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Journal articles on the topic 'Autonomous parking systems'

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Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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1

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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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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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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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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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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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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6

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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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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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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8

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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9

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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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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10

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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11

Gómez-Bravo, F., F. Cuesta, and A. Ollero. "Parallel and diagonal parking in nonholonomic autonomous vehicles." Engineering Applications of Artificial Intelligence 14, no. 4 (August 2001): 419–34. http://dx.doi.org/10.1016/s0952-1976(01)00004-5.

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12

Liu, Ming, Bo Leng, Lu Xiong, Yize Yu, and Xing Yang. "Segment Drift Control with a Supervision Mechanism for Autonomous Vehicles." Actuators 10, no. 9 (September 1, 2021): 219. http://dx.doi.org/10.3390/act10090219.

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Stable maneuverability is extremely important for the overall safety and robustness of autonomous vehicles under extreme conditions, and automated drift is able to ensure the widest possible range of maneuverability. However, due to the strong nonlinearity and fast vehicle dynamics occurring during the drift process, drift control is challenging. In view of the drift parking scenario, this paper proposes a segmented drift parking method to improve the handling ability of vehicles under extreme conditions. The whole process is divided into two parts: the location approach part and the drift part. The model predictive control (MPC) method was used in the approach to achieve consistency between the actual state and the expected state. For drift, the open-loop control law was designed on the basis of drift trajectories obtained by professional drivers. The drift monitoring strategy aims to monitor the whole drift process and improve the success rate of the drift. A simulation and an actual vehicle test platform were built, and the test results show that the proposed algorithm can be used to achieve accurate vehicle drift to the parking position.
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13

Jiang, Shengchuan, Cong Zhao, Yifan Zhu, Chenwei Wang, and Yuchuan Du. "A Practical and Economical Ultra-wideband Base Station Placement Approach for Indoor Autonomous Driving Systems." Journal of Advanced Transportation 2022 (March 3, 2022): 1–12. http://dx.doi.org/10.1155/2022/3815306.

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Automated valet parking (AVP) has attracted much attention as the entry point to autonomous driving. In an indoor environment, high-precision positioning systems are essential for AVP. Ultra-wideband (UWB) is one of the most widely adopted techniques. However, the base station placement significantly influences the system’s positioning accuracy, especially for the irregular architecture of underground parking lots. This article proposes a three-stage practical and economical layout planning approach for UWB base stations, including determining the deployment strategy and layout parameters and comprehensive adjustment and scheme verification. The approach considers regional differentiation accuracy requirements for AVP, such as ramp area, surface fluctuation area, and narrow area. The adopted positioning method of a UWB system is the time difference of arrival (TDOA), and the evaluation index of positioning accuracy is the horizontal dilution of precision (HDOP). Through experimental tests in an actual parking lot, the proposed approach is confirmed to ensure stability and economy with fewer UWB base stations and can meet the positioning accuracy requirements of AVP.
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14

Katiyar, Divya, Shubham Gupta, Prashant Kumar, Prakriti Chaudhary, and Mandeep Katre. "Android Based Classic PARKADE System." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 1860–62. http://dx.doi.org/10.22214/ijraset.2022.41659.

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Abstract: As we all can Notice that world’s population is increasing day by day and due to increase in the number of populations, the number of vehicles are also increasing and their uses. This has been created a problem for parking the vehicles. Now in Metropolitan cities also, the areas are very less for parking and to deal with all this we required a good plan and major steps which can reduce the parking problem. The entrance into parking slot is also managed by an android based application. To overcome this problem we need to take a certain steps. In this research paper, is shown for Classic Parked Systems. Autonomous vehicle is a hot research area now. Keywords: Parking system, safety, REAL TIME, SELF PARKING, Slot booking , Android application
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15

Kim, Woo-Bin, Ye-Bin Kim, Yu-Min Hong, and Sung-Soo Hwang. "Autonomous Valet Parking Using Camera Switching and Car Headings." Journal of Institute of Control, Robotics and Systems 28, no. 3 (March 31, 2022): 273–79. http://dx.doi.org/10.5302/j.icros.2022.21.0150.

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16

Chai, Runqi, Antonios Tsourdos, Al Savvaris, Senchun Chai, Yuanqing Xia, and C. L. Philip Chen. "Multiobjective Optimal Parking Maneuver Planning of Autonomous Wheeled Vehicles." IEEE Transactions on Industrial Electronics 67, no. 12 (December 2020): 10809–21. http://dx.doi.org/10.1109/tie.2019.2962482.

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17

Gupta, Shubham, Divya Katiyar, Prakriti Chaudhary, Prashant Kumar, and Mandeep Katre. "Android Based Classic Parkade System." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 228–30. http://dx.doi.org/10.22214/ijraset.2022.42136.

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Abstract: As we all can Notice that world’s population is increasing day by day and due to increase in the number of populations, the number of vehicles are also increasing and their uses. This has been created a problem for parking the vehicles. Now in Metropolitan cities also, the areas are very less for parking and to deal with all this we required a good plan and major steps which can reduce the parking problem. The entrance into parking slot is also managed by an android based application. To overcome this problem we need to take a certain steps. In this research paper, is shown for Classic Parked Systems. Autonomous vehicle is a hot research area now.
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18

El Khalidi, Nihal, Faouzia Benabbou, and Nawal Sael. "Distributed parking management architecture based on multi-agent systems." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 4 (December 1, 2021): 801. http://dx.doi.org/10.11591/ijai.v10.i4.pp801-809.

