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Journal articles on the topic 'Intelligent vehicles localization'
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1
Hui Fang, Chunxiang Wang, Ming Yang, and Ruqing Yang. "Ground-Texture-Based Localization for Intelligent Vehicles." IEEE Transactions on Intelligent Transportation Systems 10, no. 3 (September 2009): 463–68. http://dx.doi.org/10.1109/tits.2009.2026445.
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Wan, Liangtian, Mingyue Zhang, Lu Sun, and Xianpeng Wang. "Machine Learning Empowered IoT for Intelligent Vehicle Location in Smart Cities." ACM Transactions on Internet Technology 21, no. 3 (August 31, 2021): 1–25. http://dx.doi.org/10.1145/3448612.
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Intelligent Transportation System (ITS) can boost the development of smart cities, and artificial intelligence and edge computing are key technologies that support the implementation of ITS. Vehicle localization is critical for ITS since the safety driving and location-aware serves highly depend on the accurate location information. In this article, we construct a vehicle localization system architecture composed of multiple Internet of Things (IoT) with arbitrary array configuration and a large amount of vehicles in smart cities. In order to deal with the coexisting of circular and non-circular signals transmitted by vehicles, we proposed several vehicle number estimation methods for non-circular signals. Based on the machine learning technique, we extend the vehicle number estimation method into mixed signals in more complex scenario of smart cities. Then the DOA estimation method for non-circular signals based on IoT is proposed, and then the performance of this method is analyzed as well. Simulation outcomes verify the excellent performance of the proposed vehicle number estimation methods and the DOA estimation method in smart cities, and the vehicle positions can be achieved with high estimation accuracy.
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Wang, Mingyang, Xinbo Chen, Pengyuan Lv, Baobao Jin, Wei Wang, and Yong Shen. "UWB Based Relative Planar Localization with Enhanced Precision for Intelligent Vehicles." Actuators 10, no. 7 (June 26, 2021): 144. http://dx.doi.org/10.3390/act10070144.
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Along with the rapid development of advanced driving assistance systems for intelligent vehicles, essential functions such as forward collision warning and collaborative cruise control need to detect the relative positions of surrounding vehicles. This paper proposes a relative planar localization system based on the ultra-wideband (UWB) ranging technology. Three UWB modules are installed on the top of each vehicle. Because of the limited space on the vehicle roof compared with the ranging error, the traditional triangulation method leads to significant positioning errors. Therefore, an optimal localization algorithm combining homotopy and the Levenberg–Marquardt method is first proposed to enhance the precision. The triangular side lengths and directed area are introduced as constraints. Secondly, a UWB sensor error self-correction method is presented to further improve the ranging accuracy. Finally, we carry out simulations and experiments to show that the presented algorithm in this paper significantly improves the relative position and orientation precision of both the pure UWB localization system and the fusion system integrated with dead reckoning.
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Huang, Gang, Zhaozheng Hu, Mengchao Mu, Xianglong Wang, and Fan Zhang. "Multi-View and Multi-Scale Localization for Intelligent Vehicles in Underground Parking Lots." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 11 (June 19, 2019): 791–800. http://dx.doi.org/10.1177/0361198119857032.
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Because of limited access to global positioning system (GPS) signals, accurate and reliable localization for intelligent vehicles in underground parking lots is still an open problem. This paper proposes a multi-view and multi-scale localization method aiming at solving this problem. The proposed method is divided into an offline mapping stage and an online localization stage. In the mapping stage, the offline map is generated by fusing 3-D information, WiFi features, visual features, and trajectory from visual odometry (VO). In the localization stage, WiFi fingerprint matching is exploited for coarse localization. Based on the result of coarse localization, multi-view localization is exploited for image-level localization. Finally, metric localization is exploited to refine the localization results. By applying this multi-scale strategy, it is possible to fuse WiFi localization and visual localization and reduce the image matching and error rate to a great extent. Because of exploiting more information, multi-view localization is more robust and accurate than single-view localization. The method is tested in a 2,000 m2 underground parking lot. The result demonstrates that this method can achieve sub-meter localization on average. The proposed localization method can be a supplement to the existing intelligent vehicle localization techniques.
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Yang, Haixu, Jichao Hong, Lingjun Wei, Xun Gong, and Xiaoming Xu. "Collaborative Accurate Vehicle Positioning Based on Global Navigation Satellite System and Vehicle Network Communication." Electronics 11, no. 19 (October 9, 2022): 3247. http://dx.doi.org/10.3390/electronics11193247.
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Intelligence is a direction of development for vehicles and transportation. Accurate vehicle positioning plays a vital role in intelligent driving and transportation. In the case of obstruction or too few satellites, the positioning capability of the Global navigation satellite system (GNSS) will be significantly reduced. To eliminate the effect of unlocalization due to missing GNSS signals, a collaborative multi-vehicle localization scheme based on GNSS and vehicle networks is proposed. The vehicle first estimates the location based on GNSS positioning information and then shares this information with the environmental vehicles through vehicle network communication. The vehicle further integrates the relative position of the ambient vehicle observed by the radar with the ambient vehicle position information obtained by communication. A smaller error estimate of the position of self-vehicle and environmental vehicles is obtained by correcting the positioning of self-vehicle and environmental vehicles. The proposed method is validated by simulating multi-vehicle motion scenarios in both lane change and straight-ahead scenarios. The root-mean-square error of the co-location method is below 0.5 m. The results demonstrate that the combined vehicle network communication approach has higher accuracy than single GNSS positioning in both scenarios.
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Li, Zheng Feng, and Lian Zhou Gao. "Study on WSN Localization Algorithm and Simulation Model for Intelligent Transportation System." Applied Mechanics and Materials 548-549 (April 2014): 1407–14. http://dx.doi.org/10.4028/www.scientific.net/amm.548-549.1407.
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This paper conducts research on the algorithm to improve the location of Wireless Sensor Network (WSN) in Intelligent Transportation System (ITS). The localization algorithm introduced an improved RSSI vehicle localization algorithm based on multi-path effect and Gaussian white noise. The localization results under different values of Gaussian white noise and different density of beacon nodes are analyzes, and Kalman filtering algorithm is introduced to reduce the influence of signal noise. Finally, a simulation model of ITS is developed to test the algorithm based on mixed noise and Kalman filtering algorithm, which is used to simulate the localization of real vehicles. The simulation shows the algorithm has effect to improve location accuracy and to application
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Gao, Lian Zhou. "Study on WSN Localization Algorithm and Simulation Model for Intelligent Transportation System." Applied Mechanics and Materials 539 (July 2014): 867–73. http://dx.doi.org/10.4028/www.scientific.net/amm.539.867.
