Vehicle Precise Positioning Based on Integrated Navigation System in Vehicle Networking

Bai, Xiangzhi; Chu, Wei; Hou, Qianhe; Feng, DeFu

doi:10.1109/raai52226.2021.9507900

Cited by 4 publications

(1 citation statement)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Its positioning has high accuracy but only takes into account the distance of the vehicle from the left and right lane lines when the vehicle is traveling in a certain lane and can be considered a static positioning, which will not be applicable to the dynamic positioning of the vehicle when a lane change occurs. Xin et al, proposed the method of integrating dead reckoning with inertial navigation to solve the problem of vehicle positioning when a GPS signal is lost [15]. Although the positioning accuracy is improved compared with the use of a single inertial navigation system, there is not much research on the lateral positioning of vehicles.…”

Section: Introductionmentioning

confidence: 99%

Multi-Sensor Fusion for Lateral Vehicle Localization in Tunnels

Jiang

Liu

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

The satellite navigation signal in the tunnel is weak, and it is difficult to achieve accurate lateral positioning in complex conditions such as low-speed congestion by relying solely on inertial navigation or line image recognition, which is one of the problems of automatic driving at present. In this paper, a lane-level location method based on multi-sensor fusion is proposed. Using the machine vision method, detecting lane lines with the monocular camera, and fitting the lane lines to determine the driving status of the vehicle based on the lane line information. The top view of the lane line is taken by the binocular camera, and the distance of the vehicle from the lane line and the width of the lane are calculated from the pictures taken by the binocular camera. Obtaining the heading angle information of the vehicle using the gyroscope in inertial navigation and the distance information of the vehicle using the odometer. When a car changes lanes or overtakes, the new lane the vehicle is in is calculated by calculating the difference in heading angle and combining it with the lane width and odometer information so as to complete the lateral positioning of the vehicle. The simulation results show that the algorithm has high lateral positioning accuracy. The positioning accuracy is less affected by the drift of inertial elements, and the error will not accumulate.

show abstract

Section: Introductionmentioning

confidence: 99%

Multi-Sensor Fusion for Lateral Vehicle Localization in Tunnels

Jiang

Liu

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

show abstract

Synchronous Acquisition Control System and Coordinate Correction Algorithm for Subway Tunnel Comprehensive Detection Equipment

Sun,

Xu,

Zhou

et al. 2025

IEEE Sensors J.

View full text Add to dashboard Cite

How to Promote Urban Intelligent Transportation: A Fuzzy Cognitive Map Study

2022

View full text Add to dashboard Cite

As an important part of smart city, intelligent transportation is an critical breakthrough to solve urban traffic congestion, build an integrated transportation system, realize the intelligence of traffic infrastructure and promote sustainable development of traffic. In order to investigate the construction of intelligent transportation in cities, 20 initial affecting variables were determined in this study based on literature analysis. A questionnaire collected from professionals in intelligent transportation was conducted, and a total of 188 valid responses were received. Then the potential grouping was revealed through exploratory factor analysis. Finally, a causal model containing seven concepts was established using the practical experience and knowledge of the experts. A root cause analysis method based on fuzzy cognitive map (FCM) was also proposed to simulate intelligent transportation construction (ITC). The results indicate:(1) The 20 variables can be divided into six dimensions: policy support (PS), traffic sector control (TSC), technical support (TS), communication foundation (CF), residents’ recognition (RR), and talent quality (TQ); and (2) In the FCM model, all six concept nodes (PS, TSC, TS, CF, RR, and TQ) have a significant positive correlation with the target concept node ITC. The rank of the six dimensions according to correlation strength is TS, CF, PS, TSC, RR, and TQ. The findings of this paper can help academics and practitioners understand the deep-seated determinants of urban intelligent transportation construction more comprehensively, and provide valuable suggestions for policy makers. And thus, the efficiency of intelligent transportation construction can be improved.

show abstract

Vehicle Precise Positioning Based on Integrated Navigation System in Vehicle Networking

Cited by 4 publications

References 3 publications

Multi-Sensor Fusion for Lateral Vehicle Localization in Tunnels

Multi-Sensor Fusion for Lateral Vehicle Localization in Tunnels

Synchronous Acquisition Control System and Coordinate Correction Algorithm for Subway Tunnel Comprehensive Detection Equipment

How to Promote Urban Intelligent Transportation: A Fuzzy Cognitive Map Study

Contact Info

Product

Resources

About