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<span>With the increase of the number of vehicles on the road, several traffic congestion problems arise in the big city, and this has a negative impact on the economy, environment and citizens. The time spent looking for a parking space and the traffic generated contributes to mobility and traffic management problems. Hence the need for smart parking management to help drivers to find vacant spaces in a car park in a shorter time. Today, researchers are considering scenarios in which a large amount of services can be offered and used by drivers and authorities to improve the management of the city's car parks and standards of quality of life. Based on literature on smart parking management system (SPMS), we have established the most important services needed such as reservation, orientation, synchronization, and security. The dynamic distributed and open aspect of the problem led us to adopt a multi-agent modeling to ensure continuous evolution and flexibility of the management system. In this conceptual paper, we propose to structure those services on a multi-agent system (MAS) that covers the whole functions of a distributed SPMS. Each service is provided as an autonomous agent, able to communicate and collaborate with the others to propose optimized parking space to customers.</span>
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19

Chai, Runqi, Antonios Tsourdos, Al Savvaris, Senchun Chai, and Yuanqing Xia. "Two-Stage Trajectory Optimization for Autonomous Ground Vehicles Parking Maneuver." IEEE Transactions on Industrial Informatics 15, no. 7 (July 2019): 3899–909. http://dx.doi.org/10.1109/tii.2018.2883545.

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20

Lee, Chen-Kui, Chun-Liang Lin, and Bing-Min Shiu. "Autonomous Vehicle Parking Using Hybrid Artificial Intelligent Approach." Journal of Intelligent and Robotic Systems 56, no. 3 (March 6, 2009): 319–43. http://dx.doi.org/10.1007/s10846-009-9319-9.

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21

Wang, Yongji, and M. P. Cartmell. "Autonomous vehicle parallel parking design using function fitting approaches." Robotica 16, no. 2 (March 1998): 159–70. http://dx.doi.org/10.1017/s0263574798000496.

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One of the most fundamental problems in the development of an intelligent highway system or an autonomous mobile robot system for factory use is to find the necessary input control variables for smooth and safe movement of the vehicle, or robot, between any two configurations. In this paper it is demonstrated that this problem can be converted into one of finding a fitting function which satisfies the boundary conditions. Three curves, a quintic polynomial, a cubic polynomial and a triangular function are developed to perform the parallel transfer manoeuvre which forms the basis of several important manoeuvres such as reverse parking, moving off, negotiating a stationary obstacle, overtaking a moving vehicle, and changing lane. A detailed discussion of the effect of the vehicle's steering angle limit on the feasibility of these manoeuvres is presented. Simulation results using three typical vehicles, a long commercial vehicle, an ordinary car, and a small laboratory robot, travelling along three curves are also presented and discussed. Based on the comparative study, some suggestions for further work are made. Compared with other methods, this approach is simple and provides excellent simulation of human driver techniques. The paper concludes with a focused discussion about the integration of these techniques with satellite based GPS systems for automated vehicle guidance on highways.
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22

Liao, Zhanzhi, Jian Wang, Yuanyuan Li, and Xiaowei Hu. "Managing the Morning Commute Problem with Tradable Credit Schemes under a Fully Autonomous Vehicle Environment." Systems 10, no. 6 (October 29, 2022): 200. http://dx.doi.org/10.3390/systems10060200.

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This study investigates the morning commute problem in a single corridor with a bottleneck under a fully automated vehicle environment. By extending Vickrey’s bottleneck model, we formulate the joint decisions on departure time and parking choices of morning commuters who make trade-offs among travel cost, the cost associated with parking, and the cost of tradable credits. To alleviate traffic congestion and improve social welfare, we propose a time-varying tradable credit scheme and integrate it with the morning commute problem. We explore the travel patterns and the optimal design of tradable credit schemes for the morning commute problem with homogeneous and heterogeneous commuters, respectively. For the homogeneous case, we derive the conditions on the tradable credit scheme to ensure the existence of equilibrium. The system-wide travel cost decreases with parking density after tradable credits are incorporated. Additionally, the efficiency of the tradable credits scheme can be improved by increasing the rate of credit charge rate. For the heterogeneous case, we propose an initial distribution strategy and combine it with the optimal tradable credit in order to guarantee social equity. The commuters with a low value of time (VOT) should be allocated more credits and the commuters with high VOT should be charged more credits. For both cases at system optimum, we find that the equilibrium price of tradable credits increases with parking density and decreases with the total amount of tradable credits.
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23

Naji, Baligh, Chokri Abdelmoula, and Mohamed Masmoudi. "A Real Time Algorithm for Versatile Mode Parking System and Its Implementation on FPGA Board." Applied Sciences 12, no. 2 (January 10, 2022): 655. http://dx.doi.org/10.3390/app12020655.

