Optimal layout scheme of roadside intelligent sensing equipment
Pages 520 - 524
Abstract
With the increasing number of vehicles and the continuous increase of highway mileage. Traffic safety issues are gradually coming into view. Placing lidar on the side of the road can increase the safety of driving at high highway speeds. In order to achieve uninterrupted detection of vehicles on the highway. It is necessary to rationally deploy lidar. In this paper, the installation inclination, installation height, installation distance from the highway and the layout distance of adjacent lidar are studied. And the optimal layout scheme is given according to the highway in different scenarios.
References
[1]
Evaluation of intelligent transport systems used in urban agglomerations and intercity roads by professional truck drivers. 2021. Sustainability, 13(5), 2935. //doi.org/10.3390/su13052935
[2]
Ghaemi, Y., El-Ocla, H., Nitin, R. Y., Manisha, R. M., Raju, D. K., Dhanabal, V., & Sheshadri, V. 2021. Intelligent transport system using time delay-based multipath routing protocol for vehicular ad hoc networks. Sensors, 21(22), 7706.
[3]
Wei, Z., Zhang, F., Chang, S., Liu, Y., Wu, H., & Feng, Z. 2022. MmWave radar and vision fusion for object detection in autonomous driving: A review. Sensors, 22(7), 2542.
[4]
Wang, Y., Wang, J., Bao, X., Yu, B., & Ge, Y. 2022. An advanced system-level testing for roadside multimodal sensing and processing in IoV. Wireless Communications & Mobile Computing (Online), 2022.
[5]
Yuepeng Cui, Jianqing Wu, Hao Xu, Aobo Wang, Lane change identification and prediction with roadside lidar data, O ptics & Laser Technology, Volume 123, 2020, 105934, ISSN 0030-3992, https://doi.org/10.1016/j.optlastec.2019.105934.
[6]
Junxuan Zhao, Hao Xu, Yuan Tian, Hongchao Liu, Towards application of light detection and ranging sensor to traffic detection: an investigation of its built-in features and installation techniques, Journal of Intelligent Transportation Systems, Volume 26, Issue 2, 2022, Pages 213-234, ISSN 1547-2450, https://doi.org/10.1080/15472450.2020.1807346.
[7]
Bin Lv, Hao Xu, Jianqing Wu, Yuan Tian, Sheng Tian, Suoyao Feng, Revolution and rotation-based method for roadside lidar data integration, Optics & Laser Technology, Volume 119, 2019, 105571, ISSN 0030-3992, https://doi.org/10.1016/j.optlastec.2019.105571.
[8]
Zhang, T., Jin, P. J., & Ge, Y. 2022. Weighted bayesian gaussian mixture model for roadside lidar object detection. Ithaca: Cornell University Library, arXiv.org.
[9]
Wang, L., & Lan, J. 2022. Adaptive polar-grid gaussian-mixture model for foreground segmentation using roadside lidar. Remote Sensing, 14(11), 2522.
[10]
Zhang, T., & Jin, P. J. 2022. Roadside lidar vehicle detection and tracking using range and intensity background subtraction. Journal of Advanced Transportation, 2022.
Recommendations
A portable roadside vehicle detection system based on multi-sensing fusion
To improve traffic efficiency, the intelligent transportation system (ITS) is widely used in urban roads. The sensing system is preferred to be deployed at roadside because the normal traffic would not be interfered. In the field of the magnetic vehicle ...
Markov-based Emergency Message Reduction Scheme for Roadside Assistance
Currently, almost every family has at least one car; thus, vehicle density is increasing annually. However, road capacity is finite; consequently, traffic accident frequency may increase due to increasing vehicle density. Typically, car accidents result ...
An Enhanced Information Sharing Roadside Unit Allocation Scheme for Vehicular Networks
Sharing up-to-date environment information collected by intelligent connected vehicles is critical in achieving travel comfort, convenience, and safety in vehicular networks. Individually collected information should be made available to other vehicular ...
Comments
Information & Contributors
Information
Published In
December 2022
1396 pages
ISBN:9781450398343
DOI:10.1145/3584376
Copyright © 2022 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 19 April 2023
Check for updates
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
RICAI 2022
RICAI 2022: 2022 4th International Conference on Robotics, Intelligent Control and Artificial Intelligence
December 16 - 18, 2022
Dongguan, China
Acceptance Rates
Overall Acceptance Rate 140 of 294 submissions, 48%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 29Total Downloads
- Downloads (Last 12 months)14
- Downloads (Last 6 weeks)2
Reflects downloads up to 14 Sep 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format