Готові списки джерел за темами / Autonomous parking systems

Добірка наукової літератури з теми "Autonomous parking systems"

Автор: Grafiati
Опубліковано: 10 грудня 2022
Оновлено: 29 січня 2023

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Статті в журналах з теми "Autonomous parking systems"

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Zhao, Junqiao, Yewei Huang, Xudong He, Shaoming Zhang, Chen Ye, Tiantian Feng, and Lu Xiong. "Visual Semantic Landmark-Based Robust Mapping and Localization for Autonomous Indoor Parking." Sensors 19, no. 1 (January 4, 2019): 161. http://dx.doi.org/10.3390/s19010161.

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Анотація:
Autonomous parking in an indoor parking lot without human intervention is one of the most demanded and challenging tasks of autonomous driving systems. The key to this task is precise real-time indoor localization. However, state-of-the-art low-level visual feature-based simultaneous localization and mapping systems (VSLAM) suffer in monotonous or texture-less scenes and under poor illumination or dynamic conditions. Additionally, low-level feature-based mapping results are hard for human beings to use directly. In this paper, we propose a semantic landmark-based robust VSLAM for real-time localization of autonomous vehicles in indoor parking lots. The parking slots are extracted as meaningful landmarks and enriched with confidence levels. We then propose a robust optimization framework to solve the aliasing problem of semantic landmarks by dynamically eliminating suboptimal constraints in the pose graph and correcting erroneous parking slots associations. As a result, a semantic map of the parking lot, which can be used by both autonomous driving systems and human beings, is established automatically and robustly. We evaluated the real-time localization performance using multiple autonomous vehicles, and an repeatability of 0.3 m track tracing was achieved at a 10 kph of autonomous driving.
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2

Nakrani, Naitik, and Maulin M. Joshi. "An adaptive motion planning algorithm for obstacle avoidance in autonomous vehicle parking." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 3 (September 1, 2021): 687. http://dx.doi.org/10.11591/ijai.v10.i3.pp687-697.

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Анотація:
In the recent era, machine learning-based autonomous vehicle parking and obstacle avoidance navigation have drawn increased attention. An intelligent design is needed to solve the autonomous vehicles related problems. Presently, autonomous parking systems follow path planning techniques that generally do not possess a quality and a skill of natural adapting behavior of a human. Most of these designs are built on pre-defined and fixed criteria. It needs to be adaptive with respect to the vehicle dynamics. A novel adaptive motion planning algorithm is proposed in this paper that incorporates obstacle avoidance capability into a standalone parking controller that is kept adaptive to vehicle dimensions to provide human-like intelligence for parking problems. This model utilizes fuzzy membership thresholds concerning vehicle dimensions and vehicle localization to enhance the vehicle’s trajectory during parking when taking into consideration obstacles. It is generalized for all segments of cars, and simulation results prove the proposed algorithm’s effectiveness.
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3

Nakrani, Naitik M., and Maulin M. Joshi. "Integration of Dimension-adaptive Obstacle Avoidance Algorithm in Fuzzy-based Autonomous Vehicle Parking." International Journal of Circuits, Systems and Signal Processing 15 (April 16, 2021): 367–76. http://dx.doi.org/10.46300/9106.2021.15.40.

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Анотація:
Sensor-based obstacle avoidance and Autonomous vehicle parking have been immensely researched in recent times. An integration of both will increase the usability of autonomous parking systems in dynamic and uncertain environments. The fuzzy logic theory is widely used to learn expert human skills for machines. However, existing fuzzy-based expert systems generally fail to mimic the natural adaptive skills of humans. The expert driver has a natural tendency to adapt to machine dynamics, especially vehicle-related. This paper proposes a novel non-holonomic dimension-based obstacle avoidance parking algorithm that integrates obstacle avoidance capabilities to a standalone parking controller. This algorithm is developed based on adaptive fuzzy membership inferences concerning passenger cars' different sizes and segments. It is tested for various vehicles in simulation results to show the effectiveness of the algorithm.
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4

Wu, Mingkang, Haobin Jiang, and Chin-An Tan. "Automated Parking Space Allocation during Transition with both Human-Operated and Autonomous Vehicles." Applied Sciences 11, no. 2 (January 18, 2021): 855. http://dx.doi.org/10.3390/app11020855.