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This paper conducts research on the algorithm to improve the location of Wireless Sensor Network (WSN) in Intelligent Transportation System (ITS). The localization algorithm introduced an improved RSSI vehicle localization algorithm based on multi-path effect and Gaussian white noise. The localization results under different values of Gaussian white noise and different density of beacon nodes are analyzes, and Kalman filtering algorithm is introduced to reduce the influence of signal noise. Finally, a simulation model of ITS is developed to test the algorithm based on mixed noise and Kalman filtering algorithm, which is used to simulate the localization of real vehicles. The simulation shows the algorithm has effect to improve location accuracy and to application
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Ma, Fangwu, Jinzhu Shi, Liang Wu, Kai Dai, and Shouren Zhong. "Consistent Monocular Ackermann Visual–Inertial Odometry for Intelligent and Connected Vehicle Localization." Sensors 20, no. 20 (October 10, 2020): 5757. http://dx.doi.org/10.3390/s20205757.
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The observability of the scale direction in visual–inertial odometry (VIO) under degenerate motions of intelligent and connected vehicles can be improved by fusing Ackermann error state measurements. However, the relative kinematic error measurement model assumes that the vehicle velocity is constant between two consecutive camera states, which degrades the positioning accuracy. To address this problem, a consistent monocular Ackermann VIO, termed MAVIO, is proposed to combine the vehicle velocity and yaw angular rate error measurements, taking into account the lever arm effect between the vehicle and inertial measurement unit (IMU) coordinates with a tightly coupled filter-based mechanism. The lever arm effect is firstly introduced to improve the reliability for information exchange between the vehicle and IMU coordinates. Then, the process model and monocular visual measurement model are presented. Subsequently, the vehicle velocity and yaw angular rate error measurements are directly used to refine the estimator after visual observation. To obtain a global position for the vehicle, the raw Global Navigation Satellite System (GNSS) error measurement model, termed MAVIO-GNSS, is introduced to further improve the performance of MAVIO. The observability, consistency and positioning accuracy were comprehensively compared using real-world datasets. The experimental results demonstrated that MAVIO not only improved the observability of the VIO scale direction under the degenerate motions of ground vehicles, but also resolved the inconsistency problem of the relative kinematic error measurement model of the vehicle to further improve the positioning accuracy. Moreover, MAVIO-GNSS further improved the vehicle positioning accuracy under a long-distance driving state. The source code is publicly available for the benefit of the robotics community.
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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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Chen, Xiaobo, Jianyu Ji, and Yanjun Wang. "Robust Cooperative Multi-Vehicle Tracking with Inaccurate Self-Localization Based on On-Board Sensors and Inter-Vehicle Communication." Sensors 20, no. 11 (June 5, 2020): 3212. http://dx.doi.org/10.3390/s20113212.
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The fusion of on-board sensors and transmitted information via inter-vehicle communication has been proved to be an effective way to increase the perception accuracy and extend the perception range of connected intelligent vehicles. The current approaches rely heavily on the accurate self-localization of both host and cooperative vehicles. However, such information is not always available or accurate enough for effective cooperative sensing. In this paper, we propose a robust cooperative multi-vehicle tracking framework suitable for the situation where the self-localization information is inaccurate. Our framework consists of three stages. First, each vehicle perceives its surrounding environment based on the on-board sensors and exchanges the local tracks through inter-vehicle communication. Then, an algorithm based on Bayes inference is developed to match the tracks from host and cooperative vehicles and simultaneously optimize the relative pose. Finally, the tracks associated with the same target are fused by fast covariance intersection based on information theory. The simulation results based on both synthesized data and a high-quality physics-based platform show that our approach successfully implements cooperative tracking without the assistance of accurate self-localization.
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Yu, Biao, Hui Zhu, Deyi Xue, Liwei Xu, Shijin Zhang, and Bichun Li. "A Dead Reckoning Calibration Scheme Based on Optimization with an Adaptive Quantum-Inspired Evolutionary Algorithm for Vehicle Self-Localization." Entropy 24, no. 8 (August 15, 2022): 1128. http://dx.doi.org/10.3390/e24081128.
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Parameter calibration is critical for self-localization based on dead reckoning in the control of intelligent vehicles such as autonomous driving. Most traditional calibration methods for robotics control based on dead reckoning rely on data collection with specially designed paths. For the calibration of parameters in the control of intelligent vehicles, the design of such paths is considered impossible due to the complexity of road conditions. To solve this problem, an optimization-based dead reckoning calibration scheme is introduced in this research using the differential global positioning system to obtain the actual positions of the intelligent vehicle. In this scheme, the difference between the positions obtained through dead reckoning and the positions obtained through the differential global positioning system is selected as the optimization objective function to be minimized. An adaptive quantum-inspired evolutionary algorithm is developed to improve the quality and efficiency of optimization. Experiments with an intelligent vehicle were also conducted to demonstrate the effectiveness of the developed calibration scheme. In addition, the newly introduced adaptive quantum-inspired evolutionary algorithm is compared with the classic genetic algorithm and the classic quantum-inspired evolutionary algorithm using eight benchmark test functions considering computation quality and efficiency.
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de Miguel, Miguel Ángel, Fernando García, and José María Armingol. "Improved LiDAR Probabilistic Localization for Autonomous Vehicles Using GNSS." Sensors 20, no. 11 (June 2, 2020): 3145. http://dx.doi.org/10.3390/s20113145.
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This paper proposes a method that improves autonomous vehicles localization using a modification of probabilistic laser localization like Monte Carlo Localization (MCL) algorithm, enhancing the weights of the particles by adding Kalman filtered Global Navigation Satellite System (GNSS) information. GNSS data are used to improve localization accuracy in places with fewer map features and to prevent the kidnapped robot problems. Besides, laser information improves accuracy in places where the map has more features and GNSS higher covariance, allowing the approach to be used in specifically difficult scenarios for GNSS such as urban canyons. The algorithm is tested using KITTI odometry dataset proving that it improves localization compared with classic GNSS + Inertial Navigation System (INS) fusion and Adaptive Monte Carlo Localization (AMCL), it is also tested in the autonomous vehicle platform of the Intelligent Systems Lab (LSI), of the University Carlos III de of Madrid, providing qualitative results.
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Salichs, M. A., and C. Balaguer. "Introduction." Robotica 18, no. 3 (May 2000): 225. http://dx.doi.org/10.1017/s0263574799002209.
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Mobile robotics is a very attractive research field. There are two main reasons for this. The first is the high number of applications and sectors where these machines could be introduced in the future: transport, agriculture, military, aerospace, etc. The second reason is that mobile robots are excellent test-beds to develop and integrate the results of many other research areas: control, computer vision, electronics, mechanics, artificial intelligence, etc. The third IFAC Symposium on Intelligent Autonomous Vehicles was an opportunity to present some of the latest developments in the area. Authors presented not only new methods and technologies to solve the classical problems related to intelligent autonomous vehicles, which include path planning, localization, environment modeling, path following, etc., but also new approaches to their design, such as new architectures, new navigation procedures, self-learning systems, etc. In addition, many different applications and vehicles were considered: from autonomous ocean vehicles to planetary rovers. This special issue of Robotica comprises some revised and updated selected papers of the Symposium. The variety of selected papers includes surveys, applications and design and development aspects of intelligent autonomous vehicles.
