scholarly journals A Modified Fault Detection Bayesian Learning Model For Inter Connected Vehicle Networks

2021 ◽  
pp. 182-189
Author(s):  
Syed Umar ◽  
Tadele Debisa Deressa ◽  
Bodena Terfa
Keyword(s):  
Fault Detection ◽  
Communication Systems ◽  
Bayesian Learning ◽  
Connected Vehicles ◽  
Connected Vehicle ◽  
Data Systems ◽  
Wireless Medium ◽  
Worldwide Web ◽  
Vehicle Networks ◽  
Access Information

Currently an important worldwide web, The IoT represents the biggest connected vehicle network of all, but will evolve into a much larger network of connected vehicles. Though a concept promising, the combination of different enabling frequencies does pose various intrinsic and defining challenges in the form of communication systems like privacy and protection. It is also important to establish an effective and dependable strategy to access information for solutions which emerge from increasingly complex vehicle and data systems because of the proliferation of wireless medium. In this article, we provide and improve a new algorithm known as Advanced Fault Detection and Management with Bayesian Network techniques, in which we intend to locate and adjust spatial vehicle faults in real time. Often, we apply measurement method to discover the most effective fault detection methodology, which is the turning point. A sequence of recent studies illustrated findings shows that the suggested approaches include fault detection and correction utilizing tools accessible previously.

scholarly journals Blockchain-Based Solutions for UAV-Assisted Connected Vehicle Networks in Smart Cities: A Review, Open Issues, and Future Perspectives

Telecom ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 108-140
Author(s):  
Paulo Álvares ◽  
Lion Silva ◽  
Naercio Magaia
Keyword(s):  
Traffic Congestion ◽  
Resource Constraints ◽  
Smart Cities ◽  
The Internet ◽  
Connected Vehicle ◽  
Sensitive Data ◽  
Internet Of Vehicles ◽  
Smart Vehicles ◽  
Security Properties ◽  
Vehicle Networks

It had been predicted that by 2020, nearly 26 billion devices would be connected to the Internet, with a big percentage being vehicles. The Internet of Vehicles (IoVa) is a concept that refers to the connection and cooperation of smart vehicles and devices in a network through the generation, transmission, and processing of data that aims at improving traffic congestion, travel time, and comfort, all the while reducing pollution and accidents. However, this transmission of sensitive data (e.g., location) needs to occur with defined security properties to safeguard vehicles and their drivers since attackers could use this data. Blockchain is a fairly recent technology that guarantees trust between nodes through cryptography mechanisms and consensus protocols in distributed, untrustful environments, like IoV networks. Much research has been done in implementing the former in the latter to impressive results, as Blockchain can cover and offer solutions to many IoV problems. However, these implementations have to deal with the challenge of IoV node’s resource constraints since they do not suffice for the computational and energy requirements of traditional Blockchain systems, which is one of the biggest limitations of Blockchain implementations in IoV. Finally, these two technologies can be used to build the foundations for smart cities, enabling new application models and better results for end-users.

Sequential Optimization of an Emergency Response Vehicle’s Intra-Link Movement in a Partially Connected Vehicle Environment

2021 ◽  
pp. 036119812110179
Author(s):  
Gaby Joe Hannoun ◽  
Pamela Murray-Tuite ◽  
Kevin Heaslip ◽  
Thidapat Chantem
Keyword(s):  
Emergency Response ◽  
Lateral Position ◽  
Automated System ◽  
Sequential Optimization ◽  
Connected Vehicles ◽  
Linear Programs ◽  
Connected Vehicle ◽  
Market Penetration ◽  
Short Term ◽  
New Approach