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Анотація:
As fully automated valet parking systems are being developed, there is a transition period during which both human-operated vehicles (HVs) and autonomous vehicles (AVs) are present in the same parking infrastructure. This paper addresses the problem of allocation of a parking space to an AV without conflicting with the parking space chosen by the driver of a HV. A comprehensive assessment of the key factors that affect the preference and choice of a driver for a parking space is established by the fuzzy comprehensive method. The algorithm then generates a ranking order of the available parking spaces to first predict the driver’s choice of parking space and then allocate a space for the AV. The Floyd algorithm of shortest distance is used to determine the route for the AV to reach its parking space. The proposed allocation and search algorithm is applied to the examples of a parking lot with three designed scenarios. It is shown that parking space can be reasonably allocated for AVs.
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5

Kartal, Seda Karadeniz, and Giuseppe Casalino. "Horizontal Parking Control of Autonomous Underwater Vehicle, FOLOGA." IFAC-PapersOnLine 52, no. 8 (2019): 397–402. http://dx.doi.org/10.1016/j.ifacol.2019.08.102.

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Shahzad, Aamir, Abdelouahed Gherbi, and Kaiwen Zhang. "Enabling Fog–Blockchain Computing for Autonomous-Vehicle-Parking System: A Solution to Reinforce IoT–Cloud Platform for Future Smart Parking." Sensors 22, no. 13 (June 27, 2022): 4849. http://dx.doi.org/10.3390/s22134849.

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Анотація:
With the advent of modern technologies, including the IoT and blockchain, smart-parking (SP) systems are becoming smarter and smarter. Similar to other automated systems, and particularly those that require automation or minimal interaction with humans, the SP system is heuristic in delivering performances, such as throughput in terms of latency, efficiency, privacy, and security, and it is considered a long-term cost-effective solution. This study looks ahead to future trends and developments in SP systems and presents an inclusive, long-term, effective, and well-performing smart autonomous vehicle parking (SAVP) system that explores and employs the emerging fog-computing and blockchain technologies as robust solutions to strengthen the existing collaborative IoT–cloud platform to build and manage SP systems for autonomous vehicles (AVs). In other words, the proposed SAVP system offers a smart-parking solution, both indoors and outdoors, and mainly for AVs looking for vacant parking, wherein the fog nodes act as a middleware layer that provides various parking operations closer to IoT-enabled edge devices. To address the challenges of privacy and security, a lightweight integrated blockchain and cryptography (LIBC) module is deployed, which is functional at each fog node, to authorize and grant access to the AVs in every phase of parking (e.g., from the parking entrance to the parking slot to the parking exit). A proof-of-concept implementation was conducted, wherein the overall computed results, such as the average response time, efficiency, privacy, and security, were examined as highly efficient to enable a proven SAVP system. This study also examined an innovative pace, with careful considerations to combatting the existing SP-system challenges and, therefore, to building and managing future scalable SP systems.
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7

Chan, Teck Kai, and Cheng Siong Chin. "Review of Autonomous Intelligent Vehicles for Urban Driving and Parking." Electronics 10, no. 9 (April 25, 2021): 1021. http://dx.doi.org/10.3390/electronics10091021.

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Анотація:
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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Lee, Sun-Woo, Dongkyu Lee, and Seok-Cheol Kee. "Deep Learning-based Parking Area and Collision Risk Area Detection for Autonomous Parking." Journal of Institute of Control, Robotics and Systems 27, no. 8 (August 31, 2021): 565–71. http://dx.doi.org/10.5302/j.icros.2021.21.0059.

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Jiménez, Felipe, Miguel Clavijo, and Alejandro Cerrato. "Perception, Positioning and Decision-Making Algorithms Adaptation for an Autonomous Valet Parking System Based on Infrastructure Reference Points Using One Single LiDAR." Sensors 22, no. 3 (January 27, 2022): 979. http://dx.doi.org/10.3390/s22030979.