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Li, Yicheng, Zhaozheng Hu, Yuezhi Hu, and Duanfeng Chu. "Integration of vision and topological self-localization for intelligent vehicles." Mechatronics 51 (May 2018): 46–58. http://dx.doi.org/10.1016/j.mechatronics.2018.02.012.
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Nguyen, Dinh-Van, Trung-Kien Dao, Eric Castelli, and Fawzi Nashashibi. "A Fusion Method for Localization of Intelligent Vehicles in Carparks." IEEE Access 8 (2020): 99729–39. http://dx.doi.org/10.1109/access.2020.2995865.
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Ghazali, Kamarul Hawari Bin, Rui Xiao, and Jie Ma. "An Intelligent Lane Markers Recognition and Localization System Using Improved Hough Transform." Applied Mechanics and Materials 121-126 (October 2011): 1186–90. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.1186.
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According to the U.S. National Highway Traffic Safety Administration, single vehicle road departures result in many serious accidents each year[1]. An intelligent lane markers recognition and localization system can assist vehicles stay in proper location of a lane that will reduce possibility of car accidents correspondingly. A great deal of lane recognition algorithms have been developed over the past several decades. However, reliable detection is still an issue because of variable road face conditions. An optimum algorithm, Probabilistic Hough Transform (PHT) is presented in this paper for intelligent Lane markers recognition which use a fixed camera installed on the vehicle to transmit video information. The result of experiment proved that under inconsistent illumination and a diversity of road conditions, the accuracy and efficiency of developed system have been improved greatly.
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Huang, Gang, Zhaozheng Hu, Qianwen Tao, Fan Zhang, and Zhe Zhou. "Improved intelligent vehicle self-localization with integration of sparse visual map and high-speed pavement visual odometry." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 235, no. 1 (September 4, 2020): 177–87. http://dx.doi.org/10.1177/0954407020943306.
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Localization is a fundamental requirement for intelligent vehicles. Conventional localization methods usually suffer from various limitations, such as low accuracy and blocked areas for Global Positioning System, high cost for inertial navigation system or light detection and ranging, and low robustness for visual simultaneous localization and mapping or visual odometry. To overcome these problems, we propose a novel localization method integrated with a sparse visual map and a high-speed pavement visual odometry. We use a lateral-view camera to sense the sparse visual map node for accurate map-based localization. We use a down-view high-speed camera for odometry computation between two sparse visual map nodes. With a high-speed camera, it is possible to extract and track pavement features with stable resolution imaging even in high-speed movement. We also develop a data-driven motion model for the Kalman filter to fuse the localization results from the sparse map and the high-speed pavement visual odometry to enhance vehicle localization. The proposed method was tested in two different scenarios in different pavement conditions. The experimental results demonstrate that the proposed method can improve vehicle localization with low cost and high feasibility.
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Ni, Tao, Wenhang Li, Dingxuan Zhao, and Zhifei Kong. "Road Profile Estimation Using a 3D Sensor and Intelligent Vehicle." Sensors 20, no. 13 (June 30, 2020): 3676. http://dx.doi.org/10.3390/s20133676.
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Autonomous vehicles can achieve accurate localization and real-time road information perception using sensors such as global navigation satellite systems (GNSSs), light detection and ranging (LiDAR), and inertial measurement units (IMUs). With road information, vehicles can navigate autonomously to a given position without traffic accidents. However, most of the research on autonomous vehicles has paid little attention to road profile information, which is a significant reference for vehicles driving on uneven terrain. Most vehicles experience violent vibrations when driving on uneven terrain, which reduce the accuracy and stability of data obtained by LiDAR and IMUs. Vehicles with an active suspension system, on the other hand, can maintain stability on uneven roads, which further guarantees sensor accuracy. In this paper, we propose a novel method for road profile estimation using LiDAR and vehicles with an active suspension system. In the former, 3D laser scanners, IMU, and GPS were used to obtain accurate pose information and real-time cloud data points, which were added to an elevation map. In the latter, the elevation map was further processed by a Kalman filter algorithm to fuse multiple cloud data points at the same cell of the map. The model predictive control (MPC) method is proposed to control the active suspension system to maintain vehicle stability, thus further reducing drifts of LiDAR and IMU data. The proposed method was carried out in outdoor environments, and the experiment results demonstrated its accuracy and effectiveness.
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Tian, Daxin, Weiqiang Gong, Wenhao Liu, Xuting Duan, Yukai Zhu, Chao Liu, and Xin Li. "Applications of intelligent computing in vehicular networks." Journal of Intelligent and Connected Vehicles 1, no. 2 (June 11, 2018): 66–76. http://dx.doi.org/10.1108/jicv-01-2018-0001.
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Purpose This paper aims to introduce vehicular network platform, routing and broadcasting methods and vehicular positioning enhancement technology, which are three aspects of the applications of intelligent computing in vehicular networks. From this paper, the role of intelligent algorithm in the field of transportation and the vehicular networks can be understood. Design/methodology/approach In this paper, the authors introduce three different methods in three layers of vehicle networking, which are data cleaning based on machine learning, routing algorithm based on epidemic model and cooperative localization algorithm based on the connect vehicles. Findings In Section 2, a novel classification-based framework is proposed to efficiently assess the data quality and screen out the abnormal vehicles in database. In Section 3, the authors can find when traffic conditions varied from free flow to congestion, the number of message copies increased dramatically and the reachability also improved. The error of vehicle positioning is reduced by 35.39% based on the CV-IMM-EKF in Section 4. Finally, it can be concluded that the intelligent computing in the vehicle network system is effective, and it will improve the development of the car networking system. Originality/value This paper reviews the research of intelligent algorithms in three related areas of vehicle networking. In the field of vehicle networking, these research results are conducive to promoting data processing and algorithm optimization, and it may lay the foundation for the new methods.
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Xu, Jijin, and Zhen Liang. "Multiview Fusion 3D Target Information Perception Model in Nighttime Unmanned Intelligent Vehicles." Journal of Function Spaces 2022 (August 17, 2022): 1–10. http://dx.doi.org/10.1155/2022/9295395.
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Unmanned technology is an important development project of today’s cutting-edge science and technology, which has a significant impact on social and economic development, national defense construction, and scientific and technological development. The rapid development of industrial information technology has driven the unmanned intelligent vehicle system to innovate and gradually enter the public’s view, and at the same time, the driving safety of unmanned intelligent vehicles is also widely concerned. Target information perception system is the foundation of unmanned system and the fundamental guarantee of safety and intelligence of unmanned vehicles. There are three key problems of target recognition in unmanned driving, namely, target classification, localization, and attitude determination. In the implementation of a networked virtual environment system, a flexible and complete perception model is needed as the guiding model of the system. Since 3D point cloud data can provide more spatial information than 2D RGB image data, it is more beneficial to determine the target category, position, and pose in 3D. In this paper, based on the existing research of unmanned intelligent vehicle perception system, we combine the application of fusion of 3D target information perception model and develop a nighttime unmanned system based on multiview fusion of 3D target information perception model. This system can simultaneously perform the detection of multiple categories of objects and predict the center point, length, width, height, and orientation of the objects, so that the unmanned car can sense the location of the surrounding objects when driving in the actual scene.
