scholarly journals Hololens Used for Precise Position Tracking of the Third Party Devices - Autonomous Vehicles

2019 ◽  
Vol 21 (2) ◽  
pp. 18-23
Author(s):  
Jindrich Cyrus ◽  
David Krcmarik ◽  
Reza Moezzi ◽  
Jan Koci ◽  
Michal Petru
Keyword(s):  
Autonomous Vehicles ◽  
Complex Space ◽  
Autonomous Driving ◽  
Real Space ◽  
Third Party ◽  
Location Information ◽  
Position Tracking ◽  
Precise Position ◽  
The Third ◽  
Localization And Mapping

A completely new area of HoloLens usage is proposed. The Hololens is an augmented reality device, which provides the high precision location information. Such an information is normally used to accurately position holograms within the real space with respect to the viewer (user of HoloLens). The information is precise enough to use it for reporting the position for the purpose of autonomous driving. Several experiments have been executed in vast areas (20 m x 40 m) in order to find out the potential error coming from vibrations or other effects when moving the HoloLens. The results show that the technology can be used for spaces, which are previously known by the system - pre-scan of the space is needed. The big advantage of the system is its readiness for indoor positioning applications with no additional infrastructure needed, simultaneous localization and mapping, complex space mapping and reached precision. The disadvantage is mainly the costs.

Future Blockchain Technology for Autonomous Applications/Autonomous Vehicle

2022 ◽  
pp. 1027-1038
Author(s):  
Arnab Kumar Show ◽  
Abhishek Kumar ◽  
Achintya Singhal ◽  
Gayathri N. ◽  
K. Vengatesan
Keyword(s):  
Autonomous Vehicles ◽  
New Technology ◽  
Autonomous Vehicle ◽  
The Self ◽  
Third Party ◽  
Traffic Information ◽  
Online Transactions ◽  
The Third ◽  
Blockchain Technology ◽  
Self Driving Cars

The autonomous industry has rapidly grown for self-driving cars. The main purpose of autonomous industry is trying to give all types of security, privacy, secured traffic information to the self-driving cars. Blockchain is another newly established secured technology. The main aim of this technology is to provide more secured, convenient online transactions. By using this new technology, the autonomous industry can easily provide more suitable, safe, efficient transportation to the passengers and secured traffic information to the vehicles. This information can easily gather by the roadside units or by the passing vehicles. Also, the economical transactions can be possible more efficiently since blockchain technology allows peer-to-peer communications between nodes, and it also eliminates the need of the third party. This chapter proposes a concept of how the autonomous industry can provide more adequate, proper, and safe transportation with the help of blockchain. It also examines for the possibility that autonomous vehicles can become the future of transportation.

Intelligent Prevention Method for Third-Party Damage of Long-Distance Pipeline Based on Mobile Devices Location Information

2020 ◽  
Author(s):  
Jiatong Ling ◽  
Hang Zhang ◽  
Shaohua Dong ◽  
Jinheng Luo
Keyword(s):  
Mobile Devices ◽  
Oil And Gas ◽  
Third Party ◽  
Location Information ◽  
Long Distance ◽  
The Third ◽  
Pipeline Segment ◽  
The Third Party ◽  
The Difference ◽  
Prevention Method

Abstract As one of the main risks of long-distance oil and gas pipelines, the consequences of pipeline accidents caused by third-party damage (TPD) are usually catastrophic. At present, TPD prevention approaches mainly include manual line patrol, fiber-optical vibration warning, and unmanned aerial vehicle (UAV) line patrol, but there are some limitations such as untimely warning, false alarm, and the missed report. As the location technology of mobile device matures, the user group provides massive data sources for the collection of location information, with which the tracks and features of the third-party activity along the pipeline can be directly obtained. Therefore, this paper proposes a method to identify the TPD behavior based on the location data of mobile devices. Firstly, the characteristics of relevant destruction behaviors were extracted from the historical destruction events. Then, the location information of the third-party activity near the target pipeline is obtained and the data is processed to remove the influence of noise, to reduce the computational burden of the subsequent identification process. Finally, calculate the difference degree of neighborhood trajectory and the similarity with the TPD features based on the data feature grouping (Difference feature and Similarity feature) to classify the type of third-party activity. Taking a 10km pipeline segment as an example, the method of this paper is used to preprocess the collected data and calculate the difference degree and similarity, 232 suspected TPD events are identified. After the on-site verification of the suspected damage by the line patrol, the results show that the method can better identify the third-party activities near the pipeline.