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This paper presents the design and development of a technique for an Autonomous and Versatile mode Parking System (AVPS) that combines a various number of parking modes. The proposed approach is different from that of many developed parking systems. Previous research has focused on choosing only a parking lot starting from two parking modes (which are parallel and perpendicular). This research aims at developing a parking system that automatically chooses a parking lot starting from four parking modes. The automatic AVPS was proposed for the car-parking control problem, and could be potentially exploited for future vehicle generation. A specific mode can be easily computed using the proposed strategy. A variety of candidate modes could be generated using one developed real time VHDL (VHSIC Hardware Description Language) algorithm providing optimal solutions with performance measures. Based on simulation and experimental results, the AVPS is able to find and recognize in advance which parking mode to select. This combination describes full implementation on a mobile robot, such as a car, based on a specific FPGA (Field-Programmable Gate Array) card. To prove the effectiveness of the proposed innovation, an evaluation process comparing the proposed technique with existing techniques was conducted and outlined.
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24

Diachuk, Maksym, Said M. Easa, and Joel Bannis. "Path and Control Planning for Autonomous Vehicles in Restricted Space and Low Speed." Infrastructures 5, no. 5 (May 12, 2020): 42. http://dx.doi.org/10.3390/infrastructures5050042.

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This paper presents models of path and control planning for the parking, docking, and movement of autonomous vehicles at low speeds, considering space constraints. Given the low speed of motion, and in order to test and approve the proposed algorithms, vehicle kinematic models are used. Recent works on the development of parking algorithms for autonomous vehicles are reviewed. Bicycle kinematic models for vehicle motion are considered for three basic types of vehicles: passenger car, long wheelbase truck, and articulated vehicles with and without steered semitrailer axes. Mathematical descriptions of systems of differential equations in matrix form and expressions for determining the linearization elements of nonlinear motion equations that increase the speed of finding the optimal solution are presented. Options are proposed for describing the interaction of vehicle overall dimensions with the space boundaries, within which a maneuver should be performed. An original algorithm that considers numerous constraints is developed for determining vehicle permissible positions within the closed boundaries of the parking area, which are directly used in the iterative process of searching for the optimal plan solution using nonlinear model predictive control (NMPC). The process of using NMPC to find the best trajectories and control laws while moving in a semi-limited space of constant curvature (turnabouts, roundabouts) are described. Simulation tests were used to validate the proposed models for both constrained and unconstrained conditions and the output (state-space) and control parameters’ dependencies are shown. The proposed models represent an initial effort to model the movement of autonomous vehicles for parking and have the potential for other highway applications.
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25

Hu, Zhaozheng, Hanbiao Xiao, Zhe Zhou, and Na Li. "Detection of parking slots occupation by temporal difference of inverse perspective mapping from vehicle-borne monocular camera." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 235, no. 12 (April 28, 2021): 3119–26. http://dx.doi.org/10.1177/09544070211005579.

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Detection of parking slots occupation is a crucial task for parking assistance, automatic parking, and autonomous driving systems. This paper proposed a novel method, called Temporal Difference of Inverse Perspective Mapping Difference (TD-IPM), without explicit 3D reconstruction or objection detection. In this method, temporal images from monocular camera are first inverse perspective mapped (IPM) onto the ground plane based on camera calibration results. Second, we proposed an algorithm, called Block Consensus based on Rotation Invariance Phase-Only Correlation (BC-RIPOC), for fast and robust motion estimation. From the estimated motion, we can align these two IPM images and generate IPM difference map. Third, the IPM difference map is segmented and filtered to generate a binary map that can distinguish objects on the ground plane or not for occupation detection. The obstacle is readily localized from the difference map as well. The proposed TD-IPM method has been validated in both underground and outdoor parking lots. Experimental results demonstrate that the proposed TD-IPM method can successfully detect various occupation objects, such as vehicles, cones, lockers, and others, with 97.9% average detection accuracy and speed of 17.5 frames per second (fps). The proposed method suggests an effective and low-cost solution to intelligent parking systems.
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26

Li, T. H. S., and Shih-Jie Chang. "Autonomous fuzzy parking control of a car-like mobile robot." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 33, no. 4 (July 2003): 451–65. http://dx.doi.org/10.1109/tsmca.2003.811766.

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27

Vu, Trieu Minh, Reza Moezzi, Jindrich Cyrus, Jaroslav Hlava, and Michal Petru. "Feasible Trajectories Generation for Autonomous Driving Vehicles." Applied Sciences 11, no. 23 (November 24, 2021): 11143. http://dx.doi.org/10.3390/app112311143.

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This study presents smooth and fast feasible trajectory generation for autonomous driving vehicles subject to the vehicle physical constraints on the vehicle power, speed, acceleration as well as the hard limitations of the vehicle steering angle and the steering angular speed. This is due to the fact the vehicle speed and the vehicle steering angle are always in a strict relationship for safety purposes, depending on the real vehicle driving constraints, the environmental conditions, and the surrounding obstacles. Three different methods of the position quintic polynomial, speed quartic polynomial, and symmetric polynomial function for generating the vehicle trajectories are presented and illustrated with simulations. The optimal trajectory is selected according to three criteria: Smoother curve, smaller tracking error, and shorter distance. The outcomes of this paper can be used for generating online trajectories for autonomous driving vehicles and auto-parking systems.
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28

Yigitcanlar, Wilson, and Kamruzzaman. "Disruptive Impacts of Automated Driving Systems on the Built Environment and Land Use: An Urban Planner’s Perspective." Journal of Open Innovation: Technology, Market, and Complexity 5, no. 2 (April 24, 2019): 24. http://dx.doi.org/10.3390/joitmc5020024.