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Jia, Weiwei, Weizhou Zhang, Fangwu Ma, and Liang Wu. "Attitude Control of Vehicle Based on Series Active Suspensions." Actuators 12, no. 2 (February 5, 2023): 67. http://dx.doi.org/10.3390/act12020067.
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When vehicles with traditional passive suspension systems are driving in complex terrain, large swing and vibrations of the car body make passengers and goods uncomfortable and unstable, even at very low-speed conditions. Considering the actual need for intelligent resource exploration in the sustainable economy, visual-based perception and localization systems of unmanned vehicles still cannot handle the sensor noise coursed by large body motions. In order to improve the stability and safety of vehicles in complex terrain, an attitude control system is proposed for mainly eliminating the external body motions of the vehicle by using series active suspensions. A model predictive control method considered the differences between the simulated and real vehicle, and the performance restrictions of actuators are used to design the attitude controller for reducing the heaving, pitching, and rolling motions of the vehicle. After simulations and real car tests, the results show that the proposed attitude controller can significantly improve the attitude stability of vehicles in harsh terrain.
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Osman, Mostafa, Ahmed Hussein, Abdulla Al-Kaff, Fernando García, and Dongpu Cao. "A Novel Online Approach for Drift Covariance Estimation of Odometries Used in Intelligent Vehicle Localization." Sensors 19, no. 23 (November 26, 2019): 5178. http://dx.doi.org/10.3390/s19235178.
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Localization is the fundamental problem of intelligent vehicles. For a vehicle to autonomously operate, it first needs to locate itself in the environment. A lot of different odometries (visual, inertial, wheel encoders) have been introduced through the past few years for autonomous vehicle localization. However, such odometries suffers from drift due to their reliance on integration of sensor measurements. In this paper, the drift error in an odometry is modeled and a Drift Covariance Estimation (DCE) algorithm is introduced. The DCE algorithm estimates the covariance of an odometry using the readings of another on-board sensor which does not suffer from drift. To validate the proposed algorithm, several real-world experiments in different conditions as well as sequences from Oxford RobotCar Dataset and EU long-term driving dataset are used. The effect of the covariance estimation on three different fusion-based localization algorithms (EKF, UKF and EH-infinity) is studied in comparison with the use of constant covariance, which were calculated based on the true variance of the sensors being used. The obtained results show the efficacy of the estimation algorithm compared to constant covariances in terms of improving the accuracy of localization.
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López-Sastre, Roberto, Carlos Herranz-Perdiguero, Ricardo Guerrero-Gómez-Olmedo, Daniel Oñoro-Rubio, and Saturnino Maldonado-Bascón. "Boosting Multi-Vehicle Tracking with a Joint Object Detection and Viewpoint Estimation Sensor." Sensors 19, no. 19 (September 20, 2019): 4062. http://dx.doi.org/10.3390/s19194062.
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In this work, we address the problem of multi-vehicle detection and tracking for traffic monitoring applications. We preset a novel intelligent visual sensor for tracking-by-detection with simultaneous pose estimation. Essentially, we adapt an Extended Kalman Filter (EKF) to work not only with the detections of the vehicles but also with their estimated coarse viewpoints, directly obtained with the vision sensor. We show that enhancing the tracking with observations of the vehicle pose, results in a better estimation of the vehicles trajectories. For the simultaneous object detection and viewpoint estimation task, we present and evaluate two independent solutions. One is based on a fast GPU implementation of a Histogram of Oriented Gradients (HOG) detector with Support Vector Machines (SVMs). For the second, we adequately modify and train the Faster R-CNN deep learning model, in order to recover from it not only the object localization but also an estimation of its pose. Finally, we publicly release a challenging dataset, the GRAM Road Traffic Monitoring (GRAM-RTM), which has been especially designed for evaluating multi-vehicle tracking approaches within the context of traffic monitoring applications. It comprises more than 700 unique vehicles annotated across more than 40.300 frames of three videos. We expect the GRAM-RTM becomes a benchmark in vehicle detection and tracking, providing the computer vision and intelligent transportation systems communities with a standard set of images, annotations and evaluation procedures for multi-vehicle tracking. We present a thorough experimental evaluation of our approaches with the GRAM-RTM, which will be useful for establishing further comparisons. The results obtained confirm that the simultaneous integration of vehicle localizations and pose estimations as observations in an EKF, improves the tracking results.
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Li, Yicheng, Yingfeng Cai, Zhixiong Li, Shizhe Feng, Hai Wang, and Miguel Angel Sotelo. "Map-based localization for intelligent vehicles from bi-sensor data fusion." Expert Systems with Applications 203 (October 2022): 117586. http://dx.doi.org/10.1016/j.eswa.2022.117586.
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Dong, Longjun, Daoyuan Sun, Guangjie Han, Xibing Li, Qingchun Hu, and Lei Shu. "Velocity-Free Localization of Autonomous Driverless Vehicles in Underground Intelligent Mines." IEEE Transactions on Vehicular Technology 69, no. 9 (September 2020): 9292–303. http://dx.doi.org/10.1109/tvt.2020.2970842.
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Li, Liang, Ming Yang, Lindong Guo, Chunxiang Wang, and Bing Wang. "Hierarchical Neighborhood Based Precise Localization for Intelligent Vehicles in Urban Environments." IEEE Transactions on Intelligent Vehicles 1, no. 3 (September 2016): 220–29. http://dx.doi.org/10.1109/tiv.2017.2654065.
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Wang, Mingyang, Xinbo Chen, Baobao Jin, Pengyuan Lv, Wei Wang, and Yong Shen. "A Novel V2V Cooperative Collision Warning System Using UWB/DR for Intelligent Vehicles." Sensors 21, no. 10 (May 17, 2021): 3485. http://dx.doi.org/10.3390/s21103485.
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The collision warning system (CWS) plays an essential role in vehicle active safety. However, traditional distance-measuring solutions, e.g., millimeter-wave radars, ultrasonic radars, and lidars, fail to reflect vehicles’ relative attitude and motion trends. In this paper, we proposed a vehicle-to-vehicle (V2V) cooperative collision warning system (CCWS) consisting of an ultra-wideband (UWB) relative positioning/directing module and a dead reckoning (DR) module with wheel-speed sensors. Each vehicle has four UWB modules on the body corners and two wheel-speed sensors on the rear wheels in the presented configuration. An over-constrained localization method is proposed to calculate the relative position and orientation with the UWB data more accurately. Vehicle velocities and yaw rates are measured by wheel-speed sensors. An extended Kalman filter (EKF) is applied based on the relative kinematic model to combine the UWB and DR data. Finally, the time to collision (TTC) is estimated based on the predicted vehicle collision position. Furthermore, through UWB signals, vehicles can simultaneously communicate with each other and share information, e.g., velocity, yaw rate, which brings the potential for enhanced real-time performance. Simulation and experimental results show that the proposed method significantly improves the positioning, directing, and velocity estimating accuracy, and the proposed system can efficiently provide collision warning.