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Анотація:
Autonomous parking valet systems improve users’ comfort, helping with the task of searching for a parking space and parking maneuvering; and due to the simple infrastructure design and low speeds, this maneuver is quite feasible for automated vehicles. Various demonstrations have been performed in both closed parking and in open air parking; scenarios that allow the use of specific technological tools for navigation and searching for a parking space. However, there are still challenges. The purpose of this paper was the integration of perception, positioning, decision-making, and maneuvering algorithms for the control of an autonomous vehicle in a parking lot with the support of a single LiDAR sensor, and with no additional sensors in the infrastructure. Based on a digital map, which was as simplified as possible, the driver can choose the range of parking spaces in which the vehicle must look for a space. From that moment on, the vehicle moves, looking for free places until an available one in the range selected by the driver is found. Then, the vehicle performs the parking maneuver, choosing between two alternatives to optimize the required space. Tests in a real parking lot, with spaces covered with metallic canopies, showed an accurate behavior.
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Zeng, Dequan, Zhuoping Yu, Lu Xiong, Peizhi Zhang, and Zhiqiang Fu. "A unified optimal planner for autonomous parking vehicle." Control Theory and Technology 17, no. 4 (November 2019): 346–56. http://dx.doi.org/10.1007/s11768-019-9121-6.

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Дисертації з теми "Autonomous parking systems"

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Yuan, Xin. "Multi-approaches to achieve an advanced cognitive agent in a new type of parallel processing computer." Thesis, 2020. http://hdl.handle.net/2440/130741.

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Анотація:
In this work, we addressed the problem of developing an agent-based artificial general intelligence that can be implemented in compact and power-efficient electronic hardware. We proposed an approach intended to show the feasibility of using this conceptual hardware-based architecture to replicate simple cognitive behaviours. This research started with surveys on cognitive behaviours and their decision-making architectures and compared them with a production rule-based parallel processing computation architecture. In order to demonstrate their potential, a sample case study of the homing behaviour of honey bees was undertaken to demonstrate the possibility of reproducing cognitive behaviours using a production rule-based cognitive architecture. We developed rule-based agents for a mobile platform which, under experimental conditions, made decisions to retrace its path back to a target position by comparison with the reference images. The agent made consistent overall cognitive decisions using fuzzified elements and guided the system reliably to target positions. Then, the research shifted to finding cognitive data representations and constructing cognitive decision-making structures in that production rule-based system. We introduced a new symbolic way of describing the significant features in an image, which is to use a collection of fuzzy symbolic elements to describe the characteristics of the current environmental information. It filtered out any unnecessary details, yet retained sufficient information describing the frame to enable reliable comparisons between images for the purposes of navigation. Numerical data were converted into fuzzy symbolic representations of the surrounding environment. The modified Fuzzy Inference System includes the reasoning rules used to support the cognitive decision-making process. One of the main disadvantages of a rule-based approach is the effort spent on developing rules. In order to reduce the workload of developing rules manually for agents, a modified Association Rules Mining (ARM) method was introduced to discover effective rules for agents autonomously, based on training data sets. This novel rule development method has been demonstrated through a trainable autonomous parking system, which can develop rules for autonomous parking agents.
Thesis (Ph.D.) -- University of Adelaide, School of Electrical and Electronic Engineering, 2021
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2

Chang, Chi-Cheng, and 張其正. "Design and Implementation of Autonomous Parking Control System for Dual-Sensors Car-Like Mobile Robot using Nios Embedded Processor System." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/27621384789030448342.

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Анотація:
碩士
國立成功大學
電機工程學系碩博士班
93
This thesis presents the design and implementation of the intelligent autonomous parking controller (APC) and accomplishes it in a car-like mobile robot (CLMR). This CLMR estimates the environment by integrating the information of infrared and ultrasonic sensors. We utilize the Nios embedded processor system to compute these data and decide the reactive behavior by fuzzy logic control (FLC). 　Firstly, we describe the system architecture of the CLMR. It contains the reconstruction of the chassis of the CLMR, DC motor unit, servo motor unit, driver circuit, Nios development board, A/D unit, infrared sensor, and ultrasonic sensor. Secondly, we develop the intelligent parking control method, which is based on the fuzzy logic control. We propose four parking modes including right-side parallel-parking mode, right-side garage-parking mode, left-side parallel-parking mode, and left-side garage-parking mode. And the CLMR can autonomously determine which mode to use and park itself into the parking lot. Furthermore, we address how to implement the controller by utilizing the Nios SOPC Builder, VHDL, and C/C++ language in the Nios development board. Finally, it is perceived that our intelligent APC is feasible and effective from the practical experiments.
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Частини книг з теми "Autonomous parking systems"

