Data-Driven Ground Models: The Road to Fully-Integrated Site Characterization and Design

2022 ◽  
pp. 3-21
Author(s):  
Maarten Vanneste ◽  
Guillaume Sauvin ◽  
Jean-Rémi Dujardin ◽  
Carl Fredrik Forsberg ◽  
Rasmus T. Klinkvort ◽  
...  
Keyword(s):  
Site Characterization ◽  
Data Driven ◽  
Fully Integrated ◽  
The Road

Identification of Linear Tire Cornering Stiffness Using Subspace Methods

2014 ◽  
Vol 701-702 ◽  
pp. 492-497
Author(s):  
Teng Yue Ba ◽  
Xi Qiang Guan ◽  
Jian Wu Zhang
Keyword(s):  
Data Driven ◽  
Subspace Identification ◽  
Subspace Methods ◽  
Identification Methods ◽  
The Road ◽  
Strong Robustness ◽  
Subspace Algorithms ◽  
Double Lane Change ◽  
On The Road ◽  
Lane Change Test

In this paper, subspace identification methods are proposed to estimate the linear tire cornering stiffness, which are only based on the road tests data without any prior knowledge. This kind of data-driven method has strong robustness. In order to validate the feasibility and effectiveness of the algorithms, a series of standard road tests are carried out. Comparing with different subspace algorithms used in road tests, it can be concluded that the front tire cornering stiffness can be estimated accurately by the N4SID and CCA methods when the double lane change test data are taken into analysis.

Influence of Autonomous Vehicles on Car-Following Behavior of Human Drivers

2019 ◽  
Vol 2673 (12) ◽  
pp. 367-379 ◽  
Author(s):  
Yalda Rahmati ◽  
Mohammadreza Khajeh Hosseini ◽  
Alireza Talebpour ◽  
Benjamin Swain ◽  
Christopher Nelson
Keyword(s):  
Autonomous Vehicles ◽  
Simulation Models ◽  
Driving Behavior ◽  
Data Driven ◽  
Microscopic Simulation ◽  
Car Following ◽  
The Road ◽  
Human Driver ◽  
Significant Difference ◽  
On The Road

Despite numerous studies on general human–robot interactions, in the context of transportation, automated vehicle (AV)–human driver interaction is not a well-studied subject. These vehicles have fundamentally different decision-making logic compared with human drivers and the driving interactions between AVs and humans can potentially change traffic flow dynamics. Accordingly, through an experimental study, this paper investigates whether there is a difference between human–human and human–AV interactions on the road. This study focuses on car-following behavior and conducted several car-following experiments utilizing Texas A&M University’s automated Chevy Bolt. Utilizing NGSIM US-101 dataset, two scenarios for a platoon of three vehicles were considered. For both scenarios, the leader of the platoon follows a series of speed profiles extracted from the NGSIM dataset. The second vehicle in the platoon can be either another human-driven vehicle (scenario A) or an AV (scenario B). Data is collected from the third vehicle in the platoon to characterize the changes in driving behavior when following an AV. A data-driven and a model-based approach were used to identify possible changes in driving behavior from scenario A to scenario B. The findings suggested there is a statistically significant difference between human drivers’ behavior in these two scenarios and human drivers felt more comfortable following the AV. Simulation results also revealed the importance of capturing these changes in human behavior in microscopic simulation models of mixed driving environments.

scholarly journals Data-driven profiling of traffic flow with varying road conditions

2019 ◽  
pp. 149-157 ◽  
Author(s):  
O K Golovnin
Keyword(s):  
Traffic Flow ◽  
Road Traffic ◽  
Flow Characteristics ◽  
Weather Conditions ◽  
Flow Speed ◽  
Data Driven ◽  
The Road ◽  
Data Driven Approach ◽  
Traffic Flow Characteristics ◽  
The City