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Dai, Kai, Bohua Sun, Guanpu Wu, Shuai Zhao, Fangwu Ma, Yufei Zhang, and Jian Wu. "LiDAR-Based Sensor Fusion SLAM and Localization for Autonomous Driving Vehicles in Complex Scenarios." Journal of Imaging 9, no. 2 (February 20, 2023): 52. http://dx.doi.org/10.3390/jimaging9020052.
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LiDAR-based simultaneous localization and mapping (SLAM) and online localization methods are widely used in autonomous driving, and are key parts of intelligent vehicles. However, current SLAM algorithms have limitations in map drift and localization algorithms based on a single sensor have poor adaptability to complex scenarios. A SLAM and online localization method based on multi-sensor fusion is proposed and integrated into a general framework in this paper. In the mapping process, constraints consisting of normal distributions transform (NDT) registration, loop closure detection and real time kinematic (RTK) global navigation satellite system (GNSS) position for the front-end and the pose graph optimization algorithm for the back-end, which are applied to achieve an optimized map without drift. In the localization process, the error state Kalman filter (ESKF) fuses LiDAR-based localization position and vehicle states to realize more robust and precise localization. The open-source KITTI dataset and field tests are used to test the proposed method. The method effectiveness shown in the test results achieves 5–10 cm mapping accuracy and 20–30 cm localization accuracy, and it realizes online autonomous driving in complex scenarios.
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Bonnifait, Philippe, Maged Jabbour, and Gérald Dherbomez. "Real-Time Implementation of a GIS-Based Localization System for Intelligent Vehicles." EURASIP Journal on Embedded Systems 2007 (2007): 1–12. http://dx.doi.org/10.1155/2007/39350.
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Bonnifait, Philippe, Maged Jabbour, and Gérald Dherbomez. "Real-Time Implementation of a GIS-Based Localization System for Intelligent Vehicles." EURASIP Journal on Embedded Systems 2007, no. 1 (2007): 039350. http://dx.doi.org/10.1186/1687-3963-2007-039350.
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Yu, Junwei, and Zhuoping Yu. "Mono-Vision Based Lateral Localization System of Low-Cost Autonomous Vehicles Using Deep Learning Curb Detection." Actuators 10, no. 3 (March 11, 2021): 57. http://dx.doi.org/10.3390/act10030057.
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The localization system of low-cost autonomous vehicles such as autonomous sweeper requires a highly lateral localization accuracy as the vehicle needs to keep a near lateral-distance between the side brush system and the road curb. Existing methods usually rely on a global navigation satellite system that often loses signal in a cluttered environment such as sweeping streets between high buildings and trees. In a GPS-denied environment, map-based methods are often used such as visual and LiDAR odometry systems. Apart from heavy computation costs from feature extractions, they are too expensive to meet the low-price market of the low-cost autonomous vehicles. To address these issues, we propose a mono-vision based lateral localization system of an autonomous sweeper. Our system relies on a fish-eye camera and precisely detects road curbs with a deep curb detection network. Curbs locations are then referred to as straightforward marks to control the lateral motion of the vehicle. With our self-recorded dataset, our curb detection network achieves 93% pixel-level precision. In addition, experiments are performed with an intelligent sweeper to prove the accuracy and robustness of our proposed approach. Results demonstrate that the average lateral distance error and the maximum invalid rate are within 0.035 m and 9.2%, respectively.
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Li, Hong Sheng, Guang Rong Bian, and Ning Hui He. "Network Design and Implementation of Intelligent Warehouse Based on EPC/RFID and WSN." Applied Mechanics and Materials 236-237 (November 2012): 338–43. http://dx.doi.org/10.4028/www.scientific.net/amm.236-237.338.
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The establishment of intelligent warehouse improves the efficiency of the items storage and realizes the localization and tracking of the various elements of the warehouse. Users can dynamically monitor the location and whereabouts of material, operating machinery and vehicles in warehouse by intelligent terminal. The overall architecture of the intelligent warehouse is built on the Internet and the Internet of Things.With the use of RFID technology, the wireless sensor network technology, the short-range wireless communication technology and automatic identification technology to locate and track the various elements of the warehouse for making warehouse management automatic, intelligent and accurate. The paper first analyzes the basic architecture of the intelligent warehouse system and designs the network architecture of the intelligent warehouse. It also compares the real-time location tracking system based on the RFID with the one based on wireless sensor networks. Therefore it can develop improved method of intelligent location tracking system and apply 8421 coding techniques in the localization and tracking of the intelligent warehouse so as to obtain a satisfactory effect of static and dynamic positioning.
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Gao, Lian Zhou. "Study on WSN Localization Algorithm and Simulation Model Based on Kalman Filtering Algorithm." Advanced Materials Research 945-949 (June 2014): 2380–85. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.2380.
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This paper studies on the algorithm to improve the location of Wireless Sensor Network (WSN) in Intelligent Transportation System (ITS). Considering multi-path effect in the localization, an improved RSSI algorithm is introduced in the localization algorithm. The localization results are analyzed under different density of beacon nodes, and Kalman filtering algorithm is introduced to reduce the influence of signal noise. Finally, to test the algorithm based on Kalman filtering algorithm, a simulation model of ITS is developed, which is used to simulate the localization of real vehicles. The simulation shows the algorithm has effect to improve location accuracy and to application.
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Fontana, Caio Fernando, Cledson Akio Sakurai, and Antonio Gil Da Silva Andrade. "Non-Intrusive Techonology to Its for BRT." International Journal of Computational and Applied Mathematics & Computer Science 2 (June 4, 2022): 11–17. http://dx.doi.org/10.37394/232028.2022.2.3.
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The Intelligent Transport Systems (ITS) is allowing that ubiquitous society use the ITS facilities to conducting their activities, so this research studied the use of LPR (License Plate Recognition) as a tool for ITS BRT operation. The use of Optical Character Recognition (OCR) to identify the vehicles will permit to monitoring several items, like: localization of bus, non-authorized vehicles and so on. This research considers the use of existing ITS infrastructure and technology and the main advantage of LPR is the ability to identify vehicles without disrupting the normal flow or even cause a decrease in speed and to be nonintrusive in perspective of vehicles owners.
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Rahman, Shaikh Mohammad Ehsanur, Farhat Anwar, and Aisha Hassan Abdalla Hashim. "Performance Enhancement of NEMO based VANET using Localization Router (LR) to reduce Handoff delays." Indonesian Journal of Electrical Engineering and Computer Science 8, no. 2 (November 1, 2017): 511. http://dx.doi.org/10.11591/ijeecs.v8.i2.pp511-521.
