scholarly journals Map Matching-Based Driving Lane Recognition for Low-Cost Precise Vehicle Positioning on Highways

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 42192-42205
Author(s):  
Youn Joo Lee ◽  
Jae Kyu Suhr ◽  
Ho Gi Jung
Keyword(s):  
Low Cost ◽  
Map Matching ◽  
Lane Recognition ◽  
Vehicle Positioning

Vehicle positioning system with multi-hypothesis map matching and robust feedback

2017 ◽  
Vol 11 (10) ◽  
pp. 649-658 ◽  
Author(s):  
Kai Zhang ◽  
Shaojun Liu ◽  
Yuhan Dong ◽  
Daoshun Wang ◽  
Yi Zhang ◽  
...  
Keyword(s):  
Positioning System ◽  
Map Matching ◽  
Vehicle Positioning

Enhancing Vehicle Positioning Data Through Map-Matching

2012 ◽  
pp. 343-364 ◽  
Author(s):  
Mohammed A. Quddus ◽  
Nagendra R. Velaga
Keyword(s):  
Map Matching ◽  
Vehicle Positioning

scholarly journals Lane-level localization system using surround-view cameras adaptive to different driving conditions

2020 ◽  
Vol 17 (2) ◽  
pp. 172988142092163
Author(s):  
Tianyi Li ◽  
Yuhan Qian ◽  
Arnaud de La Fortelle ◽  
Ching-Yao Chan ◽  
Chunxiang Wang
Keyword(s):  
Deep Learning ◽  
Low Cost ◽  
Lateral Position ◽  
Measurement Unit ◽  
Map Matching ◽  
Matching Model ◽  
Localization System ◽  
Perceptual Model ◽  
Road Map ◽  
Driving Conditions

This article presents a lane-level localization system adaptive to different driving conditions, such as occlusions, complicated road structures, and lane-changing maneuvers. The system uses surround-view cameras, other low-cost sensors, and a lane-level road map which suits for mass deployment. A map-matching localizer is proposed to estimate the probabilistic lateral position. It consists of a sub-map extraction module, a perceptual model, and a matching model. A probabilistic lateral road feature is devised as a sub-map without limitations of road structures. The perceptual model is a deep learning network that processes raw images from surround-view cameras to extract a local probabilistic lateral road feature. Unlike conventional deep-learning-based methods, the perceptual model is trained by auto-generated labels from the lane-level map to reduce manual effort. The matching model computes the correlation between the sub-map and the local probabilistic lateral road feature to output the probabilistic lateral estimation. A particle-filter-based framework is developed to fuse the output of map-matching localizer with the measurements from wheel speed sensors and an inertial measurement unit. Experimental results demonstrate that the proposed system provides the localization results with submeter accuracy in different driving conditions.

scholarly journals A Novel Vehicle Stationary Detection Utilizing Map Matching and IMU Sensors

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Md. Syedul Amin ◽  
Mamun Bin Ibne Reaz ◽  
Salwa Sheikh Nasir ◽  
Mohammad Arif Sobhan Bhuiyan ◽  
Mohd. Alauddin Mohd. Ali
Keyword(s):  
Stationary State ◽  
Navigation System ◽  
Urban Areas ◽  
Microelectromechanical Systems ◽  
Low Cost ◽  
Measurement Unit ◽  
Map Matching ◽  
Mm Algorithm ◽  
Bias Drift ◽  
Navigation Information