The article describes the road, institutional and weather conditions that affect the traffic flow. I proposed a method for traffic flow profiling using a data-driven approach. The method operates with macroscopic traffic flow characteristics and detailed data of road conditions. The article presents the results of traffic flow speed and intensity profiling taking into account weather conditions. The study used road traffic and conditions data for the city of Aarhus, Denmark. The results showed that the method is effective for traffic flow forecasting due to varying road conditions.

scholarly journals The use of language in migrant stay-at-home parents’ process of integration

2017 ◽  
Vol 11 (3) ◽  
pp. 121-142 ◽  
Author(s):  
Päivi Iikkanen
Keyword(s):  
Content Analysis ◽  
Social Inclusion ◽  
Data Driven ◽  
Integration Process ◽  
Fully Integrated ◽  
Recent Migrant ◽  
Conventional Content Analysis ◽  
Using Data ◽  
Shed Light ◽  
At Home

This paper examines the use of language and how it contributes to the experiences of inclusion and exclusion of recent migrant stay-at-home parents in Finland. The study shows how the use of language facilitates the integration process of newly arrived stay-at-home parents of migrant background and affects their experiences of social inclusion and/or exclusion. The study uses the translingual approach (Canagarajah, 2013) to shed light on the multilingual reality migrants are faced with in their new surroundings. The approach is ethnographic and the data is interpreted using data-driven conventional content analysis (Tuomi & Sarajärvi, 2009, pp. 108–113). The results suggest that English works quite well as a lingua franca, although to become “fully” integrated, migrants feel that they need to develop a command of the local language.

scholarly journals A Mobility Performance Assessment on Plug-in EV Battery

2020 ◽  
Vol 3 (2) ◽  
Author(s):  
Seyed Mohammad Rezvanizanian ◽  
Yixiang Huang ◽  
Jiang Chuan ◽  
Jay Lee
Keyword(s):  
State Of Charge ◽  
Data Driven ◽  
Mobility Prediction ◽  
Data Sets ◽  
Speed Profile ◽  
The Road ◽  
Mobility Performance ◽  
Benchmark Data ◽  
Battery State Of Charge ◽  
Road Slope

This paper deals with mobility prediction of LiFeMnPO4 batteries for an emission-free Electric Vehicle. The data-driven model has been developed based on empirical data from two different road types –highway and local streets –and two different driving modes – aggressive and moderate. Battery State of Charge (SoC) can be predicted on any new roads based on the trained model by selecting the drving mode. In this paper, the performance of Adaptive Recurrent Neural Network (ARNN) and regression is evaluated using two benchmark data sets. The ARNN model at first estimates the speed profile of the new road based on slope and then both slope and speed is going to be used as the input to estimate battery current and SoC. Through comparison it is found that if ARNN system is appropriately trained, it performs with better accuracy than Regression in both two road types and driving modes. The results show that prediction SoC model follows the Columb-counting SoC according to the road slope.

Assyrians Abroad: Expanding Borders Through Mobile Identities in the Middle Bronze Age

2018 ◽  
Vol 4 (1-2) ◽  
pp. 61-93 ◽  
Author(s):  
Nancy Highcock
Keyword(s):  
Bronze Age ◽  
Political Institutions ◽  
Driving Forces ◽  
Community Identity ◽  
Central Anatolia ◽  
Fully Integrated ◽  
The Road ◽  
Middle Bronze Age ◽  
On The Road ◽  
Civic Institutions

AbstractRecent work by both archaeologists and Assyriologists has characterized the main Assyrian settlement at Kaneš/Kültepe not as “colony” at all but as a place in which Assyrians fully integrated themselves into Anatolian society to create a hybridized community or “middle ground.” This paper builds upon their work by examining the ways in which Assyrians participated in such an intercultural society whilst still maintaining the bounded social category of “Assyrian.” Through the reconstruction of their civic institutions and social traditions abroad, Assyrian merchants were able to expand their mental topography of what constituted “Assyrian-ness” from northern Mesopotamia across central Anatolia. This phenomenon is framed within wider discussions of mobile societies and the Old Assyrian textual record to illustrate that a community identity founded upon the mother city of Assur and its cultural conventions continued to thrive across various political and cultural borders. Treaties and letters demonstrate that these borders were well defined and maintained by the Assyrians themselves, but concurrently, that the driving forces behind a trader’s life on the road also meant for such borders to be expanded and reconstituted. Analyzing the Old Assyrian mercantile phenomenon through the vector of mobility enables us to better understand the ways in which the Old Assyrian merchants maintained a cohesive social identity and bounded community whilst working and living in “foreign” territories. Mobility is not an inherently disintegrating force, but shape the common cultural and political institutions which act as fibers binding communities together across great distances.