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<p>Vehicular Ad hoc networks (VANETs) combined with vehicle-to-vehicle and vehicle-to-infrastructure communications can be considered as the most suitable technology to enable ITS (Intelligent Transport System) application bestowed upon travellers with mobility, safety and productivity with human comfort. As a delay sensitive ITS application, handoff delays and packet losses are critical parameters for maintaining seamless connectivity in VANET solution. During handoff, when mobile node (vehicle) is acquiring new CoA (care of address), packets directed towards that node are lost; because it’s old identity is no more valid. So in high speed dynamic vehicular environment the number of frequent handoffs would produce delay beyond the normal limit. Therefore, it is very important to resolve the issues of handoff delay and packet losses in VANET environment. As a solution, a domain based RHD-NV (Reducing Handoff Delay in NEMO based VANET) scheme is proposed in this paper. Number of vehicles moving towards the road constructs a domain where network mobility NEMO-BS protocol is applied. A vehicle is selected as MR (master router) and connected to the RSU (road side unit) to the internet and other vehicles in the domain work as LRs (localization router) and communicate through MR. Simulation tests performed in NS3 (network simulator) and MATLAB SIMULINK demonstrate that using LRs (localization router) in the domain, the number of handoffs and handoff delay are significantly reduced.</p>
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Awang Salleh, Dayang Nur Salmi Dharmiza, and Emmanuel Seignez. "Swift Path Planning: Vehicle Localization by Visual Odometry Trajectory Tracking and Mapping." Unmanned Systems 06, no. 04 (October 2018): 221–30. http://dx.doi.org/10.1142/s2301385018500085.
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Accurate localization is the key component in intelligent vehicles for navigation. With the rapid development especially in urban area, the increasing high-rise buildings results in urban canyon and road network has become more complex. These affect the vehicle navigation performance particularly in the event of poor Global Positioning System (GPS) signal. Therefore, it is essential to develop a perceptive localization system to overcome this problem. This paper proposes a localization approach that exhibits the advantages of Visual Odometry (VO) in low-cost data fusion to reduce vehicle localization error and improve its response rate in path selection. The data used are sourced from camera as visual sensor, low-cost GPS and free digital map from OpenStreetMap. These data are fused by Particle filter (PF) where our method estimates the curvature similarity score of VO trajectory curve with candidate ways extracted from the map. We evaluate the robustness of our proposed approach with three types of GPS errors such as random noise, biased noise and GPS signal loss in an instance of ambiguous road decision. Our results show that this method is able to detect and select the correct path simultaneously which contributes to a swift path planning.
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Tsugawa, Sadayuki. "Special Issue on Fundamental Technologies for ITS." Journal of Robotics and Mechatronics 13, no. 4 (August 20, 2001): 339. http://dx.doi.org/10.20965/jrm.2001.p0339.
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Intelligent transport systems (ITS), a combination of IT(Information Technology) and TS (Transport Systems), solves problems such as accidents and congestion, lessening environmental impact and conserving energy. As conventional solutions to traffic issues became less and less effective, high-tech solutions have been sought. Preceding the term ITS, coined in 1994, were road transport informatics (RTI), advanced transport telematics (AT), and intelligent vehicle-highway systems (IVHS). In the mid-1980s, large ITS projects started in Europe, the US, and Japan, but the use of high-tech solutions emerged in the 1950s. As indicated above, ITS includes systems covering passenger-car safety and freight management, supported by a wide range of technologies including sensing, control, communications, and human factors. This special issue on ITS focuses on ITS technologies that share similarities with robotics and mechatronics. The papers in this issue are classed into sensing, control, simulation, and electric vehicles. Papers in sensing deal with the application of vehicle localization in automated driving, 3-dimensional localization with corner cubes and laser radar, vision-based passage detection, and night-time obstacle detection with machine vision. The technology presented in these papers is expected to play an important role in robotics and mechatronics. The 4 control papers include an overview on control algorithms for automated driving and 3 papers on control algorithms for lateral control, lane changing, and parking assistance. The major difference between mobile robots and automobiles is that, due to speed, the behavior of mobile robots can be described with kinematics, but that of automobiles must be described with dynamics. Nevertheless, control algorithms for automated automobiles are insightful in robotics. Simulation technologies are essential in ITS to present virtually situations difficult or not possible to realize in the real world. One paper deals with a driving simulator and the other with automobile traffic. The last area in this ITS issue is electric vehicles. Their handicaps can be overcome by ITS, leading to new road transport. The paper on electric vehicles introduces an experimental electric vehicle both educational and informative to readers planning electric vehicles to conduct experiments involving ITS. We thank those on the JSME Research Committee 179 for cooperation between human and systems in ITS for reviewing submitted papers.
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Efatmaneshnik, Mahmoud, Allison Kealy, Asghar Tabatabei Balaei, and Andrew G. Dempster. "Information Fusion for Localization Within Vehicular Networks." Journal of Navigation 64, no. 3 (June 7, 2011): 401–16. http://dx.doi.org/10.1017/s0373463311000075.
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Cooperative positioning (CP) is a localization technique originally developed for use across wireless sensor networks. With the emergence of Dedicated Short Range Communications (DSRC) infrastructure for use in Intelligent Transportation Systems (ITS), CP techniques can now be adapted for use in location determination across vehicular networks. In vehicular networks, the technique of CP fuses GPS positions with additional sensed information such as inter-vehicle distances between the moving vehicles to determine their location within a neighbourhood. This paper presents the results obtained from a research study undertaken to demonstrate the capabilities of DSRC for meeting the positioning accuracies of road safety applications. The results show that a CP algorithm that fully integrates both measured/sensed data as well as navigation information such as map data can meet the positioning requirements of safety related applications of DSRC (<0·5 m). This paper presents the results of a Cramer Rao Lower Bound analysis which is used to benchmark the performance of the CP algorithm developed. The Kalman Filter (KF) models used in the CP algorithm are detailed and results obtained from integrating GPS positions, inter-vehicular ranges and information derived from in-vehicle maps are then discussed along with typical results as determined through a variety of network simulation studies.
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Xiao, Zhongyang, Diange Yang, Tuopu Wen, Kun Jiang, and Ruidong Yan. "Monocular Localization with Vector HD Map (MLVHM): A Low-Cost Method for Commercial IVs." Sensors 20, no. 7 (March 27, 2020): 1870. http://dx.doi.org/10.3390/s20071870.
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Real-time vehicle localization (i.e., position and orientation estimation in the world coordinate system) with high accuracy is the fundamental function of an intelligent vehicle (IV) system. In the process of commercialization of IVs, many car manufacturers attempt to avoid high-cost sensor systems (e.g., RTK GNSS and LiDAR) in favor of low-cost optical sensors such as cameras. The same cost-saving strategy also gives rise to an increasing number of vehicles equipped with High Definition (HD) maps. Rooted upon these existing technologies, this article presents the concept of Monocular Localization with Vector HD Map (MLVHM), a novel camera-based map-matching method that efficiently aligns semantic-level geometric features in-camera acquired frames against the vector HD map in order to achieve high-precision vehicle absolute localization with minimal cost. The semantic features are delicately chosen for the ease of map vector alignment as well as for the resiliency against occlusion and fluctuation in illumination. The effective data association method in MLVHM serves as the basis for the camera position estimation by minimizing feature re-projection errors, and the frame-to-frame motion fusion is further introduced for reliable localization results. Experiments have shown that MLVHM can achieve high-precision vehicle localization with an RMSE of 24 cm with no cumulative error. In addition, we use low-cost on-board sensors and light-weight HD maps to achieve or even exceed the accuracy of existing map-matching algorithms.