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Cities have started to restructure themselves into ‘smart cities’ to address the challenges of the 21st Century—such as climate change, sustainable development, and digital disruption. One of the major obstacles to success for a smart city is to tackle the mobility and accessibility issues via ‘smart mobility’ solutions. At the verge of the age of smart urbanism, autonomous vehicle technology is seen as an opportunity to realize the smart mobility vision of cities. However, this innovative technological advancement is also speculated to bring a major disruption in urban transport, land use, employment, parking, car ownership, infrastructure design, capital investment decisions, sustainability, mobility, and traffic safety. Despite the potential threats, urban planners and managers are not yet prepared to develop autonomous vehicle strategies for cities to deal with these threats. This is mainly due to a lack of knowledge on the social implications of autonomous capabilities and how exactly they will disrupt our cities. This viewpoint provides a snapshot of the current status of vehicle automation, the direction in which the field is moving forward, the potential impacts of systematic adoption of autonomous vehicles, and how urban planners can mitigate the built environment and land use disruption of autonomous vehicles.
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29

Walambe, Rahee, Narendra Patwardhan, and Vrunda Joshi. "Development of Auto-parking and Collision Avoidance Algorithms on Car type Autonomous Mobile Robots." IFAC-PapersOnLine 53, no. 1 (2020): 567–72. http://dx.doi.org/10.1016/j.ifacol.2020.06.095.

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30

Xu, Dezhi, Yan Shi, and Zhicheng Ji. "Model-Free Adaptive Discrete-Time Integral Sliding-Mode-Constrained-Control for Autonomous 4WMV Parking Systems." IEEE Transactions on Industrial Electronics 65, no. 1 (January 2018): 834–43. http://dx.doi.org/10.1109/tie.2017.2739680.

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31

Chung, Yi, and Yee-Pien Yang. "Hardware-in-the-Loop Simulation of Self-Driving Electric Vehicles by Dynamic Path Planning and Model Predictive Control." Electronics 10, no. 19 (October 8, 2021): 2447. http://dx.doi.org/10.3390/electronics10192447.

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This paper applies a dynamic path planning and model predictive control (MPC) to simulate self-driving and parking for an electric van on a hardware-in-the-loop (HiL) platform. The hardware platform is a simulator which consists of an electric power steering system, accelerator and brake pedals, and an Nvidia drive PX2 with a robot operating system (ROS). The vehicle dynamics model, sensors, controller, and test field map are virtually built with the PreScan simulation platform. Both manual and autonomous driving modes can be simulated, and a graphic user interface allows a test driver to select a target parking space on a display screen. Three scenarios are demonstrated: forward parking, reverse parking, and obstacle avoidance. When the vehicle perceives an obstacle, the map is updated and the route is adaptively planned. The effectiveness of the proposed MPC is verified in experiments and proved to be superior to a traditional proportional–integral–derivative controller with regards to safety, energy-saving, comfort, and agility.
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Amarasinghe, Dilan, George K. I. Mann, and Raymond G. Gosine. "Vision-based hybrid control scheme for autonomous parking of a mobile robot." Advanced Robotics 21, no. 8 (January 2007): 905–30. http://dx.doi.org/10.1163/156855307780851993.

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33

Shin, Hee-Seok, Sung-Haek Jung, and Jung-Ha Kim. "Development of Path Generation & Tracking Algorithm for Reverse Perpendicular Parking System of Autonomous Tractor - Trailer." Journal of Institute of Control, Robotics and Systems 28, no. 2 (February 28, 2022): 109–18. http://dx.doi.org/10.5302/j.icros.2022.21.0203.

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34

Geza Katona and Janos Juhasz. "The History of the Transport System Development and Future with Sharing and Autonomous Systems." Communications - Scientific letters of the University of Zilina 22, no. 1 (January 2, 2020): 25–34. http://dx.doi.org/10.26552/com.c.2020.1.25-34.

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The world today is rapidly changing with the spread of automation. Moreover, urban life is more and more common and the number of inhabitants is rapidly growing. This time is the renaissance of bicycle-based vehicles, especially of electric power assisted bicycles. Vehicle sharing is also getting more popular. These progresses will have a crucial effect on traditional transportation networks. Moreover, the traffic volume, parking facilities and public space usage can be drastically modified. A research data is introduced from Paris and Lisbon, which helps to forecast the effect of the autonomous and sharing systems. This evolution can be advantageous, but it can also bring some disadvantages. In this article, these changes are summarized in the mirror of history, especially the Futurama concept and the transport system changes in Los Angeles. This overview will be a good basis for further research and for the development of a routing algorithm.
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Yoshida, Hidehisa, Manabu Omae, and Takahiro Wada. "Toward Next Active Safety Technology of Intelligent Vehicle." Journal of Robotics and Mechatronics 27, no. 6 (December 18, 2015): 610–16. http://dx.doi.org/10.20965/jrm.2015.p0610.

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<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00270006/02.jpg"" width=""300"" /> Intelligent vehicle technologies</div>Autonomous driving has attracted attention in recent years from the viewpoint of energy consumption and traffic accident prevention; hence, its introduction has been desired. In Japan, various accident prevention safety technologies were developed for cooperative control between the driver and the vehicle system. For example, “adaptive cruise control system” and “lane departure warning system” were developed in the 1990s and “lane keeping assist system” and “braking control device for reducing collision damage” in the early stages of the 2000s. Later in Europe, autonomous driving systems were actively studied, and an automated braking system to avoid collisions was introduced in the market in the second half of the 2000s. Studies and development have been promoted for the practical use of active safety technologies based on autonomous driving technologies. Autonomous driving technologies could be applied to various cases, such as convoy travelling to compensate for the insufficient number of professional drivers or to improve their work environment, last-one-mile travelling from a public transportation station to home for the elderly, people who have children and people who need assistance, dead-man system for sudden illness of the driver, and automated parking for assisting the driver who is not good at it, or for the parking space to be effectively used. In this paper, an overview of the transition and history of vehicular technologies for safety and reliability is given. In particular, active safety technologies for traffic accident prevention and the necessary related technology trend are reviewed, and future problems are pointed out.
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Hwang, Kitae, In Hwan Jung, and Jae Moon Lee. "Implementation of Autonomous Driving on RC-CAR with Raspberry PI and AI Server." Webology 19, no. 1 (January 20, 2022): 4444–58. http://dx.doi.org/10.14704/web/v19i1/web19293.

