scholarly journals A Route Reservation Approach for an Autonomous Vehicles Routing Problem

2018 ◽  
Vol 220 ◽  
pp. 02004 ◽  
Author(s):  
Anton Agafonov ◽  
Aleksandr Borodinov
Keyword(s):  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
Intelligent Transportation System ◽  
Road Traffic ◽  
Autonomous Vehicle ◽  
Routing Problem ◽  
Microscopic Traffic Simulation ◽  
The Road ◽  
On The Road ◽  
Road Segments

Autonomous vehicle development is one of many trends that will affect future transport demands and planning needs. Autonomous vehicles management in the context of an intelligent transportation system could significantly reduce the traffic congestion level and decrease the overall travel time in a network. In this work, we investigate a route reservation architecture to manage road traffic within an urban area. The routing architecture decomposes road segments into time and spatial slots and for every vehicle, it makes the reservation of the appropriate slots on the road segments in the selected route. This approach allows to predict the traffic in the network and to find the shortest path more precisely. We propose to use a rerouting procedure to improve the quality of the routing approach. Experimental study of the routing architecture is conducted using microscopic traffic simulation in SUMO package.

scholarly journals Comparative Analysis of V2V and A2A Technologies

2018 ◽  
Vol 45 (1) ◽  
pp. 345-364 ◽  
Author(s):  
Agata Kołodziejska ◽  
Karolina Krzykowska ◽  
Mirosław Siergiejczyk
Keyword(s):  
Autonomous Vehicles ◽  
Road Traffic ◽  
Autonomous Vehicle ◽  
Legal Aspects ◽  
Transport Systems ◽  
Human Beings ◽  
Driver Assistance Systems ◽  
The Road ◽  
Safety And Reliability ◽  
On The Road

Abstract In recent years, around the world, there has been work underway on systems, which will increase not only the comfort of traveling but, above all, the safety and reliability of the road traffic. The systems in this field, designed to replace human beings in the future, thus eliminating their mistakes on the road, already have their prototypes. However, these prototypes are still being improved and require a lot of work so they could operate fully and reliably. The subject of the publication is a compilation of two new concepts in the field of Intelligent Transport Systems. These concepts are V2V (Vehicle - to - Vehicle) and A2A (Autonomous vehicle - to - Autonomous vehicle). Their comparison was carried out in terms of functionality, communication, vehicle equipment, legal aspects and the anticipated date of their entry into the market. Also examples of first tests and implementations of vehicles with driver assistance systems, and semi-autonomous vehicles were presented.

scholarly journals Novel Routing Algorithm for Autonomous Vehicles in Smart Transportation System

2021 ◽  
Vol 3 (3) ◽  
pp. 164-179
Author(s):  
Haoxiang Wang
Keyword(s):  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
Intelligent Transportation System ◽  
Autonomous Vehicle ◽  
Routing Algorithm ◽  
Transportation System ◽  
Traffic Condition ◽  
Smart Transportation ◽  
Centralized Management ◽  
The City

In recent times Automation is emerging every day and bloomed in every sector. Intelligent Transportation System (ITS) is one of the important branches of Automation. The major constrain in the transportation system is traffic congestion. This slurps the individual’s time and consequently pollutes the environment. A centralized management is required for optimizing the transportation system. The current traffic condition is predicted by evaluating the historical data and thereby it reduces the traffic congestion. The periodic update of traffic condition in each and every street of the city is obtained and the data is transferred to the autonomous vehicle. These data are obtained from the simulation results of transportation prediction tool SUMO. It is proved that our proposed work reduces the traffic congestion and maintains ease traffic flow and preserves the fleet management.

scholarly journals INTELLIGENT CAR PARKING SYSTEM

2014 ◽  
pp. 261-267
Author(s):  
S. AVINASH ◽  
SNEHA MITTRA ◽  
SUDIPTA NAYAN GOGOI ◽  
C. SURESH
Keyword(s):  
Traffic Congestion ◽  
Road Traffic ◽  
Empty Space ◽  
The West ◽  
The Road ◽  
Design And Implementation ◽  
Dc Motors ◽  
Parking System ◽  
Machine Interface ◽  
On The Road