scholarly journals Visual Semantic Landmark-Based Robust Mapping and Localization for Autonomous Indoor Parking

Sensors ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 161 ◽  
Author(s):  
Junqiao Zhao ◽  
Yewei Huang ◽  
Xudong He ◽  
Shaoming Zhang ◽  
Chen Ye ◽  
...  
Keyword(s):  
Real Time ◽  
Autonomous Vehicles ◽  
Indoor Localization ◽  
Autonomous Driving ◽  
Human Beings ◽  
Low Level ◽  
Localization And Mapping ◽  
Challenging Tasks ◽  
Parking Lot ◽  
Feature Based

Autonomous parking in an indoor parking lot without human intervention is one of the most demanded and challenging tasks of autonomous driving systems. The key to this task is precise real-time indoor localization. However, state-of-the-art low-level visual feature-based simultaneous localization and mapping systems (VSLAM) suffer in monotonous or texture-less scenes and under poor illumination or dynamic conditions. Additionally, low-level feature-based mapping results are hard for human beings to use directly. In this paper, we propose a semantic landmark-based robust VSLAM for real-time localization of autonomous vehicles in indoor parking lots. The parking slots are extracted as meaningful landmarks and enriched with confidence levels. We then propose a robust optimization framework to solve the aliasing problem of semantic landmarks by dynamically eliminating suboptimal constraints in the pose graph and correcting erroneous parking slots associations. As a result, a semantic map of the parking lot, which can be used by both autonomous driving systems and human beings, is established automatically and robustly. We evaluated the real-time localization performance using multiple autonomous vehicles, and an repeatability of 0.3 m track tracing was achieved at a 10 kph of autonomous driving.

Future Blockchain Technology for Autonomous Applications/Autonomous Vehicle

2021 ◽  
pp. 165-177
Author(s):  
Arnab Kumar Show ◽  
Abhishek Kumar ◽  
Achintya Singhal ◽  
Gayathri N. ◽  
K. Vengatesan
Keyword(s):  
Autonomous Vehicles ◽  
New Technology ◽  
Autonomous Vehicle ◽  
The Self ◽  
Third Party ◽  
Traffic Information ◽  
Online Transactions ◽  
The Third ◽  
Blockchain Technology ◽  
Self Driving Cars

The autonomous industry has rapidly grown for self-driving cars. The main purpose of autonomous industry is trying to give all types of security, privacy, secured traffic information to the self-driving cars. Blockchain is another newly established secured technology. The main aim of this technology is to provide more secured, convenient online transactions. By using this new technology, the autonomous industry can easily provide more suitable, safe, efficient transportation to the passengers and secured traffic information to the vehicles. This information can easily gather by the roadside units or by the passing vehicles. Also, the economical transactions can be possible more efficiently since blockchain technology allows peer-to-peer communications between nodes, and it also eliminates the need of the third party. This chapter proposes a concept of how the autonomous industry can provide more adequate, proper, and safe transportation with the help of blockchain. It also examines for the possibility that autonomous vehicles can become the future of transportation.

Digital Civil Liberties and the Translation Problem

2019 ◽  
pp. 364-391
Author(s):  
Michael Washington ◽  
Neil Richards
Keyword(s):  
Personal Information ◽  
Civil Liberties ◽  
Digital Technologies ◽  
Third Party ◽  
Location Information ◽  
Location Tracking ◽  
The European Union ◽  
Judicial Interpretation ◽  
The Third ◽  
New Type

This chapter examines the implications of digital technologies for civil liberties and for the translation problem by looking at four case studies, each of which shows how technology and social norms have changed to allow substantially greater government access to personal information. The chapter first traces the origins of the translation problem and the trouble with telephonic communications before discussing a new type of information, location information, and how courts have addressed the translation problem when location information is involved. It then considers how GPS and other location-tracking technologies have stretched the law to its doctrinal limits. It also explores the translation problem in the contexts of smartphones and the cloud, as well as how a single doctrine of judicial interpretation, the third-party doctrine, has threatened civil liberties worldwide. Finally, it describes the translation problem in the European Union.