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A lot of research is being done on autonomous driving vehicles or robots that recognize objects and drive themselves without human intervention. In order to develop autonomous driving technology, there is a fundamental difficulty in securing expensive real cars equipped with various sensors. In this paper, an autonomous driving system development platform was developed using an inexpensive RC-Car, and a test system that can test various algorithms related to autonomous driving was introduced. In the system developed in this study, the single board computer Raspberry PI was mounted on the RC-Car to control the car, and the autonomous driving-related algorithms were implemented in a separate AI server, and they communicated with the message-based ROS protocol. In addition, those who want to develop an autonomous driving system can easily attach desired sensors to the RC-Car, increasing scalability. In this paper, almost all algorithms related to autonomous driving have been implemented. A simple autonomous driving RC-Car system was actually implemented and operation was verified by designing and implementing algorithms such as lane recognition, driving along the lane, obstacle detection and stopping, traffic light recognition, driving between smooth and sharp curves, and autonomous parking. In sharp curves, the angle of the lane was tracked in a short period to prevent the vehicle from crossing the lane. In addition, we developed an Android app that can manually control the car and monitor the video from the camera in time. This study presented and solved various difficulties that could not be known by developing an autonomous driving algorithm using simulators.
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Meng, Jie, Shuting Wang, Gen Li, Liquan Jiang, Xiaolong Zhang, Chao Liu, and Yuanlong Xie. "Iterative-learning error compensation for autonomous parking of mobile manipulator in harsh industrial environment." Robotics and Computer-Integrated Manufacturing 68 (April 2021): 102077. http://dx.doi.org/10.1016/j.rcim.2020.102077.

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38

Ahmed, Hafiz Usman, Ying Huang, Pan Lu, and Raj Bridgelall. "Technology Developments and Impacts of Connected and Autonomous Vehicles: An Overview." Smart Cities 5, no. 1 (March 17, 2022): 382–404. http://dx.doi.org/10.3390/smartcities5010022.

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The scientific advancements in the vehicle and infrastructure automation industry are progressively improving nowadays to provide benefits for the end-users in terms of traffic congestion reduction, safety enhancements, stress-free travels, fuel cost savings, and smart parking, etc. The advances in connected, autonomous, and connected autonomous vehicles (CV, AV, and CAV) depend on the continuous technology developments in the advanced driving assistance systems (ADAS). A clear view of the technology developments related to the AVs will give the users insights on the evolution of the technology and predict future research needs. In this paper, firstly, a review is performed on the available ADAS technologies, their functions, and the expected benefits in the context of CVs, AVs, and CAVs such as the sensors deployed on the partial or fully automated vehicles (Radar, LiDAR, etc.), the communication systems for vehicle-to-vehicle and vehicle-to-infrastructure networking, and the adaptive and cooperative adaptive cruise control technology (ACC/CACC). Secondly, for any technologies to be applied in practical AVs related applications, this study also includes a detailed review in the state/federal guidance, legislation, and regulations toward AVs related applications. Last but not least, the impacts of CVs, AVs, and CAVs on traffic are also reviewed to evaluate the potential benefits as the AV related technologies penetrating in the market. Based on the extensive reviews in this paper, the future related research gaps in technology development and impact analysis are also discussed.
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Min, Haigen, Xia Wu, Chaoyi Cheng, and Xiangmo Zhao. "Kinematic and Dynamic Vehicle Model-Assisted Global Positioning Method for Autonomous Vehicles with Low-Cost GPS/Camera/In-Vehicle Sensors." Sensors 19, no. 24 (December 9, 2019): 5430. http://dx.doi.org/10.3390/s19245430.

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Real-time, precise and low-cost vehicular positioning systems associated with global continuous coordinates are needed for path planning and motion control in autonomous vehicles. However, existing positioning systems do not perform well in urban canyons, tunnels and indoor parking lots. To address this issue, this paper proposes a multi-sensor positioning system that combines a global positioning system (GPS), a camera and in-vehicle sensors assisted by kinematic and dynamic vehicle models. First, the system eliminates image blurring and removes false feature correspondences to ensure the local accuracy and stability of the visual simultaneous localisation and mapping (SLAM) algorithm. Next, the global GPS coordinates are transferred to a local coordinate system that is consistent with the visual SLAM process, and the GPS and visual SLAM tracks are calibrated with the improved weighted iterative closest point and least absolute deviation methods. Finally, an inverse coordinate system conversion is conducted to obtain the position in the global coordinate system. To improve the positioning accuracy, information from the in-vehicle sensors is fused with the interacting multiple-model extended Kalman filter based on kinematic and dynamic vehicle models. The developed algorithm was verified via intensive simulations and evaluated through experiments using KITTI benchmarks (A project of Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago) and data captured using our autonomous vehicle platform. The results show that the proposed positioning system improves the accuracy and reliability of positioning in environments in which the Global Navigation Satellite System is not available. The developed system is suitable for the positioning and navigation of autonomous vehicles.
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40

Roman, Adrian-Silviu, Béla Genge, Adrian-Vasile Duka, and Piroska Haller. "Privacy-Preserving Tampering Detection in Automotive Systems." Electronics 10, no. 24 (December 18, 2021): 3161. http://dx.doi.org/10.3390/electronics10243161.