Precise navigation is a vital need for many modern vehicular applications. The global positioning system (GPS) cannot provide continuous navigation information in urban areas. The widely used inertial navigation system (INS) can provide full vehicle state at high rates. However, the accuracy diverges quickly in low cost microelectromechanical systems (MEMS) based INS due to bias, drift, noise, and other errors. These errors can be corrected in a stationary state. But detecting stationary state is a challenging task. A novel stationary state detection technique from the variation of acceleration, heading, and pitch and roll of an attitude heading reference system (AHRS) built from the inertial measurement unit (IMU) sensors is proposed. Besides, the map matching (MM) algorithm detects the intersections where the vehicle is likely to stop. Combining these two results, the stationary state is detected with a smaller timing window of 3 s. A longer timing window of 5 s is used when the stationary state is detected only from the AHRS. The experimental results show that the stationary state is correctly identified and the position error is reduced to 90% and outperforms previously reported work. The proposed algorithm would help to reduce INS errors and enhance the performance of the navigation system.

scholarly journals High Definition Map-Based Localization Using ADAS Environment Sensors for Application to Automated Driving Vehicles

2020 ◽  
Vol 10 (14) ◽  
pp. 4924
Author(s):  
Donghoon Shin ◽  
Kang-moon Park ◽  
Manbok Park
Keyword(s):  
High Performance ◽  
Low Cost ◽  
Autonomous Driving ◽  
Feature Representation ◽  
Map Matching ◽  
Localization Algorithm ◽  
Automated Driving ◽  
High Definition ◽  
Vehicle Position ◽  
Near Future

This paper presents high definition (HD) map-based localization using advanced driver assistance system (ADAS) environment sensors for application to automated driving vehicles. A variety of autonomous driving technologies are being developed using expensive and high-performance sensors, but limitations exist due to several practical issues. In respect of the application of autonomous driving cars in the near future, it is necessary to ensure autonomous driving performance by effectively utilizing sensors that are already installed for ADAS purposes. Additionally, the most common localization algorithm, which is usually used lane information only, has a highly unstable disadvantage in the absence of that information. Therefore, it is essential to ensure localization performance with other road features such as guardrails when there are no lane markings. In this study, we would like to propose a localization algorithm that could be implemented in the near future by using low-cost sensors and HD maps. The proposed localization algorithm consists of several sections: environment feature representation with low-cost sensors, digital map analysis and application, position correction based on map-matching, designated validation gates, and extended Kalman filter (EKF)-based localization filtering and fusion. Lane information is detected by monocular vision in front of the vehicle. A guardrail is perceived by radar by distinguishing low-speed object measurements and by accumulating several steps to extract wall features. These lane and guardrail information are able to correct the host vehicle position by using the iterative closest point (ICP) algorithm. The rigid transformation between the digital high definition map (HD map) and environment features is calculated through ICP matching. Each corrected vehicle position by map-matching is selected and merged based on EKF with double updating. The proposed algorithm was verified through simulation based on actual driving log data.

Vehicle Positioning Based on Velocity and Heading Angle Observer Using Low-Cost Sensor Fusion

2017 ◽  
Vol 139 (12) ◽  
Author(s):  
Giseo Park ◽  
Yoonjin Hwang ◽  
Seibum B. Choi
Keyword(s):  
Sensor Fusion ◽  
Low Cost ◽  
Estimation Error ◽  
Nonlinear Observer ◽  
Positioning System ◽  
Positioning Systems ◽  
Noise Covariance ◽  
Heading Angle ◽  
Vehicle Positioning ◽  
Vehicle Sensors

The vehicle positioning system can be utilized for various automotive applications. Primarily focusing on practicality, this paper presents a new method for vehicle positioning systems using low-cost sensor fusion, which combines global positioning system (GPS) data and data from easily available in-vehicle sensors. As part of the vehicle positioning, a novel nonlinear observer for vehicle velocity and heading angle estimation is designed, and the convergence of estimation error is also investigated using Lyapunov stability analysis. Based on this estimation information, a new adaptive Kalman filter with rule-based logic provides robust and highly accurate estimations of the vehicle position. It adjusts the noise covariance matrices Q and R in order to adapt to various environments, such as different driving maneuvers and ever-changing GPS conditions. The performance of the entire system is verified through experimental results using a commercial vehicle. Finally, through a comparative study, the effectiveness of the proposed algorithm is confirmed.

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