Ultrasonic Multi-Sensor Detection Patterns On Autonomous Vehicles Using Data Stream Method

Roads are a central element of transportation systems, enabling economic and social development, fostering territorial cohesion and facilitating the movement of people and cargo. Governments have devoted significant financial resources to developing and improving their road networks, and are still facing increasing pressure to ensure proper maintenance and payments to those concessionaires that developed roads under public–private partnership arrangements. As in other sectors, digitalization is paving a way towards significant changes in the way we build, operate and finance infrastructure. These changes will have a profound impact on the entire life cycle of an infrastructure, from the design and/or construction stage, to its operation and transfer. This article provides an overall overview of the main technological developments which are, or could impact road infrastructure in the short, medium and long term. For each technological development identified in our research, we analyse the potential impact on Capex, Opex and revenues as well as their level of maturity and expected lifetime for mass adoption, and also the main bottlenecks or barriers to implementation. Additionally, we explore potential savings on investment (capex) and operational costs (opex) and increase in revenues, using data from the Portuguese highway companies. Savings can represent almost 30% of capex and opex. Overall, savings and increases in revenues can represent an impact similar to 20–40% of current revenues. The findings show that digitalization and technological development in the road sector can significantly impact the economic performance of roads, thus enhancing the value of money for the society. The findings also show that there might be some excess capacity of road systems once autonomous vehicles achieve higher market penetration. However, there are still some relevant legal, regulatory, institutional and technological and economic barriers that are slowing down the digitalization process.

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Perspective analysis of telecommunication fraud detection using data stream analytics and neural network classification based data mining

International Journal of Information Technology ◽

10.1007/s41870-017-0036-5 ◽

2017 ◽

Vol 9 (3) ◽

pp. 303-310 ◽

Cited By ~ 7

Author(s):

Vanita Jain

Keyword(s):

Neural Network ◽

Data Mining ◽

Data Stream ◽

Fraud Detection ◽

Neural Network Classification ◽

Using Data ◽

Stream Analytics ◽

Perspective Analysis

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Simultaneous Estimation of Vehicle Roll and Sideslip Angles through a Deep Learning Approach

Sensors ◽

10.3390/s20133679 ◽

2020 ◽

Vol 20 (13) ◽

pp. 3679

Author(s):

Lisardo Prieto González ◽

Susana Sanz Sánchez ◽

Javier Garcia-Guzman ◽

María Jesús L. Boada ◽

Beatriz L. Boada

Keyword(s):

Deep Learning ◽

Autonomous Vehicles ◽

Ground Truth ◽

Critical Parameters ◽

Simultaneous Estimation ◽

Adequate Knowledge ◽

Non Linear ◽

Intelligence Models ◽

Artificial Intelligence Models ◽

Using Data

Presently, autonomous vehicles are on the rise and are expected to be on the roads in the coming years. In this sense, it becomes necessary to have adequate knowledge about its states to design controllers capable of providing adequate performance in all driving scenarios. Sideslip and roll angles are critical parameters in vehicular lateral stability. The later has a high impact on vehicles with an elevated center of gravity, such as trucks, buses, and industrial vehicles, among others, as they are prone to rollover. Due to the high cost of the current sensors used to measure these angles directly, much of the research is focused on estimating them. One of the drawbacks is that vehicles are strong non-linear systems that require specific methods able to tackle this feature. The evolution in Artificial Intelligence models, such as the complex Artificial Neural Network architectures that compose the Deep Learning paradigm, has shown to provide excellent performance for complex and non-linear control problems. In this paper, the authors propose an inexpensive but powerful model based on Deep Learning to estimate the roll and sideslip angles simultaneously in mass production vehicles. The model uses input signals which can be obtained directly from onboard vehicle sensors such as the longitudinal and lateral accelerations, steering angle and roll and yaw rates. The model was trained using hundreds of thousands of data provided by Trucksim® and validated using data captured from real driving maneuvers using a calibrated ground truth device such as VBOX3i dual-antenna GPS from Racelogic®. The use of both Trucksim® software and the VBOX measuring equipment is recognized and widely used in the automotive sector, providing robust data for the research shown in this article.

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