Vehicle Position Estimation using Tire Model

Yoon, Jaewoo; Kim, Byeong-Woo

doi:10.1007/978-3-662-46578-3_90

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…The vehicle must be localized in the longitudinal direction with respect to the road context, in the lateral direction with respect to the lane lines and in the altitudinal direction with respect to the road structure [19][20][21]. As safety is a prime priority, dead reckoning (DR) is employed to estimate the vehicle position X t,DR using on-board sensors [22,23]. This is achieved by integrating the time interval ∆t and the vehicle velocity v to the previous position X t−1,DR as clarified in Equation ( 9).…”

Section: Lsm Based Localization In Multilevel Environmentsmentioning

confidence: 99%

2.5D Layered Sub-Image LIDAR Maps for Autonomous Driving in Multilevel Environments

Aldibaja

Suganuma²,

Yanase³

2022

Remote Sensing

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This paper proposes a reliable framework to map multilevel road structures in the 2D image domain called layered sub-image maps (LSM). The road is divided into a set of sub-areas providing IDs in the XY plane. Each sub-area is decomposed into several layered images using LIDAR intensity and elevation data to form a 2.5D map image. The layered elevation images are given IDs in the Z plane to represent the height of the contained road features in meter-order whereas the elevation pixels indicate the cm-order of the road slope in the range of 200 cm. The layered intensity images are then created to describe the road surface in conjunction with the number of the layered elevation images and the corresponding pixel distributions. A significant map retrieval strategy during autonomous driving has been designed based on the LSM implementation tactic and the IDs in the XYZ plane. The system’s reliability has been proved by a unique localization module to localize an autonomous vehicle in a challenging multilevel environment consisting of four stacked loops with an average accuracy of 5 cm in lateral, longitudinal and altitudinal directions.

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Section: Lsm Based Localization In Multilevel Environmentsmentioning

confidence: 99%

2.5D Layered Sub-Image LIDAR Maps for Autonomous Driving in Multilevel Environments

Aldibaja

Suganuma²,

Yanase³

2022

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The general approach to building topological maps is to precisely estimate the vehicle trajectories and then accumulate the sensory data accordingly [3]. The trajectory estimation can be achieved using GNSS/INS-RTK (GIR) systems, SLAM technologies, or Dead Reckoning (DR) based on the vehicle velocity and time interval between positions [4][5][6][7][8]. The topological illustration can be achieved by encoding dense sensory representations, such as 3D point clouds and 2D grid maps, or sparse descriptions, such as feature-based maps [9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Waypoint Transfer Module between Autonomous Driving Maps Based on LiDAR Directional Sub-Images

Aldibaja,

Yanase,

Suganuma

2024

Sensors

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Lane graphs are very important for describing road semantics and enabling safe autonomous maneuvers using the localization and path-planning modules. These graphs are considered long-life details because of the rare changes occurring in road structures. On the other hand, the global position of the corresponding topological maps might be changed due to the necessity of updating or extending the maps using different positioning systems such as GNSS/INS-RTK (GIR), Dead-Reckoning (DR), or SLAM technologies. Therefore, the lane graphs should be transferred between maps accurately to describe the same semantics of lanes and landmarks. This paper proposes a unique transfer framework in the image domain based on the LiDAR intensity road surfaces, considering the challenging requirements of its implementation in critical road structures. The road surfaces in a target map are decomposed into directional sub-images with X, Y, and Yaw IDs in the global coordinate system. The XY IDs are used to detect the common areas with a reference map, whereas the Yaw IDs are utilized to reconstruct the vehicle trajectory in the reference map and determine the associated lane graphs. The directional sub-images are then matched to the reference sub-images, and the graphs are safely transferred accordingly. The experimental results have verified the robustness and reliability of the proposed framework to transfer lane graphs safely and accurately between maps, regardless of the complexity of road structures, driving scenarios, map generation methods, and map global accuracies.

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“…In addition, the jump of the hardened and softened primary isolators can be eliminated. Articles [17], [41]- [45], [52] proposed a three-degreeof-freedom vehicle dynamics model in which the Dugoff tire model was applied. As a result, the position estimation of the new model was more accurate than those using GPS.…”

Section: Introductionmentioning

confidence: 99%

An Accurate Vehicle and Road Condition Estimation Algorithm for Vehicle Networking Applications

et al. 2019

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The Internet of Vehicles is essential for building smart cities. By analyzing the big data collected by vehicle sensors on the road, we can estimate vehicle information and real-time road conditions. To improve the prediction accuracy, this paper proposes a new adaptive filtering algorithm for variable measurement noise problems that occur during the driving state estimations of two-axle electric vehicles. Based on the nonlinear three-degree-of-freedom vehicle model, the dual-motor torque output model, and the Dugoff tire model, fuzzy logic is used to correct the measurement noise in the cubature Kalman filter algorithm. Moreover, the ant colony algorithm is used to optimize the input and output membership functions. Based on the big sensor data, we can accurately predict road conditions, such as vehicle speed and road adhesion coefficients. The simulation results based on CarSim/Simulink show that the new algorithm improves the estimation accuracy of the whole system, regardless of whether the measurement noise is fixed or variable. The research in this paper provides a reference for multi-data comprehensive analyses under different vehicle states. INDEX TERMS Big sensor data, ant colony algorithm, fuzzy control, cubature Kalman filter, state estimation.

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Vehicle Position Estimation using Tire Model

Cited by 5 publications

References 8 publications

2.5D Layered Sub-Image LIDAR Maps for Autonomous Driving in Multilevel Environments

2.5D Layered Sub-Image LIDAR Maps for Autonomous Driving in Multilevel Environments

Waypoint Transfer Module between Autonomous Driving Maps Based on LiDAR Directional Sub-Images

An Accurate Vehicle and Road Condition Estimation Algorithm for Vehicle Networking Applications

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