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Modern auto-vehicles are built upon a vast collection of sensors that provide large amounts of data processed by dozens of Electronic Control Units (ECUs). These, in turn, monitor and control advanced technological systems providing a large palette of features to the vehicle’s end-users (e.g., automated parking, autonomous vehicles). As modern cars become more and more interconnected with external systems (e.g., cloud-based services), enforcing privacy on data originating from vehicle sensors is becoming a challenging research topic. In contrast, deliberate manipulations of vehicle components, known as tampering, require careful (and remote) monitoring of the vehicle via data transmissions and processing. In this context, this paper documents an efficient methodology for data privacy protection, which can be integrated into modern vehicles. The approach leverages the Fast Fourier Transform (FFT) as a core data transformation algorithm, accompanied by filters and additional transformations. The methodology is seconded by a Random Forest-based regression technique enriched with further statistical analysis for tampering detection in the case of anonymized data. Experimental results, conducted on a data set collected from the On-Board Diagnostics (OBD II) port of a 2015 EUR6 Skoda Rapid 1.2 L TSI passenger vehicle, demonstrate that the restored time-domain data preserves the characteristics required by additional processing algorithms (e.g., tampering detection), showing at the same time an adjustable level of privacy. Moreover, tampering detection is shown to be 100% effective in certain scenarios, even in the context of anonymized data.
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41

Al-Nuaimi, Mohammed, Sapto Wibowo, Hongyang Qu, Jonathan Aitken, and Sandor Veres. "Hybrid Verification Technique for Decision-Making of Self-Driving Vehicles." Journal of Sensor and Actuator Networks 10, no. 3 (June 29, 2021): 42. http://dx.doi.org/10.3390/jsan10030042.

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The evolution of driving technology has recently progressed from active safety features and ADAS systems to fully sensor-guided autonomous driving. Bringing such a vehicle to market requires not only simulation and testing but formal verification to account for all possible traffic scenarios. A new verification approach, which combines the use of two well-known model checkers: model checker for multi-agent systems (MCMAS) and probabilistic model checker (PRISM), is presented for this purpose. The overall structure of our autonomous vehicle (AV) system consists of: (1) A perception system of sensors that feeds data into (2) a rational agent (RA) based on a belief–desire–intention (BDI) architecture, which uses a model of the environment and is connected to the RA for verification of decision-making, and (3) a feedback control systems for following a self-planned path. MCMAS is used to check the consistency and stability of the BDI agent logic during design-time. PRISM is used to provide the RA with the probability of success while it decides to take action during run-time operation. This allows the RA to select movements of the highest probability of success from several generated alternatives. This framework has been tested on a new AV software platform built using the robot operating system (ROS) and virtual reality (VR) Gazebo Simulator. It also includes a parking lot scenario to test the feasibility of this approach in a realistic environment. A practical implementation of the AV system was also carried out on the experimental testbed.
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42

Poulose, Alwin, and Dong Seog Han. "Hybrid Deep Learning Model Based Indoor Positioning Using Wi-Fi RSSI Heat Maps for Autonomous Applications." Electronics 10, no. 1 (December 22, 2020): 2. http://dx.doi.org/10.3390/electronics10010002.

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Positioning using Wi-Fi received signal strength indication (RSSI) signals is an effective method for identifying the user positions in an indoor scenario. Wi-Fi RSSI signals in an autonomous system can be easily used for vehicle tracking in underground parking. In Wi-Fi RSSI signal based positioning, the positioning system estimates the signal strength of the access points (APs) to the receiver and identifies the user’s indoor positions. The existing Wi-Fi RSSI based positioning systems use raw RSSI signals obtained from APs and estimate the user positions. These raw RSSI signals can easily fluctuate and be interfered with by the indoor channel conditions. This signal interference in the indoor channel condition reduces localization performance of these existing Wi-Fi RSSI signal based positioning systems. To enhance their performance and reduce the positioning error, we propose a hybrid deep learning model (HDLM) based indoor positioning system. The proposed HDLM based positioning system uses RSSI heat maps instead of raw RSSI signals from APs. This results in better localization performance for Wi-Fi RSSI signal based positioning systems. When compared to the existing Wi-Fi RSSI based positioning technologies such as fingerprint, trilateration, and Wi-Fi fusion approaches, the proposed approach achieves reasonably better positioning results for indoor localization. The experiment results show that a combination of convolutional neural network and long short-term memory network (CNN-LSTM) used in the proposed HDLM outperforms other deep learning models and gives a smaller localization error than conventional Wi-Fi RSSI signal based localization approaches. From the experiment result analysis, the proposed system can be easily implemented for autonomous applications.
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Rodríguez-Corbo, Fidel Alejandro, Leyre Azpilicueta, Mikel Celaya-Echarri, Peio Lopez-Iturri, Ana V. Alejos, and Francisco Falcone. "Deterministic Propagation Approach for Millimeter-Wave Outdoor Smart Parking Solution Deployment." Engineering Proceedings 2, no. 1 (November 14, 2020): 81. http://dx.doi.org/10.3390/ecsa-7-08231.

