking and tracking control scheme, which include environment perception and reconstruction, parking path planning, and path following, and Wu et al [15] addressed the multivehicle cooperative autonomous parking condition by establishing a vehicle kinematics model combined with dynamic constraints and endpoint and collision avoidance constraints.…”

Section: Related Workmentioning

confidence: 99%

A Collaborative Reservation Mechanism of Multiple Parking Lots Based on Dynamic Vehicle Path Planning

Xiao

Cai

et al. 2021

Journal of Advanced Transportation

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With the development of wireless communication and artificial intelligence technology, online parking reservation system can effectively save drivers’ searching time for vacant spaces. However, in the environment with multiple candidate parking lots around the destination, how to coordinate and maximize parking space resources to reduce the travel time is still a practical issue for urban drivers. In order to solve this problem, a collaborative reservation mechanism based on dynamic vehicle path planning is proposed in this paper. By the aid of the dedicated backbone network with a clear division of work responsibilities, the information of traffic and parking lots is collected in real time, based on which the travel time prediction and empty spaces evaluation are executed separately, and then the optimal decision of path planning and parking lot selection can be made and adjusted dynamically by a step-by-step acknowledgement mechanism. The simulation results show that, based on collaborative working and overall planning, our proposed reservation mechanism can effectively raise the utilization rate of the parking lots resources and significantly reduce the travel time for drivers under different traffic environments. Compared to current mechanisms, the collaborative parking reservation mechanism reveals higher feasibility and applicability. It can assist in design and operation of urban traffic management and space resource utilization.

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Section: Related Workmentioning

confidence: 99%

A Collaborative Reservation Mechanism of Multiple Parking Lots Based on Dynamic Vehicle Path Planning

Xiao

Cai

et al. 2021

Journal of Advanced Transportation

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“…A parking area is defined as the empty space formed by two obstacles or painted lines. For an obstacle-formed parking space, the range sensor-based methods were developed [ 5 , 17 ]. Ultrasonic sensors and LiDAR can be used to fit the shapes of obstacles.…”

Section: Related Workmentioning

confidence: 99%

Data Efficient Reinforcement Learning for Integrated Lateral Planning and Control in Automated Parking System

Song

Chen

Sun

et al. 2020

Sensors

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Reinforcement learning (RL) is a promising direction in automated parking systems (APSs), as integrating planning and tracking control using RL can potentially maximize the overall performance. However, commonly used model-free RL requires many interactions to achieve acceptable performance, and model-based RL in APS cannot continuously learn. In this paper, a data-efficient RL method is constructed to learn from data by use of a model-based method. The proposed method uses a truncated Monte Carlo tree search to evaluate parking states and select moves. Two artificial neural networks are trained to provide the search probability of each tree branch and the final reward for each state using self-trained data. The data efficiency is enhanced by weighting exploration with parking trajectory returns, an adaptive exploration scheme, and experience augmentation with imaginary rollouts. Without human demonstrations, a novel training pipeline is also used to train the initial action guidance network and the state value network. Compared with path planning and path-following methods, the proposed integrated method can flexibly co-ordinate the longitudinal and lateral motion to park a smaller parking space in one maneuver. Its adaptability to changes in the vehicle model is verified by joint Carsim and MATLAB simulation, demonstrating that the algorithm converges within a few iterations. Finally, experiments using a real vehicle platform are used to further verify the effectiveness of the proposed method. Compared with obtaining rewards using simulation, the proposed method achieves a better final parking attitude and success rate.

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“…A scene-dependent B-spline parallel parking path planning algorithm meets vehicle kinematics model constraints well. Here, the vehicle actuator's dynamic performance and parking path smoothness are key attributes [13].…”

Section: Introductionmentioning

confidence: 99%

Path Planning Optimization for Driverless Vehicle in Parallel Parking Integrating Radial Basis Function Neural Network

Wang

Hou

et al. 2021

Applied Sciences

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To optimize performances such as continuous curvature, safety, and satisfying curvature constraints of the initial planning path for driverless vehicles in parallel parking, a novel method is proposed to train control points of the Bézier curve using the radial basis function neural network method. Firstly, the composition and working process of an autonomous parking system are analyzed. An experiment concerning parking space detection is conducted using an Arduino intelligent minicar with ultrasonic sensor. Based on the analysis of the parallel parking process of experienced drivers and the idea of simulating a human driver, the initial path is planned via an arc-line-arc three segment composite curve and fitted by a quintic Bézier curve to make up for the discontinuity of curvature. Then, the radial basis function neural network is established, and slopes of points of the initial path are used as input to train and obtain horizontal ordinates of four control points in the middle of the Bézier curve. Finally, simulation experiments are carried out by MATLAB, whereby parallel parking of driverless vehicle is simulated, and the effects of the proposed method are verified. Results show the trained and optimized Bézier curve as a planning path meets the requirements of continuous curvature, safety, and curvature constraints, thus improving the abilities for parallel parking in small parking spaces.

show abstract

Laser‐based SLAM automatic parallel parking path planning and tracking for passenger vehicle

Cited by 35 publications

References 23 publications

A Collaborative Reservation Mechanism of Multiple Parking Lots Based on Dynamic Vehicle Path Planning

A Collaborative Reservation Mechanism of Multiple Parking Lots Based on Dynamic Vehicle Path Planning

Data Efficient Reinforcement Learning for Integrated Lateral Planning and Control in Automated Parking System

Path Planning Optimization for Driverless Vehicle in Parallel Parking Integrating Radial Basis Function Neural Network

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