Multi-Sensor Fusion for Lateral Vehicle Localization in Tunnels

Jiang, Xinyu; Liu, Zunmin; Liu, Bilong; Liu, Jiang

doi:10.3390/app12136634

Cited by 6 publications

(4 citation statements)

References 25 publications

(30 reference statements)

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“…Unlike static ranging, the pixel points of lane lines are extracted through algorithms during dynamic ranging. We determine the pixel information of points on the lane line by extracting the contour of the lane line [ 23 ]. The results of dynamic ranging are shown in Table 9 .…”

Section: Using Binocular Cameras To Assist In Locating the First Slid...mentioning

confidence: 99%

Lane Detection Algorithm in Curves Based on Multi-Sensor Fusion

Zhang

Liu

Jiang

2023

Sensors

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Identifying lane markings is a key technology in assisted driving and autonomous driving. The traditional sliding window lane detection algorithm has good detection performance in straight lanes and curves with small curvature, but its detection and tracking performance is poor in curves with larger curvature. Large curvature curves are common scenes in traffic roads. Therefore, in response to the problem of poor lane detection performance of traditional sliding window lane detection algorithms in large curvature curves, this article improves the traditional sliding window algorithm and proposes a sliding window lane detection calculation method, which integrates steering wheel angle sensors and binocular cameras. When a vehicle first enters a bend, the curvature of the bend is not significant. Traditional sliding window algorithms can effectively detect the lane line of the bend and provide angle input to the steering wheel, enabling the vehicle to travel along the lane line. However, as the curvature of the curve increases, traditional sliding window lane detection algorithms cannot track lane lines well. Considering that the steering wheel angle of the car does not change much during the adjacent sampling time of the video, the steering wheel angle of the previous frame can be used as input for the lane detection algorithm of the next frame. By using the steering wheel angle information, the search center of each sliding window can be predicted. If the number of white pixels within the rectangular range centered around the search center is greater than the threshold, the average of the horizontal coordinate values of these white pixels will be used as the horizontal coordinate value of the sliding window center. Otherwise, the search center will be used as the center of the sliding window. A binocular camera is used to assist in locating the position of the first sliding window. The simulation and experimental results show that compared with traditional sliding window lane detection algorithms, the improved algorithm can better recognize and track lane lines with large curvature in bends.

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Section: Using Binocular Cameras To Assist In Locating the First Slid...mentioning

confidence: 99%

Lane Detection Algorithm in Curves Based on Multi-Sensor Fusion

Zhang

Liu

Jiang

2023

Sensors

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“…Since many modern vehicles have integrated camera systems, some works have investigated the integration of INS with computer vision techniques to further improve the attitude estimation in GNSS-denied environments [30], [31]. Authors in [30] evaluate the use of a binocular camera to measure lane width together with inertial sensors and odometer data in order to estimate the lateral position of a vehicle on the road. The work in [31] proposes the use of vehicle cameras to detect kilometer signs on the road to correct the accumulated positioning error of an INS/odometer system.…”

Section: Related Workmentioning

confidence: 99%

Evaluation Study of Inertial Positioning in Road Tunnels for Cooperative ITS Applications

Rodriguez,

Khademi

2023

2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

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Global Navigation Satellite Systems (GNSS) are unreliable positioning sources in road tunnels, as the satellite signals are unable to reach deep inside the tunnels. As innovative technologies emerge within the transportation sector -e.g., with Autonomous Vehicles (AVs) and Cooperative Intelligent Transport Systems (C-ITS), higher availability, timeliness and accuracy of positioning services is demanded. In GNSS-denied environments such as road tunnels, Inertial Navigation Systems (INS) can leverage the use of accelerometers and gyroscopes to estimate the vehicle's position while the GNSS positioning signal is lost. However, these systems have proven to be unreliable in long tunnels, as noise artifacts in inertial sensors introduce errors which accumulate over time. This paper aims to investigate the impact of these on the positioning accuracy in road tunnels, through modeling a commercially available Inertial Measurement Unit (IMU) and simulating driving routes through various road tunnels within Norway, a country with a complex terrain and many road tunnels. Through these measurements, we also investigate if the positioning requirements of modern C-ITS applications are met in various tunnels, depending on the tunnel length and curvature. Finally, this work lays the foundation for developing a framework for designing costeffective and reliable indoor positioning solutions for road tunnel environments.

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“…When using single sensor for positioning of permanent magnet maglev trains, the positioning accuracy is degraded due to the presence of noise and occlusion in the environment, therefore a multi-sensor information fusion method is employed to solve this problem. Multi-sensor fusion technology applies data fusion [ 8 , 9 ] to target tracking [ 10 ], vehicle localization [ 11 ] and other fields [ 12 – 14 ], which solves some problems of low accuracy in many cases and has broad application prospects and great scientific value [ 15 ]. In order to meet the requirements for positioning of permanent magnet magnetic levitation trains, multiple sensors are generally installed on the maglev trains for data acquisition.…”

Section: Introductionmentioning

confidence: 99%

Multi-sensor information fusion localization of rare-earth suspended permanent magnet maglev trains based on adaptive Kalman algorithm

Xu,

Fan,

et al. 2023

PLoS ONE

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Since the positioning accuracy of sensors degrades due to noise and environmental interference when a single sensor is used to localize a suspended rare-earth permanent magnetically levitated train, a multi-sensor information fusion method using multiple sensors and self-correcting weighting is proposed for permanent magnetic levitated train localization. A decay memory factor is introduced to reduce the weight of the influence of historical measurement data on the fusion estimation, thus enhancing the robustness of the fusion algorithm. The Kalman filtering results suffer from inaccuracy when process noise is present in the system. In this paper, we use a covariance adaptive scheme that replaces the prediction step of the Kalman filter with covariance. It uses the covariance adaptive scheme to search the posterior sequence online and reconstruct the prior error covariance. Since the process noise covariance is not used in the new adaptive scheme, the negative impact of the mismatch noise statistics is greatly reduced. Simulation and experimental results show that the use of multi-sensor information fusion and covariance adaptive Kalman algorithm has significant advantages in terms of adaptability, accuracy and simplicity.

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Multi-Sensor Fusion for Lateral Vehicle Localization in Tunnels

Cited by 6 publications

References 25 publications

Lane Detection Algorithm in Curves Based on Multi-Sensor Fusion

Lane Detection Algorithm in Curves Based on Multi-Sensor Fusion

Evaluation Study of Inertial Positioning in Road Tunnels for Cooperative ITS Applications

Multi-sensor information fusion localization of rare-earth suspended permanent magnet maglev trains based on adaptive Kalman algorithm

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