scholarly journals Implementation of a Sensor Big Data Processing System for Autonomous Vehicles in the C-ITS Environment

2020 ◽  
Vol 10 (21) ◽  
pp. 7858
Author(s):  
Aelee Yoo ◽  
Sooyeon Shin ◽  
Junwon Lee ◽  
Changjoo Moon

To provide a service that guarantees driver comfort and safety, a platform utilizing connected car big data is required. This study first aims to design and develop such a platform to improve the function of providing vehicle and road condition information of the previously defined central Local Dynamic Map (LDM). Our platform extends the range of connected car big data collection from OBU (On Board Unit) and CAN to camera, LiDAR, and GPS sensors. By using data of vehicles being driven, the range of roads available for analysis can be expanded, and the road condition determination method can be diversified. Herein, the system was designed and implemented based on the Hadoop ecosystem, i.e., Hadoop, Spark, and Kafka, to collect and store connected car big data. We propose a direction of the cooperative intelligent transport system (C-ITS) development by showing a plan to utilize the platform in the C-ITS environment.

2018 ◽  
Vol 19 (1-2) ◽  
pp. 69-92 ◽  
Author(s):  
Carlos Oliveira Cruz ◽  
Joaquim Miranda Sarmento

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.


Author(s):  
Jorge Figueroa ◽  
Raúl Carrasco ◽  
Diego Fuentealba ◽  
Eduardo Viera ◽  
Carolina Lagos

Today, transit control systems go beyond simple controllers located at the intersections of our streets, involving large companies in the field, which with the implementation and use of sophisticated equipment encompass endless new and advanced technologies that manage to give control to the massive automotive park, thus ensuring fluidity and road safety. Many of these systems are used in the big world capitals, which is why the model used in Santiago, Chile is a system applied and brought directly by the SIEMENS Company of England (specifically the system used in the City of London). It is capable of transmitting the different control signals in a similar and digital way from the different interconnected devices in and out of the road infrastructure.


2019 ◽  
Vol 13 ◽  
pp. 174830261987419
Author(s):  
Wenjing Li ◽  
Shiaofang Liang ◽  
Yinjiao Jiang ◽  
Qiuxia Pan

In view of the complex road conditions in today’s cities, the traditional prediction methods for road conditions are not so accurate, and the optimization algorithm for the logistics distribution path is not sensitive to changes in the road conditions so that its application in an actual logistics distribution system is not effective. This article proposes a road condition prediction and logistics distribution path optimization algorithm based on traffic big data. First, it analyses the characteristics of the road condition information of traffic big data. By combining the powerful feature extraction and self-learning ability of a deep belief network, it establishes a road condition prediction model based on a deep belief network and completes the model training and verification through the learning of traffic big data. Then, it combines the road condition prediction (result) information, traffic network information, and logistics distribution information to construct the time-share weighted traffic network. It then modifies the access set and pheromone variables of the ant algorithm based on the time-share traffic network to establish the road condition prediction and logistics distribution path optimization algorithm based on traffic big data. Finally, it conducts comparative experiments with other logistics distribution path optimization algorithms. The experimental results show that the proposed algorithm is superior to other logistics distribution optimization algorithms. Therefore, this algorithm is an effective method for optimizing logistics distribution.


Author(s):  
S.U. Uvaysov ◽  
V.V. Chernoverskaya ◽  
N.N. Kalinin ◽  
A.A. Markin