The Third Party Logistics Spatial Location Information Services

2011 ◽  
Vol 204-210 ◽  
pp. 2019-2022
Author(s):  
Xiao Lu Li ◽  
Wen Feng Zheng ◽  
Xin Yu Yu ◽  
Dan Wang
Keyword(s):  
Spatial Location ◽  
Information Services ◽  
Third Party ◽  
Location Information ◽  
Third Party Logistics ◽  
Logistics Industry ◽  
The Third Party Logistics ◽  
The Third ◽  
Spatial Location Information ◽  
The Third Party

To help the logistics industry to reduce the high costs of facilities and resources, the research, based on SOA architecture, treating OGC specifications as its service standards, is to build the third party logistics spatial location information services platform. This platform is going to achieve resource sharing and service integration for the different user roles of logistics industry, and reduce resources excessively construction and idle waste. As most resources are provided by private transporters or private warehouse operators in China, sources sharing set high demand on the design of platform architecture. This experimental verification platform implements the expected functionality and validates the feasibility of the system design.

scholarly journals Research on NDT-based Positioning for Autonomous Driving

2021 ◽  
Vol 257 ◽  
pp. 02055
Author(s):  
Sijia Liu ◽  
Jie Luo ◽  
Jinmin Hu ◽  
Haoru Luo ◽  
Yu Liang
Keyword(s):  
Real Time ◽  
Electromagnetic Interference ◽  
Autonomous Vehicles ◽  
Autonomous Navigation ◽  
Point Cloud ◽  
Autonomous Driving ◽  
Mapping Method ◽  
Signal Loss ◽  
Positioning Accuracy ◽  
Localization And Mapping

Autonomous driving technology is one of the currently popular technologies, while positioning is the basic problem of autonomous navigation of autonomous vehicles. GPS is widely used as a relatively mature solution in the outdoor open road environment. However, GPS signals will be greatly affected in a complex environment with obstruction and electromagnetic interference, even signal loss may occur if serious, which has a great impact on the accuracy, stability and reliability of positioning. For the time being, L4 and most L3 autonomous driving modules still provide registration and positioning based on the high-precision map constructed. Based on this, this paper elaborates on the reconstruction of the experimental scene environment, using the SLAM (simultaneous localization and mapping) method to construct a highprecision point cloud map. On the constructed prior map, the 3D laser point cloud NDT matching method is used for real-time positioning, which is tested and verified on the “JAC Electric Vehicle” platform. The experimental results show that this algorithm has high positioning accuracy and its real-time performance meets the requirements, which can replace GPS signals to complete the positioning of autonomous vehicles when there is no GPS signal or the GPS signal is weak, and provide positioning accuracy meeting the requirements.

scholarly journals 1 year, 1000 km: The Oxford RobotCar dataset

2016 ◽  
Vol 36 (1) ◽  
pp. 3-15 ◽  
Author(s):  
Will Maddern ◽  
Geoffrey Pascoe ◽  
Chris Linegar ◽  
Paul Newman
Keyword(s):  
Autonomous Vehicles ◽  
Ground Truth ◽  
Heavy Rain ◽  
Weather Conditions ◽  
Autonomous Driving ◽  
Urban Environments ◽  
Ground Truth Data ◽  
Full Dataset ◽  
Localization And Mapping

We present a challenging new dataset for autonomous driving: the Oxford RobotCar Dataset. Over the period of May 2014 to December 2015 we traversed a route through central Oxford twice a week on average using the Oxford RobotCar platform, an autonomous Nissan LEAF. This resulted in over 1000 km of recorded driving with almost 20 million images collected from 6 cameras mounted to the vehicle, along with LIDAR, GPS and INS ground truth. Data was collected in all weather conditions, including heavy rain, night, direct sunlight and snow. Road and building works over the period of a year significantly changed sections of the route from the beginning to the end of data collection. By frequently traversing the same route over the period of a year we enable research investigating long-term localization and mapping for autonomous vehicles in real-world, dynamic urban environments. The full dataset is available for download at: http://robotcar-dataset.robots.ox.ac.uk

Resolving tension from the third-party forgiveness effect

2014 ◽  
Author(s):  
Jaclyn M. Moloney ◽  
Chelsea A. Reid ◽  
Jody L. Davis ◽  
Jeni L. Burnette ◽  
Jeffrey D. Green
Keyword(s):  
Third Party ◽  
The Third ◽  
The Third Party
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