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Impact factor as an indicator of efficiency or sustainability is entirely correlated with the continuous development of the smart city concept technology application. Intelligent transportation systems (ITSs) and particularly autonomous vehicles are expected to play an important role in this challenging environment. Fast and secure connections will be pivotal in order to achieve this new vehicular communications’ application era. The use of millimeter-wave (mmWave) frequency range is the most promising approach to allow these real-time, high-demand applications that require higher bandwidth with the minimum possible latency. However, an in-depth mmWave-channel characterization of the environment is required for a proper mmWave-based solution deployment. In this work, a complete radio wave propagation channel characterization for a mmWave smart parking solution deployment in a complex outdoor environment was assessed at a 28 GHz frequency band. The considered scenario is a parking lot placed in an open free university campus area surrounded by inhomogeneous vegetation. The vehicle and vegetation density within the scenario, in terms of inherent transceivers density and communication impairments, leads to overall system operation challenges, given by multiple communication links operation at line-of-sight (LOS) and non-line-of-sight (NLOS) conditions. By means of an in-house developed 3D ray launching (3D-RL) algorithm, the impact of variable vegetation density is addressed, providing precise modelling estimations of large-scale multipath propagation effects in terms of received power levels and path loss. The obtained results along with the proposed simulation methodology can aid in an adequate characterization of an mmWave communication channel for new vehicular communications networks, applications, and deployments, considering the outdoor conditions as well as the impact of different vegetation densities, for current as well as for future wireless technologies.
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44

Dong, Tianyu, Qiong Wang, and Lingxing Meng. "Research on Satisfaction of Driverless Function Based on the Artificial Intelligence Algorithm." Mobile Information Systems 2022 (September 13, 2022): 1–7. http://dx.doi.org/10.1155/2022/6831049.

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With the popularization of driverless technology, more and more vehicles have begun to apply this technology. Under these circumstances, user satisfaction with driverless function begins to have an increasing impact on car sales. This paper adopts the KANO model, a two-dimensional model, to analyze users’ attitudes toward driverless functions. It is found that users have a high dependence on functions such as forward collision warning, autonomous emergency braking, and stated-speed sign recognition. Therefore, relevant enterprises should continue to develop these functions. Besides, users have high expectations for functions such as blind spot detection systems and rear cross-traffic alerts. Enterprises can achieve more support from users by optimizing these functions. Functions like lane-keeping assist, lane departure warning, parking distance control, and door open warning systems belong to indifferent attributes of driverless function, which are not cared about by users. There is no need for enterprises to optimize these functions. However, the lane change assist system has been criticized by users and should be improved by corresponding manufacturers.
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45

Flores Fernández, Alberto, Eduardo Sánchez Morales, Michael Botsch, Christian Facchi, and Andrés García Higuera. "Generation of Correction Data for Autonomous Driving by Means of Machine Learning and On-Board Diagnostics." Sensors 23, no. 1 (December 23, 2022): 159. http://dx.doi.org/10.3390/s23010159.

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A highly accurate reference vehicle state is a requisite for the evaluation and validation of Autonomous Driving (AD) and Advanced Driver Assistance Systems (ADASs). This highly accurate vehicle state is usually obtained by means of Inertial Navigation Systems (INSs) that obtain position, velocity, and Course Over Ground (COG) correction data from Satellite Navigation (SatNav). However, SatNav is not always available, as is the case of roofed places, such as parking structures, tunnels, or urban canyons. This leads to a degradation over time of the estimated vehicle state. In the present paper, a methodology is proposed that consists on the use of a Machine Learning (ML)-method (Transformer Neural Network—TNN) with the objective of generating highly accurate velocity correction data from On-Board Diagnostics (OBD) data. The TNN obtains OBD data as input and measurements from state-of-the-art reference sensors as a learning target. The results show that the TNN is able to infer the velocity over ground with a Mean Absolute Error (MAE) of 0.167 kmh (0.046 ms) when a database of 3,428,099 OBD measurements is considered. The accuracy decreases to 0.863 kmh (0.24 ms) when only 5000 OBD measurements are used. Given that the obtained accuracy closely resembles that of state-of-the-art reference sensors, it allows INSs to be provided with accurate velocity correction data. An inference time of less than 40 ms for the generation of new correction data is achieved, which suggests the possibility of online implementation. This supports a highly accurate estimation of the vehicle state for the evaluation and validation of AD and ADAS, even in SatNav-deprived environments.
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Rahman, Muhammed Tahsin, Tashfeen Karamat, Sidney Givigi, and Aboelmagd Noureldin. "Improving Multisensor Positioning of Land Vehicles with Integrated Visual Odometry for Next-Generation Self-Driving Cars." Journal of Advanced Transportation 2018 (2018): 1–12. http://dx.doi.org/10.1155/2018/6513970.