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Tropea, Mauro, Angelo Arieta, Floriano De Rango, and Francesco Pupo. "A Proposal of a Troposphere Model in a GNSS Simulator for VANET Applications." Sensors 21, no. 7 (April 3, 2021): 2491. http://dx.doi.org/10.3390/s21072491.
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Vehicle positioning is becoming an important issue related to Intelligent Transportation Systems (ITSs). Novel vehicles and autonomous vehicles need to be localized under different weather conditions and it is important to have a reliable positioning system to track vehicles. Satellite navigation systems can be a key technology in providing global coverage and providing localization services through many satellite constellations such as GPS, GLONASS, Galileo and so forth. However, the modeling of positioning and localization systems under different weather conditions is not a trivial objective especially considering different factors such as receiver sensitivity, dynamic weather conditions, propagation delay and so forth. This paper focuses on the use of simulators for performing different kinds of tests on Global Navigation Satellite System (GNSS) systems in order to reduce the cost of the positioning testing under different techniques or models. Simulation driven approach, combined with some specific hardware equipment such as receivers and transmitters can characterize a more realistic scenario and the simulation can consider other aspects that could be complex to really test. In this work, the main contribution is the introduction of the Troposphere Collins model in a GNSS simulator for VANET applications, the GPS-SDR-SIM software. The use of the Collins model in the simulator allows to improve the accuracy of the simulation experiments throughout the reduction of the receiver errors.
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Cai, Hao, Zhaozheng Hu, Gang Huang, Dunyao Zhu, and Xiaocong Su. "Integration of GPS, Monocular Vision, and High Definition (HD) Map for Accurate Vehicle Localization." Sensors 18, no. 10 (September 28, 2018): 3270. http://dx.doi.org/10.3390/s18103270.
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Self-localization is a crucial task for intelligent vehicles. Existing localization methods usually require high-cost IMU (Inertial Measurement Unit) or expensive LiDAR sensors (e.g., Velodyne HDL-64E). In this paper, we propose a low-cost yet accurate localization solution by using a custom-level GPS receiver and a low-cost camera with the support of HD map. Unlike existing HD map-based methods, which usually requires unique landmarks within the sensed range, the proposed method utilizes common lane lines for vehicle localization by using Kalman filter to fuse the GPS, monocular vision, and HD map for more accurate vehicle localization. In the Kalman filter framework, the observations consist of two parts. One is the raw GPS coordinate. The other is the lateral distance between the vehicle and the lane, which is computed from the monocular camera. The HD map plays the role of providing reference position information and correlating the local lateral distance from the vision and the GPS coordinates so as to formulate a linear Kalman filter. In the prediction step, we propose using a data-driven motion model rather than a Kinematic model, which is more adaptive and flexible. The proposed method has been tested with both simulation data and real data collected in the field. The results demonstrate that the localization errors from the proposed method are less than half or even one-third of the original GPS positioning errors by using low cost sensors with HD map support. Experimental results also demonstrate that the integration of the proposed method into existing ones can greatly enhance the localization results.
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Moustafa, Akram A., and Mohammed-Issa Riad Mousa Jaradat. "A New Approach for License Plate Detection and Localization: Between Reality and Applicability." International Business Research 8, no. 11 (October 26, 2015): 13. http://dx.doi.org/10.5539/ibr.v8n11p13.
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<p>License Plate Detection and Localization (LPDL) is known to have become one of the most progressive and growing areas of study in the field of Intelligent Traffic Management System (ITMS). LPDL provides assistance by being able to specifically locate a vehicle’s number plate which is an essential part of ITMS, that is used for automatic road tax collection, traffic signals defilement implementation, borders and payments barriers and to monitor unlike activities. Organizations can deploy the number plate detection and recognition system to track their vehicles and to monitor each of them in their vital business activities like inbound and outbound logistics, find the exact location of their vehicles and organize entrance management. A competent algorithm is proposed in this paper for number plate detection and localization based on segmentation and morphological operators. Thus, the proposed algorithm it works on enhancing the quality of the image by applying morphological operators afterwards to segment out license plate from the captured image. No assumptions about the license plate color, style of font, size of text and type of material the plate is made of. The results reveal that the proposed algorithm works perfectly on all kinds of license plates with 93.43% efficiency rate. </p>
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Miao, Yanan, Xiaoming Tao, Xiaolin Xu, and Jianhua Lu. "Joint 3-D Shape Estimation and Landmark Localization From Monocular Cameras of Intelligent Vehicles." IEEE Internet of Things Journal 6, no. 1 (February 2019): 15–25. http://dx.doi.org/10.1109/jiot.2018.2872435.
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Babu, R. Ganesh, P. Karthika, and G. Manikandan. "Polynomial Equation Based Localization and Recognition Intelligent Vehicles Axis using Wireless Sensor in MANET." Procedia Computer Science 167 (2020): 1281–90. http://dx.doi.org/10.1016/j.procs.2020.03.444.
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Charroud, Anas, Karim El El Moutaouakil, Vasile Palade, and Ali Yahyaouy. "XDLL: Explained Deep Learning LiDAR-Based Localization and Mapping Method for Self-Driving Vehicles." Electronics 12, no. 3 (January 22, 2023): 567. http://dx.doi.org/10.3390/electronics12030567.
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Self-driving vehicles need a robust positioning system to continue the revolution in intelligent transportation. Global navigation satellite systems (GNSS) are most commonly used to accomplish this task because of their ability to accurately locate the vehicle in the environment. However, recent publications have revealed serious cases where GNSS fails miserably to determine the position of the vehicle, for example, under a bridge, in a tunnel, or in dense forests. In this work, we propose a framework architecture of explaining deep learning LiDAR-based (XDLL) models that predicts the position of the vehicles by using only a few LiDAR points in the environment, which ensures the required fastness and accuracy of interactions between vehicle components. The proposed framework extracts non-semantic features from LiDAR scans using a clustering algorithm. The identified clusters serve as input to our deep learning model, which relies on LSTM and GRU layers to store the trajectory points and convolutional layers to smooth the data. The model has been extensively tested with short- and long-term trajectories from two benchmark datasets, Kitti and NCLT, containing different environmental scenarios. Moreover, we investigated the obtained results by explaining the contribution of each cluster feature by using several explainable methods, including Saliency, SmoothGrad, and VarGrad. The analysis showed that taking the mean of all the clusters as an input for the model is enough to obtain better accuracy compared to the first model, and it reduces the time consumption as well. The improved model is able to obtain a mean absolute positioning error of below one meter for all sequences in the short- and long-term trajectories.
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Mahmood, Zahid, Khurram Khan, Uzair Khan, Syed Hasan Adil, Syed Saad Azhar Ali, and Mohsin Shahzad. "Towards Automatic License Plate Detection." Sensors 22, no. 3 (February 7, 2022): 1245. http://dx.doi.org/10.3390/s22031245.