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Ertekin, Mehmet, and Mehmet Önder Efe. "Autonomous Parking with Continuous Reinforcement Learning." In Engineering Cyber-Physical Systems and Critical Infrastructures, 25–37. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-09753-9_3.

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2

Ravikanna, Roopika, Marc Hanheide, Gautham Das, and Zuyuan Zhu. "Maximising Availability of Transportation Robots Through Intelligent Allocation of Parking Spaces." In Towards Autonomous Robotic Systems, 337–48. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89177-0_34.

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3

Nakrani, Naitik, and Maulin Joshi. "Fuzzy based Autonomous Parallel Parking Challenges in Real time Scenario." In Advances in Intelligent Systems and Computing, 789–802. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47952-1_63.

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4

Sánchez, Carlos Martín, Matilde Santos Peñas, and Luis Garmendia Salvador. "A Fuzzy Decision System for an Autonomous Car Parking." In Handbook on Decision Making, 237–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25755-1_13.

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Karamchandani, Sunil, Saurabh Pednekar, Atharva Pusalkar, Shivani Bhattacharjee, and Disha Issrani. "Autonomous Parking System Perception and Control Simulations on ROS-Gazebo." In Lecture Notes on Data Engineering and Communications Technologies, 345–53. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6601-8_32.

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6

Xiong, Xing, and Byung-Jae Choi. "Design of Genetic Algorithm-Based Parking System for an Autonomous Vehicle." In Communications in Computer and Information Science, 50–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-26010-0_6.

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7

Wegener, Joachim, and Oliver Bühler. "Evaluation of Different Fitness Functions for the Evolutionary Testing of an Autonomous Parking System." In Genetic and Evolutionary Computation – GECCO 2004, 1400–1412. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24855-2_160.

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8

Liu, Yu-Lin, David W. Hsiao, and Amy J. C. Trappey. "The Study of Autonomous Negotiation System Based on Auction Enabled Intelligent Agent – Using Parking Tower Asset Maintenance as Case Example." In Global Perspective for Competitive Enterprise, Economy and Ecology, 769–79. London: Springer London, 2009. http://dx.doi.org/10.1007/978-1-84882-762-2_73.

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9

Dutta, Arjun, Ankur Bhattacharjee, and Abhijit Kar Gupta. "Development of a Low Cost Autonomous Car Parking System: Towards Smart City." In Intelligent Electrical Systems: A Step towards Smarter Earth, 105–11. CRC Press, 2021. http://dx.doi.org/10.1201/9780429355998-13.

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10

Kannan, M., N. Jagadeesh, L. William Mary, and C. Priya. "A secured IoT parking system based on smart sensor communication with two-step user verification." In Autonomous and Connected Heavy Vehicle Technology, 141–59. Elsevier, 2022. http://dx.doi.org/10.1016/b978-0-323-90592-3.00008-2.

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Тези доповідей конференцій з теми "Autonomous parking systems"

1

Moshchuk, Nikolai, and Shih-Ken Chen. "Autonomous Parking Strategy." In ASME 2008 International Mechanical Engineering Congress and Exposition. ASMEDC, 2008. http://dx.doi.org/10.1115/imece2008-66536.

