MATISSE 3.0: A Large-Scale Multi-agent Simulation System for Intelligent Transportation Systems

2018 ◽  
pp. 357-360 ◽  
Author(s):  
B. Torabi ◽  
M. Al-Zinati ◽  
R. Z. Wenkstern
Keyword(s):  
Intelligent Transportation Systems ◽  
Large Scale ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Simulation System ◽  
Agent Simulation ◽  
Multi Agent

scholarly journals GNSS RUMS: GNSS Realistic Urban Multi-agent Simulator for Collaborative Positioning Research

2020 ◽  
Author(s):  
Guohao Zhang ◽  
Bing Xu ◽  
Hoi-Fung Ng ◽  
Li-Ta Hsu
Keyword(s):  
Urban Area ◽  
Intelligent Transportation Systems ◽  
Urban Areas ◽  
Large Scale ◽  
Transportation Systems ◽  
Accurate Localization ◽  
Multipath Signal ◽  
Multi Agent ◽  
Scale Test ◽  
Error Behavior

Accurate localization of road agents is the basis of intelligent transportation systems, which is still difficult to achieve for GNSS positioning in urban areas due to the signal interferences from buildings. Various collaborative positioning techniques are recently developed to improve the positioning performance by the aid from neighboring agents. However, it is still challenging to study their performances comprehensively. The GNSS measurement error behavior is complicated in urban areas and unable to be represented by naive models. On the other hand, real experiment requiring numbers of devices is hard to be conducted, especially for a large-scale test. Therefore, a GNSS realistic urban measurement simulator is developed to provide measurements for collaborative positioning studies. The proposed simulator employs a ray-tracing technique searching for all possible interferences in the urban area. Then, it categorizes them into direct, reflected, diffracted, and multipath signal to simulate the pseudorange, carrier-phase, 〖C/N〗_0, and Doppler shift measurements correspondingly. The performance of the proposed simulator is validated through real experimental comparisons with different scenarios. The proposed simulator is also applied with different positioning algorithms, which verifies it is sophisticated enough for the collaborative positioning studies in the urban area.

On Modeling and Verification of Agent-Based Traffic Simulation Properties in Alloy

2012 ◽  
Vol 4 (4) ◽  
pp. 38-60 ◽  
Author(s):  
Junia Valente ◽  
Frederico Araujo ◽  
Rym Z. Wenkstern
Keyword(s):  
Intelligent Transportation Systems ◽  
Large Scale ◽  
Traffic Simulation ◽  
Simulation Models ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Agent Based ◽  
Traffic Models ◽  
First Order ◽  
New Generation

The advances in Intelligent Transportation Systems (ITS) call for a new generation of traffic simulation models that support connectivity and collaboration among simulated vehicles and traffic infrastructure. In this paper we introduce MATISSE, a complex, large scale agent-based framework for the modeling and simulation of ITS and discuss how Alloy, a modeling language based on set theory and first order logic, was used to specify, verify, and analyze MATISSE’s traffic models.

scholarly journals Automatic Extraction of Structural and Non-Structural Road Edges from Mobile Laser Scanning Data

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 5030 ◽  
Author(s):  
Yang ◽  
Liu ◽  
Jiang ◽  
Xu ◽  
Sheng ◽  
...  
Keyword(s):  
Intelligent Transportation Systems ◽  
Laser Scanning ◽  
Large Scale ◽  
Auxiliary Information ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Road Maintenance ◽  
The Road ◽  
Large Scale Data ◽  
Road Width

Accurate road information is important for applications involving road maintenance, intelligent transportation, and road network updates. Mobile laser scanning (MLS) can effectively extract road information. However, accurately extracting road edges based on large-scale data for complex road conditions, including both structural and non-structural road types, remains difficult. In this study, a robust method to automatically extract structural and non-structural road edges based on a topological network of laser points between adjacent scan lines and auxiliary surfaces is proposed. The extraction of road and curb points was achieved mainly from the roughness of the extracted surface, without considering traditional thresholds (e.g., height jump, slope, and density). Five large-scale road datasets, containing different types of road curbs and complex road scenes, were used to evaluate the practicality, stability, and validity of the proposed method via qualitative and quantitative analyses. Measured values of the correctness, completeness, and quality of extracted road edges were over 95.5%, 91.7%, and 90.9%, respectively. These results confirm that the proposed method can extract road edges from large-scale MLS datasets without the need for auxiliary information on intensity, image, or geographic data. The proposed method is effective regardless of whether the road width is fixed, the road is regular, and the existence of pedestrians and vehicles. Most importantly, the proposed method provides a valuable solution for road edge extraction that is useful for road authorities when developing intelligent transportation systems, such as those required by self-driving vehicles.

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