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Automatic License Plate Detection (ALPD) is an integral component of using computer vision approaches in Intelligent Transportation Systems (ITS). An accurate detection of vehicles’ license plates in images is a critical step that has a substantial impact on any ALPD system’s recognition rate. In this paper, we develop an efficient license plate detecting technique through the intelligent combination of Faster R-CNN along with digital image processing techniques. The proposed algorithm initially detects vehicle(s) in the input image through Faster R-CNN. Later, the located vehicle is analyzed by a robust License Plate Localization Module (LPLM). The LPLM module primarily uses color segmentation and processes the HSV image to detect the license plate in the input image. Moreover, the LPLM module employs morphological filtering and dimension analysis to find the license plate. Detailed trials on challenging PKU datasets demonstrate that the proposed method outperforms few recently developed methods by producing high license plates detection accuracy in much less execution time. The proposed work demonstrates a great feasibility for security and target detection applications.
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Noonan, John, Ehud Rivlin, and Hector Rotstein. "FloorVLoc: A Modular Approach to Floorplan Monocular Localization." Robotics 9, no. 3 (September 10, 2020): 69. http://dx.doi.org/10.3390/robotics9030069.
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Intelligent vehicles for search and rescue, whose mission is assisting emergency personnel by visually exploring an unfamiliar building, require accurate localization. With GPS not available, and approaches relying on new infrastructure installation, artificial landmarks, or pre-constructed dense 3D maps not feasible, the question is whether there is an approach which can combine ubiquitous prior map information with a monocular camera for accurate positioning. Enter FloorVLoc—Floorplan Vision Vehicle Localization. We provide a means to integrate a monocular camera with a floorplan in a unified and modular fashion so that any monocular visual Simultaneous Localization and Mapping (SLAM) system can be seamlessly incorporated for global positioning. Using a floorplan is especially beneficial since walls are geometrically stable, the memory footprint is low, and prior map information is kept at a minimum. Furthermore, our theoretical analysis of the visual features associated with the walls shows how drift is corrected. To see this approach in action, we developed two full global positioning systems based on the core methodology introduced, operating in both Monte Carlo Localization and linear optimization frameworks. Experimental evaluation of the systems in simulation and a challenging real-world environment demonstrates that FloorVLoc performs with an average error of 0.06 m across 80 m in real-time.
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Halili, Rreze, Noori BniLam, Marwan Yusuf, Emmeric Tanghe, Wout Joseph, Maarten Weyn, and Rafael Berkvens. "Vehicle Localization Using Doppler Shift and Time of Arrival Measurements in a Tunnel Environment." Sensors 22, no. 3 (January 22, 2022): 847. http://dx.doi.org/10.3390/s22030847.
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Most applications and services of Cooperative Intelligent Transport Systems (C-ITS) rely on accurate and continuous vehicle location information. The traditional localization method based on the Global Navigation Satellite System (GNSS) is the most commonly used. However, it does not provide reliable, continuous, and accurate positioning in all scenarios, such as tunnels. Therefore, in this work, we present an algorithm that exploits the existing Vehicle-to-Infrastructure (V2I) communication channel that operates within the LTE-V frequency band to acquire in-tunnel vehicle location information. We propose a novel solution for vehicle localization based on Doppler shift and Time of Arrival measurements. Measurements performed in the Beveren tunnel in Antwerp, Belgium, are used to obtain results. A comparison between estimated positions using Extended Kalman Filter (EKF) on Doppler shift measurements and individual Kalman Filter (KF) on Doppler shift and Time of Arrival measurements is carried out to analyze the filtering methods performance. Findings show that the EKF performs better than KF, reducing the average estimation error by 10 m, while the algorithm accuracy depends on the relevant RF channel propagation conditions and other in-tunnel-related environment knowledge included in the estimation. The proposed solution can be used for monitoring the position and speed of vehicles driving in tunnel environments.
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Li, Hui, Yue Quan Bao, Shun Long Li, Wen Li Chen, Shu Jin Laima, and Jin Ping Ou. "Monitoring, Evaluation and Control for Life-Cycle Performance of Intelligent Civil Structures." Advances in Science and Technology 83 (September 2012): 105–14. http://dx.doi.org/10.4028/www.scientific.net/ast.83.105.
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This paper includes five parts. The first is the sensing technology, in which ultrasonic-based sensing technology for scour monitoring of bridge piers, electro-chemistry-based distributed concrete cracks and automobile wireless sensors are introduced. The second is the application of compressive sensing technology in structural health monitoring, in which the recovery of lose data for wireless senor networks, spatial distribution of vehicles on the bridge and localization of acoustic emission source by using compressive technique are included. The third is damage monitoring and identification of seismically excited structures, in which data-driven seismic localization approach and nonlinear hysteretic model identification approach are proposed. The fourth is the monitoring for wind and wind effects of long-span bridges, the vortex-induced vibration of deck, suspended cables and stay cables is observed and the buffeting of bridge under Typhoon is also measured. The last one is the data analysis, modeling and safety evaluation of bridges based on structural health monitoring techniques.
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Nascimento, Pedro, Bruno Kimura, Daniel Guidoni, and Leandro Villas. "An Integrated Dead Reckoning with Cooperative Positioning Solution to Assist GPS NLOS Using Vehicular Communications." Sensors 18, no. 9 (August 31, 2018): 2895. http://dx.doi.org/10.3390/s18092895.
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In Intelligent Transportation Systems (ITS), the Vehicular Ad Hoc Networks (VANETs) paradigm based on the WAVE IEEE 802.11p standard is the main alternative for inter-vehicle communications. Recently, many protocols, applications, and services have been developed with a wide range of objectives, ranging from comfort to security. Most of these services rely on location systems and require different levels of accuracy for their full operation. The Global Positioning System (GPS) is an off-the-shelf solution for localization in VANETs and ITS. However, GPS systems present problems regarding inaccuracy and unavailability in dense urban areas, multilevel roads, and tunnels, posing a challenge for protocols, applications, and services that rely on localization. With this motivation, we carried out a characterization of the problems of inaccuracy and unavailability of GPS systems from real datasets, and regions around tunnels were selected. Since the nodes of the vehicular network are endowed with wireless communication, processing and storage capabilities, an integrated Dead Reckoning aided Geometric Dilution of Precision (GDOP)-based Cooperative Positioning solution was developed and evaluated. Leveraging the potential of vehicular sensors, such as odometers, gyroscopes, and digital compasses, vehicles share their positions and kinematics information using vehicular communication to improve their location estimations. With the assistance of a digital map, vehicles adjust the final estimated position using the road geometry. The situations of GPS unavailability characterized in the datasets were reproduced in a simulation environment to validate the proposed localization solution. The simulation results show average gains in Root Mean Square Error (RMSE) between 97% to 98% in comparison with the stand-alone GPS solution, and 83.00% to 88.00% against the GPS and Dead Reckoning (DR) only solution. The average absolute RMSE was reduced to the range of 3 to 5 m by vehicle. In addition, the proposed solution was shown to support 100% of the GPS unavailability zones on the evaluated scenarios.