Due to the proliferation in the number of vehicles on the road, traffic problems are bound to exist. This is due to the fact that the current transportation infrastructure and car parking facility developed are unable to cope with the influx of vehicles on the road. In India, the situation are made worse by the fact that the roads are significantly narrower compared to the west. Therefore problems such as traffic congestion and insufficient parking space inevitably crops up. In his paper we describe an Intelligent Car Parking System, which identifies the available spaces for parking using sensors, parks the cars in an identified empty space and gets the car back from its parked space without the help of any human personnel. A Human Machine Interface (HMI) helps in entering a unique identification number while entry of any car which helps in searching for the space where the car is parked while exit. An Indraconrol L10 PLC controls the actions of the parking system. The PLC is used to sequence the placing and fetching of the car via DC motors. We have implemented a prototype of the system. The system evaluation demonstrates the effectiveness of our design and implementation of car parking system.

scholarly journals Real-time predication and navigation on traffic congestion model with equilibrium Markov chain

2018 ◽  
Vol 14 (4) ◽  
pp. 155014771876978 ◽  
Author(s):  
Yan Zheng ◽  
Yanran Li ◽  
Chung-Ming Own ◽  
Zhaopeng Meng ◽  
Mengya Gao
Keyword(s):  
Markov Chain ◽  
Real Time ◽  
Traffic Congestion ◽  
Field Experiments ◽  
Road Traffic ◽  
Markov Chain Model ◽  
Chain Model ◽  
The Road ◽  
Shortest Path Algorithms ◽  
On The Road

With the explosive growth of vehicles on the road, traffic congestion has become an inevitable problem when applying guidance algorithms to transportation networks in a busy and crowded city. In our study, the authors proposed an advanced prediction and navigation models on a dynamic traffic network. In contrast to the traditional shortest path algorithms, focused on the static network, the first part of our guiding method considered the potential traffic jams and was developed to provide the optimal driving advice for the distinct periods of a day. Accordingly, by dividing the real-time Global Positioning System data of taxis in Shenzhen city into 50 regions, the equilibrium Markov chain model was designed for dispatching vehicles and applied to ease the city congestion. With the reveals of our field experiments, the traffic congestion of city traffic networks can be alleviated effectively and efficiently, the system performance also can be retained.

Pedestrians’ understanding of fully autonomous vehicles (FAV) intent to stop

2020 ◽  
Vol 64 (1) ◽  
pp. 1931-1932
Author(s):  
Michal Hochman ◽  
Tal Oron-Gilad
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Background Color ◽  
Urban Road ◽  
The Road ◽  
Message Type ◽  
On The Road ◽  
Future Work ◽  
Road Crossing ◽  
Distance Conditions

This study explored pedestrians’ understanding of Fully Autonomous Vehicle (FAV) intention and what influences their decision to cross. Twenty participants saw fixed simulated urban road crossing scenes with a FAV present on the road. The scenes differed from one another in the FAV’s messages: the external Human-Machine Interfaces (e-HMI) background color, message type and modality, the FAV’s distance from the crossing place, and its size. Eye-tracking data and objective measurements were collected. Results revealed that pedestrians looked at the e-HMI before making their decision; however, they did not always make the decision according to the e-HMIs’ color, instructions (in advice messages), or intention (in status messages). Moreover, when they acted according to the e-HMI proposition, for certain distance conditions, they tended to hesitate before making the decision. Findings suggest that pedestrians’ decision making to cross depends on a combination of the e-HMI implementation and the car distance. Future work should explore the robustness of the findings in dynamic and more complex crossing environments.

scholarly journals An Integrated Model for User State Detection of Subjective Discomfort in Autonomous Vehicles

Vehicles ◽  
2021 ◽  
Vol 3 (4) ◽  
pp. 764-777
Author(s):  
Dario Niermann ◽  
Alexander Trende ◽  
Klas Ihme ◽  
Uwe Drewitz ◽  
Cornelia Hollander ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Linear Models ◽  
Driving Simulator ◽  
Autonomous Vehicle ◽  
The Road ◽  
Novel Approach ◽  
Driving Scenario ◽  
On The Road ◽  
Using Data ◽  
Subjective Discomfort

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.

scholarly journals A Framework to Reduce Road Congestion by means of Data Analytics