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For their complete realization, autonomous vehicles (AVs) fundamentally rely on the Global Navigation Satellite System (GNSS) to provide positioning and navigation information. However, in area such as urban cores, parking lots, and under dense foliage, which are all commonly frequented by AVs, GNSS signals suffer from blockage, interference, and multipath. These effects cause high levels of errors and long durations of service discontinuity that mar the performance of current systems. The prevalence of vision and low-cost inertial sensors provides an attractive opportunity to further increase the positioning and navigation accuracy in such GNSS-challenged environments. This paper presents enhancements to existing multisensor integration systems utilizing the inertial navigation system (INS) to aid in Visual Odometry (VO) outlier feature rejection. A scheme called Aided Visual Odometry (AVO) is developed and integrated with a high performance mechanization architecture utilizing vehicle motion and orientation sensors. The resulting solution exhibits improved state covariance convergence and navigation accuracy, while reducing computational complexity. Experimental verification of the proposed solution is illustrated through three real road trajectories, over two different land vehicles, and using two low-cost inertial measurement units (IMUs).
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47

Farooq, Muhammad Shoaib, Haris Khalid, Ansif Arooj, Tariq Umer, Aamer Bilal Asghar, Jawad Rasheed, Raed M. Shubair, and Amani Yahyaoui. "A Conceptual Multi-Layer Framework for the Detection of Nighttime Pedestrian in Autonomous Vehicles Using Deep Reinforcement Learning." Entropy 25, no. 1 (January 9, 2023): 135. http://dx.doi.org/10.3390/e25010135.

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The major challenge faced by autonomous vehicles today is driving through busy roads without getting into an accident, especially with a pedestrian. To avoid collision with pedestrians, the vehicle requires the ability to communicate with a pedestrian to understand their actions. The most challenging task in research on computer vision is to detect pedestrian activities, especially at nighttime. The Advanced Driver-Assistance Systems (ADAS) has been developed for driving and parking support for vehicles to visualize sense, send and receive information from the environment but it lacks to detect nighttime pedestrian actions. This article proposes a framework based on Deep Reinforcement Learning (DRL) using Scale Invariant Faster Region-based Convolutional Neural Networks (SIFRCNN) technologies to efficiently detect pedestrian operations through which the vehicle, as agents train themselves from the environment and are forced to maximize the reward. The SIFRCNN has reduced the running time of detecting pedestrian operations from road images by incorporating Region Proposal Network (RPN) computation. Furthermore, we have used Reinforcement Learning (RL) for optimizing the Q-values and training itself to maximize the reward after getting the state from the SIFRCNN. In addition, the latest incarnation of SIFRCNN achieves near-real-time object detection from road images. The proposed SIFRCNN has been tested on KAIST, City Person, and Caltech datasets. The experimental results show an average improvement of 2.3% miss rate of pedestrian detection at nighttime compared to the other CNN-based pedestrian detectors.
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48

Glazebrook, Garry, and Peter Newman. "The City of the Future." Urban Planning 3, no. 2 (April 24, 2018): 1–20. http://dx.doi.org/10.17645/up.v3i2.1247.

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Limiting global warming to 1.5 °C will require rapid decarbonisation of the world’s electricity and transport systems. This must occur against a background of continuing urbanisation and the shift to the information economy. While replacement of fossil fuels in electricity generation is underway, urban transport is currently dominated by petrol and diesel-powered vehicles. The City of the Future will need to be built around a different transport and urban paradigm. This article argues that the new model will be a polycentric city linked by fast electric rail, with local access based on autonomous “community”-owned electric cars and buses supplemented by bicycles, electric bikes and scooters, with all electricity generated from renewables. Less space will be wasted on roads and parking, enabling higher accessibility yet more usable public open space. Building the cities of the future will require national governments to accelerate local initiatives through appropriate policy settings and strategic investment. The precise way in which individual cities move into the future will vary, and the article illustrates how the transformation could work for Australian cities, like Sydney, currently some of the most car dependent in the world, using new financial and city partnerships.
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Tenhundfeld, Nathan L., Ewart J. de Visser, Kerstin S. Haring, Anthony J. Ries, Victor S. Finomore, and Chad C. Tossell. "Calibrating Trust in Automation Through Familiarity With the Autoparking Feature of a Tesla Model X." Journal of Cognitive Engineering and Decision Making 13, no. 4 (August 6, 2019): 279–94. http://dx.doi.org/10.1177/1555343419869083.

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Because one of the largest influences on trust in automation is the familiarity with the system, we sought to examine the effects of familiarity on driver interventions while using the autoparking feature of a Tesla Model X. Participants were either told or shown how the autoparking feature worked. Results showed a significantly higher initial driver intervention rate when the participants were only told how to employ the autoparking feature, than when shown. However, the intervention rate quickly leveled off, and differences between conditions disappeared. The number of interventions and the distances from the parking anchoring point (a trashcan) were used to create a new measure of distrust in autonomy. Eyetracking measures revealed that participants disengaged from monitoring the center display as the experiment progressed, which could be a further indication of a lowering of distrust in the system. Combined, these results have important implications for development and design of explainable artificial intelligence and autonomous systems. Finally, we detail the substantial hurdles encountered while trying to evaluate “autonomy in the wild.” Our research highlights the need to re-evaluate trust concepts in high-risk, high-consequence environments.
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PUERCHAMBUD LADINO, CRISTIAN EDUARDO, and JUAN DAVID HENAO ARIZA. "PROMETEO SISTEMA AUTÓNOMO PARA EL MANEJO DE UNA TORRE VERTICAL DE PARQUEADEROS." Revista Ingeniería, Matemáticas y Ciencias de la Información 5, no. 9 (January 15, 2018): 41–54. http://dx.doi.org/10.21017/rimci.2018.v5.n9.a39.

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