This paper introduces a semi-automated system that facilitates emergency response vehicle (ERV) movement through a transportation link by providing instructions to downstream non-ERVs. The proposed system adapts to information from non-ERVs that are nearby and downstream of the ERV. As the ERV passes stopped non-ERVs, new non-ERVs are considered. The proposed system sequentially executes integer linear programs (ILPs) on transportation link segments with information transferred between optimizations to ensure ERV movement continuity. This paper extends a previously developed mathematical program that was limited to a single short segment. The new approach limits runtime overhead without sacrificing effectiveness and is more suitable to dynamic systems. It also accommodates partial market penetration of connected vehicles using a heuristic reservation approach, making the proposed system beneficial in the short-term future. The proposed system can also assign the ERV to a specific lateral position at the end of the link, a useful capability when next entering an intersection. Experiments were conducted to develop recommendations to reduce computation times without compromising efficiency. When compared with the current practice of moving to the nearest edge, the system reduces ERV travel time an average of 3.26 s per 0.1 mi and decreases vehicle interactions.

scholarly journals Real-Time Safety Optimization of Connected Vehicle Trajectories Using Reinforcement Learning

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3864
Author(s):  
Tarek Ghoul ◽  
Tarek Sayed
Keyword(s):  
Reinforcement Learning ◽  
Real Time ◽  
Low Cost ◽  
Safety Evaluation ◽  
Traffic Volume ◽  
Connected Vehicles ◽  
Connected Vehicle ◽  
Real World Data ◽  
Physical Constraints ◽  
Traffic Conflicts

Speed advisories are used on highways to inform vehicles of upcoming changes in traffic conditions and apply a variable speed limit to reduce traffic conflicts and delays. This study applies a similar concept to intersections with respect to connected vehicles to provide dynamic speed advisories in real-time that guide vehicles towards an optimum speed. Real-time safety evaluation models for signalized intersections that depend on dynamic traffic parameters such as traffic volume and shock wave characteristics were used for this purpose. The proposed algorithm incorporates a rule-based approach alongside a Deep Deterministic Policy Gradient reinforcement learning technique (DDPG) to assign ideal speeds for connected vehicles at intersections and improve safety. The system was tested on two intersections using real-world data and yielded an average reduction in traffic conflicts ranging from 9% to 23%. Further analysis was performed to show that the algorithm yields tangible results even at lower market penetration rates (MPR). The algorithm was tested on the same intersection with different traffic volume conditions as well as on another intersection with different physical constraints and characteristics. The proposed algorithm provides a low-cost approach that is not computationally intensive and works towards optimizing for safety by reducing rear-end traffic conflicts.

scholarly journals Trajectory Planning Method for Mixed Vehicles Considering Traffic Stability and Fuel Consumption at the Signalized Intersection

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Shan Fang ◽  
Lan Yang ◽  
Tianqi Wang ◽  
Shoucai Jing
Keyword(s):  
Fuel Consumption ◽  
Trajectory Planning ◽  
Signalized Intersections ◽  
Planning Method ◽  
Signalized Intersection ◽  
Driver Model ◽  
Connected Vehicles ◽  
Connected Vehicle ◽  
Traffic Lights ◽  
Trigonometric Model

Traffic lights force vehicles to stop frequently at signalized intersections, which leads to excessive fuel consumption, higher emissions, and travel delays. To address these issues, this study develops a trajectory planning method for mixed vehicles at signalized intersections. First, we use the intelligent driver car-following model to analyze the string stability of traffic flow upstream of the intersection. Second, we propose a mixed-vehicle trajectory planning method based on a trigonometric model that considers prefixed traffic signals. The proposed method employs the proportional-integral-derivative (PID) model controller to simulate the trajectory when connected vehicles (equipped with internet access) follow the optimal advisory speed. Essentially, only connected vehicle trajectories need to be controlled because normal vehicles simply follow the connected vehicles according to the Intelligent Driver Model (IDM). The IDM model aims to minimize traffic oscillation and ensure that all vehicles pass the signalized intersection without stopping. The results of a MATLAB simulation indicate that the proposed method can reduce fuel consumption and NOx, HC, CO2, and CO concentrations by 17%, 22.8%, 17.8%, 17%, and 16.9% respectively when the connected vehicle market penetration is 50 percent.