The active development of unmanned vehicles, in control systems of which most of the currently created artificial intelligence technologies are used, from machine vision systems to decision-making in multi-criteria control tasks, has led to the emergence of such vehicles on public roads and, in fact, has become an objective reality of ours. life. In such a situation, the person driving needs additional information to drive safely. Currently, the road infrastructure is dynamically developing, forming into a developed distributed telecommunication system with accompanying services. And now it is increasingly associated with the concept of "intelligent transport system" (ITS), to which are connected automotive equipment subsystems, wireless communication subsystems, roadside equipment subsystems and a global navigation satellite system module. Unmanned vehicles, which are integrated into the road environment, form a single telecommunication system for controlling the movement of vehicles with it. Wireless technologies and digital models of road infrastructure are important components of such a system. The information technology concept, which implies the integration of computing resources into physical entities, in particular autonomous robots and unmanned vehicles, is called the concept of cyber-physical systems. The computational component in it is distributed throughout the system. The study of the possibility of constructing digital models of roads and road infrastructure with their subsequent intrasystem transfer between interacting objects is undoubtedly of considerable interest. At the research stage of the implementation of such a technology, it is important to analyze the possibility of building and deploying modern ITS, highlight the main problems associated with the visualization of digital ITS models, and propose ways to solve the tasks. As part of the study, an overview of modern wireless technologies and communication standards with the prospect of their application in the infrastructure of the road environment is given, foreign experience of deploying such systems, their features and limitations, is considered. The functional structure of the intelligent transport system is proposed. The results of modeling the road network (creating a digital model of roads) and the practical implementation of software for compiling local maps are presented. The developed software took into account the shortcomings of specialized products on the market, and also implemented the possibility of converting a digital model of roads into the MAPEM format. After preprocessing MAPEM files, they are placed on the server of the intelligent transport system, from where they are then sent via RSU. RSUs start broadcasting this information, and it goes to the unmanned vehicle, which, in turn, processes the received file and waits for a request to move. Upon receipt of such a request, it builds the trajectory of the path and starts moving. The techniques obtained as a result of the research were implemented and applied at the test site.


Vehicles ◽  
2021 ◽  
Vol 3 (4) ◽  
pp. 764-777
Author(s):  
Dario Niermann ◽  
Alexander Trende ◽  
Klas Ihme ◽  
Uwe Drewitz ◽  
Cornelia Hollander ◽  
...  

The quickly rising development of autonomous vehicle technology and increase of (semi-) autonomous vehicles on the road leads to an increased demand for more sophisticated human–machine-cooperation approaches to improve trust and acceptance of these new systems. In this work, we investigate the feeling of discomfort of human passengers while driving autonomously and the automatic detection of this discomfort with several model approaches, using the combination of different data sources. Based on a driving simulator study, we analyzed the discomfort reports of 50 participants for autonomous inner city driving. We found that perceived discomfort depends on the driving scenario (with discomfort generally peaking in complex situations) and on the passenger (resulting in interindividual differences in reported discomfort extend and duration). Further, we describe three different model approaches on how to predict the passenger discomfort using data from the vehicle’s sensors as well as physiological and behavioral data from the passenger. The model’s precision varies greatly across the approaches, the best approach having a precision of up to 80%. All of our presented model approaches use combinations of linear models and are thus fast, transparent, and safe. Lastly, we analyzed these models using the SHAP method, which enables explaining the models’ discomfort predictions. These explanations are used to infer the importance of our collected features and to create a scenario-based discomfort analysis. Our work demonstrates a novel approach on passenger state modelling with simple, safe, and transparent models and with explainable model predictions, which can be used to adapt the vehicles’ actions to the needs of the passenger.


Day by day as the volume of data is being generated massively, storing of data and processing of data becomes a ever growing challenge in intelligent transport system (ITS). In intelligent transport system there are different areas to concentrate like smart parking systems, dynamic toll charging, smart traffic management etc. This paper is mainly focused on big data architecture for intelligent transport system for dynamic toll charging, traffic management and traffic analysis related data collection from various sources. The data collected from various sources can be in the form of structured data, semi structured data and unstructured data. Because of verity of data collected, this paper gives an idea about which data model is appropriate depending on data collected for transportation system.


In present years, there is a rapid increase in number of vehicles flying on the road. The focus is to improve the road safety and navigations standards with the help of an intelligent transport system. There is a need for novel applications and services in the vehicular environment for security and comfort. Technical advancement has been developed to predict road accidents, to prevent collisions, to understanding road conditions, to access uninterrupted internet facilities to expand the transmission range, to extend the storage capacity and to avoid the interference of wireless links. The idea of this paper work is to develop an intelligent transport model to enhance the road safety and navigation process. There are many approaches in the n communication. Effective and efficient techniques are developed to detect the road conditions with the help of vehicular communication. This paper gives the background of intelligent vehicle transport system. Various literature on VANET modelling, clustering and its based algorithms are discussed. Followed by a review of hybrid cluster algorithms , MAC protocols for VANET road safety applications, multi-hop and multi-level broadcast protocol are discussed.


Sign in / Sign up

Export Citation Format

Share Document