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Анотація:
Parallel parking can be a difficult task for novice drivers or drivers who seldom drive in congested city where parking space is limited. Parking Assist is an innovative system designed to aid the driver in performing sometimes difficult parallel parking maneuvers. Many companies are developing such systems with major automakers, such as Valeo, Aisin Seiki, Hella, Robert Bosch, and TRW. For example, Toyota IPA (Intelligent Parking Assist) system uses a rear view camera and automatically steer the vehicle into the parking spot with driver controlling braking. This paper describes the development of parking path planning strategies based on available parking space. A virtual turn center will first be defined and derived based on vehicle configuration. Required parking space for one or two cycle parking maneuver will then be determined. Path planning strategies for both one and two turn parking maneuvers will be developed next. Finally CarSim simulation will be performed to verify the design.
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2

Mansour, Marvy Badr Monir, Abdelrahman Said, Nour Eldeen Ahmed, and Seif Sallam. "Autonomous Parallel Car Parking." In 2020 Fourth World Conference on Smart Trends in Systems Security and Sustainablity (WorldS4). IEEE, 2020. http://dx.doi.org/10.1109/worlds450073.2020.9210298.

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3

Lin, Letian, and J. Jim Zhu. "Path Planning for Autonomous Car Parking." In ASME 2018 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/dscc2018-9195.

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Анотація:
The path planning problem for autonomous car parking has been widely studied. However, it is challenging to design a path planner that can cope with parking in tight environment for all common parking scenarios. The important practical concerns in design, including low computational costs and little human’s knowledge and intervention, make the problem even more difficult. In this work, a path planner is developed using a novel four-phase algorithm. By using some switching control laws to drive two virtual cars to a target line, a forward path and a reverse path are obtained. Then the two paths are connected along the target line. As illustrated by the simulation results, the proposed path planning algorithm is fast, highly autonomous, sufficiently general and can be used in tight environment.
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4

Chirca, Mihai, Roland Chapuis, and Roland Lenain. "Autonomous Valet Parking System Architecture." In 2015 IEEE 18th International Conference on Intelligent Transportation Systems - (ITSC 2015). IEEE, 2015. http://dx.doi.org/10.1109/itsc.2015.421.

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5

Au, Tsz-Chiu. "Gridlock-free Autonomous Parking Lots for Autonomous Vehicles." In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2021. http://dx.doi.org/10.1109/iros51168.2021.9636591.

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6

Li, Rui, Weitian Wang, Yi Chen, Srivatsan Srinivasan, and Venkat N. Krovi. "An End-to-End Fully Automatic Bay Parking Approach for Autonomous Vehicles." In ASME 2018 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/dscc2018-9126.

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Анотація:
Fully automatic parking (FAP) is a key step towards the age of autonomous vehicle. Motivated by the contribution of human vision to human parking, in this paper, we propose a computer vision based FAP method for the autonomous vehicles. Based on the input images from a rear camera on the vehicle, a convolutional neural network (CNN) is trained to automatically output the steering and velocity commands for the vehicle controlling. The CNN is trained by Caffe deep learning framework. A 1/10th autonomous vehicle research platform (1/10-SAVRP), which configured with a vehicle controller unit, an automated driving processor, and a rear camera, is used for demonstrating the parking maneuver. The experimental results suggested that the proposed approach enabled the vehicle to gain the ability of parking independently without human input in different driving settings.
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Wang, Yafeng, and Beibei Ren. "Quadrotor-Enabled Autonomous Parking Occupancy Detection." In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2020. http://dx.doi.org/10.1109/iros45743.2020.9341081.

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Young-Woo Seo, C. Urmson, D. Wettergreen, and Jin-Woo Lee. "Building lane-graphs for autonomous parking." In 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2010). IEEE, 2010. http://dx.doi.org/10.1109/iros.2010.5650331.

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Kang, Dong Hee, Chang Mook Kang, Joong-Sik Kim, Seunghyun Kim, Whoi-Yul Kim, Seung-Hi Lee, and Chung Choo Chung. "Vision-based autonomous indoor valet parking system." In 2017 17th International Conference on Control, Automation and Systems (ICCAS). IEEE, 2017. http://dx.doi.org/10.23919/iccas.2017.8204420.

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10

Perez-Morales, David, Salvador Dominguez-Quijada, Olivier Kermorgant, and Philippe Martinet. "Autonomous parking using a sensor based approach." In 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2016. http://dx.doi.org/10.1109/itsc.2016.7795556.

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