scholarly journals The Road to a Downstream Emissions Trading System: Designing a Scheme Combining Motorist and Government Participation

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Fahd Mohamed Omar Al-Guthmy ◽  
Wanglin Yan
Keyword(s):  
Local Governments ◽  
Emissions Trading ◽  
Road Transport ◽  
The European Union ◽  
Trading System ◽  
Fully Integrated ◽  
The Road ◽  
Emissions Trading Scheme ◽  
Trading Scheme ◽  
Emissions Trading System

As downstream road transport has not been fully integrated into any emissions trading scheme, this paper proposes and evaluates the possibility of one by addressing the main barriers hindering its development. Based on this, a scheme which separates the “Cap” and “Trade” participation to motorists and local governments, respectively, is presented through a systematic review. We investigate how the scheme addresses the problems of cost, administrative burden, and fuel allowance allocation as they are all key factors that need equal consideration. We also justify the model’s unique structure and characteristics against the world’s largest scheme, the European Union Emissions Trading System (EU ETS), to ensure they cater to the three aforementioned issues barring its viability. It is concluded that, by amending specific policy attributes of a road-based emissions trading scheme significantly, it could be more practical both economically and administratively. Also, leveraging on existing institutional arrangements would allow for an economically feasible environment for the administration and management of such a scheme.

State-of-the-art remote characterization of shallow marine sediments: the road to a fully integrated solution

2017 ◽  
Vol 15 (4) ◽  
pp. 387-402 ◽  
Author(s):  
Mark E. Vardy ◽  
Maarten Vanneste ◽  
Timothy J. Henstock ◽  
Michael A. Clare ◽  
Carl Fredrik Forsberg ◽  
...  
Keyword(s):  
Marine Sediments ◽  
State Of The Art ◽  
Fully Integrated ◽  
Shallow Marine ◽  
The Road ◽  
Shallow Marine Sediments

scholarly journals An OSM Data-Driven Method for Road-Positive Sample Creation

2020 ◽  
Vol 12 (21) ◽  
pp. 3612
Author(s):  
Jiguang Dai ◽  
Chengcheng Li ◽  
Yuqiang Zuo ◽  
Haibin Ai
Keyword(s):  
Texture Feature ◽  
Positive Sample ◽  
Data Driven ◽  
Self Similarity ◽  
The Road ◽  
Network Training ◽  
Orientation Histogram ◽  
Iterative Interpolation ◽  
Road Width ◽  
Local Road

Determining samples is considered to be a precondition in deep network training and learning, but at present, samples are usually created manually, which limits the application of deep networks. Therefore, this article proposes an OpenStreetMap (OSM) data-driven method for creating road-positive samples. First, based on the OSM data, a line segment orientation histogram (LSOH) model is constructed to determine the local road direction. Secondly, a road homogeneity constraint rule and road texture feature statistical model are constructed to extract the local road line, and on the basis of the local road lines with the same direction, a polar constraint rule is proposed to determine the local road line set. Then, an iterative interpolation algorithm is used to connect the local road lines on both sides of the gaps between the road lines. Finally, a local texture self-similarity (LTSS) model is implemented to determine the road width, and the centerpoint autocorrection model and random sample consensus (RANSAC) algorithm are used to extract the road centerline; the road width and road centerline are used to complete the creation of the road-positive samples. Experiments are conducted on different scenes and different types of images to demonstrate the proposed method and compare it with other approaches. The results demonstrate that the proposed method for creating road-positive samples has great advantages in terms of accuracy and integrity.

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