2021 ◽  
Vol 4 (1) ◽  
pp. 287-297
Author(s):  
Anosha Arooj Yousaf ◽  
Najia Saher ◽  
Faisal Shahzad ◽  
Sara Fareed
Keyword(s):  
Traffic Congestion ◽  
Urban Areas ◽  
Data Analytics ◽  
Road Traffic ◽  
Image Data ◽  
The Road ◽  
Road Planning ◽  
On The Road ◽  
Critical Traffic ◽  
Processing Techniques

The density of vehicles on the road especially in urban areas keeps on increasing to large amount day by day. Especially during the peak hours of the day, large amount of people wastes much of their time in traffic signals. Not only they waste energy by burning excess fuel and releasing CO2 emissions in the environment as well as their time and money. An idea has been proposed to monitor the traffic congestion by means of data analytics on image data and solve the critical traffic congestion issue. The CCTV or surveillance cameras installed at the top points on the roads acts as a medium to provide image data as an input to analyze road traffic congestion by counting the number of vehicles under specified interval of time. Monitoring of traffic congestion using image processing techniques is very useful for the future urban road planning such as: 1) if there is a need to make the road wider, 2) if there is a need to add more lanes on the road, 3) if there is need to make flyover or a bridge to control the traffic on the roads. It will help municipalities to structure and expansion of the roads.

A SYSTEM FOR DETECTING AND RECOGNIZING MOVING BIOLOGICAL OBJECTS FOR UNMANNED VEHICLES BASED ON INTELLIGENT EDGE COMPUTING

2021 ◽  
Author(s):  
М.А. АЛЬ-СВЕЙТИ ◽  
А.С. МУТХАННА ◽  
А.С. БОРОДИН ◽  
А.Е. КУЧЕРЯВЫЙ
Keyword(s):  
Autonomous Vehicles ◽  
Intelligent Transportation System ◽  
Autonomous Vehicle ◽  
Unmanned Vehicles ◽  
Mesh Network ◽  
New Approach ◽  
Remote Areas ◽  
The Road ◽  
Biological Objects ◽  
Time Critical

Обсуждается возможность применения бортовых платформ с целью поддержки наземных сетей для использования ресурсов автономных транспортных средств как части критичных к задержкам приложений. Бортовые платформы могут повысить безопасность поездок транспортных средств, доставляя на них своевременную информацию об окружающей обстановке даже в отдаленных районах земного шара. Обсуждаются требования и потенциальные решения для поддержки инфраструктуры автономных транспортных средств как части интеллектуальной транспортной системы. Предлагается использовать вдоль дороги энергоэффективные сенсоры, которые могут объединяться друг с другом в Mesh-сети. Кроме того, предлагается новый подход к обнаружению активности биологических объектов на обочине дороги, основанный на технологиях искусственного интеллекта. The article discusses the possibility of using onboard platforms to support the terrestrial networks for autonomous vehicles resources as a part of delay-critical applications. Onboard platforms can improve the safety of vehicle rides by delivering time-critical information about the environment to the vehicles, even in remote areas of the world. In this paper, we discuss requirements and potential solutions for supporting the autonomous vehicle infrastructure, as a part of an intelligent transportation system. It is proposed to use energy-efficient sensors along the road, which can connect with each other in a Mesh network. In addition, a new approach for the detection of biological objects activity on the roadside, based on artificial intelligence technologies is suggested.

Video Stream Mining for On-Road Traffic Density Analytics

2012 ◽  
pp. 182-194
Author(s):  
Rudra Narayan Hota ◽  
Kishore Jonna ◽  
P. Radha Krishna
Keyword(s):  
Traffic Congestion ◽  
Road Traffic ◽  
Video Stream ◽  
Traffic Density ◽  
Svm Classifier ◽  
Stream Mining ◽  
The Road ◽  
Video Frames ◽  
Traffic Congestion Control ◽  
On The Road

Traffic congestion problem is rising day-by-day due to increasing number of small to heavy weight vehicles on the road, poorly designed infrastructure, and ineffective control systems. This chapter addresses the problem of estimating computer vision based traffic density using video stream mining. We present an efficient approach for traffic density estimation using texture analysis along with Support Vector Machine (SVM) classifier, and describe analyzing traffic density for on-road traffic congestion control with better flow management. This approach facilitates integrated environment for users to derive traffic status by mining the available video streams from multiple cameras. It also facilitates processing video frames received from video cameras installed in traffic posts and classifies the frames according to traffic content at any particular instance. Time series information available from various input streams is combined with traffic video classification results to discover traffic trends.

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