FAULT DETECTION IN MULTIRATE SAMPLED-DATA SYSTEMS WITH TIME-DELAYS

2002 ◽  
Vol 35 (1) ◽  
pp. 311-316 ◽  
Author(s):  
P. Zhang ◽  
S.X. Ding ◽  
G.Z. Wang ◽  
D.H. Zhou
Keyword(s):  
Fault Detection ◽  
Time Delays ◽  
Data Systems ◽  
Sampled Data

Using Baseline Data to Address the Lack of Hospital Beds during Mass-Casualty Incidents

2008 ◽  
Vol 23 (4) ◽  
pp. 377-379 ◽  
Author(s):  
Hysham Hadef ◽  
Jean-Claude Bartier ◽  
Herve Delplancq ◽  
Jean-Pierre Dupeyron
Keyword(s):  
Medical Personnel ◽  
Mass Casualty ◽  
Baseline Data ◽  
Mass Casualty Incidents ◽  
Free Access ◽  
Data Systems ◽  
Hospital Beds ◽  
Access Information ◽  
Data Program ◽  
Information Database

AbstractThe management of victims during mass-casualty incidents (MCIs) is improving. In many countries, physicians and paramedics are well-trained to manage these incidents. A problem that has been encountered during MCIs is the lack of adequate numbers of hospital beds to accommodate the injured. In Europe, hospitals are crowded. One solution for the lack of beds is the creation of baseline data systems that could be consulted by medical personnel in all European countries. A MCI never has occurred in northeastern Europe, but such an event remains a possibility. This paper describes how the use of SAGEC 67, a free-access, information database concerning the availability of beds should help the participating countries, initially France, Germany, and Switzerland, respond to a MCI by dispatching each patient to an appropriate hospital and informing their families and physicians using their own language.Baseline data for more than 20 countries, and for hospitals, especially those in Germany, Switzerland, and France, were collected. Information about the number of beds and their availability hour-by-hour was included. In the case of MCIs, the baseline data program is opened and automatically connects to all of the countries. In case of a necessary hospital evacuation, the required beds immediately are occupied in one of these three countries.Questions and conversations among medical staff or family members can be accomplished between hospitals through computer, secured-line chatting that automatically translates into appropriate language.During the patient evacuation phase of a MCI, respondents acknowledged that a combination of local, state, and private resources and international cooperation eventually would be needed to meet the demand. Patient evacuation is optimized through the use of SAGEC 67, a free baseline database.

scholarly journals Microscopic Estimation of Freeway Vehicle Positions from the Behavior of Connected Vehicles

2020 ◽  
Author(s):  
Noah J. Goodall ◽  
Brian L. Smith ◽  
Byungkyu Brian Park
Keyword(s):  
Traffic Signals ◽  
The United States ◽  
Traffic Information ◽  
Connected Vehicles ◽  
Connected Vehicle ◽  
Travel Time Prediction ◽  
High Penetration ◽  
Initial Penetration ◽  
Car Following Model ◽  
Status Data

Given the current connected vehicles program in the United States, as well as other similar initiatives in vehicular networking, it is highly likely that vehicles will soon wirelessly transmit status data, such as speed and position, to nearby vehicles and infrastructure. This will drastically impact the way traffic is managed, allowing for more responsive traffic signals, better traffic information, and more accurate travel time prediction. Research suggests that to begin experiencing these benefits, at least 20% of vehicles must communicate, with benefits increasing with higher penetration rates. Because of bandwidth limitations and a possible slow deployment of the technology, only a portion of vehicles on the roadway will participate initially. Fortunately, the behavior of these communicating vehicles may be used to estimate the locations of nearby noncommunicating vehicles, thereby artificially augmenting the penetration rate and producing greater benefits. We propose an algorithm to predict the locations of individual noncommunicating vehicles based on the behaviors of nearby communicating vehicles by comparing a communicating vehicle's acceleration with its expected acceleration as predicted by a car-following model. Based on analysis from field data, the algorithm is able to predict the locations of 30% of vehicles with 9-m accuracy in the same lane, with only 10% of vehicles communicating. Similar improvements were found at other initial penetration rates of less than 80%. Because the algorithm relies on vehicle interactions, estimates were accurate only during or downstream of congestion. The proposed algorithm was merged with an existing ramp metering algorithm and was able to significantly improve its performance at low connected vehicle penetration rates and maintain performance at high penetration rates.

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