Vehicle Navigation System based on Pollution Metric Analysis with Q-Learning Algorithm

The navigation systems available in the present scenario takes into account the path distance for their estimations. In some advanced navigation systems, the road traffic analysis is also considered in the algorithm for their predictions. The proposed work estimates a navigation path with respect to the present pollution level on the roadways. The work suggests an alternate path to avoid additional vehicles to enter the same road which is already impacted by air pollution. A Q-learning (Quality learning) prediction algorithm is trained in the proposed work with a self-made dataset for the estimations. The experimental work presented in the paper explores the accuracy and computational speed of the developed algorithm in comparison to the traditional algorithms.

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Congestion Control in Charging Stations Allocation with Q-Learning

Sustainability ◽

10.3390/su11143900 ◽

2019 ◽

Vol 11 (14) ◽

pp. 3900

Author(s):

Zhang ◽

Gong ◽

Keyword(s):

Travel Time ◽

Learning Algorithm ◽

Navigation Systems ◽

Routing Problem ◽

Q Learning ◽

The Road ◽

Adaptive Allocation ◽

Charging Time ◽

Convergence Performance ◽

Charging Stations

Navigation systems can help in allocating public charging stations to electric vehicles (EVs) with the aim of minimizing EVs’ charging time by integrating sufficient data. However, the existing systems only consider their travel time and transform the allocation as a routing problem. In this paper, we involve the queuing time in stations as one part of EVs’ charging time, and another part is the travel time on roads. Roads and stations are easily congested resources, and we constructed a joint-resource congestion game to describe the interaction between vehicles and resources. With a finite number of vehicles and resources, there exists a Nash equilibrium. To realize a self-adaptive allocation work, we applied the Q-learning algorithm on systems, defining sets of states and actions in our constructed environment. After being allocated one by one, vehicles concurrently requesting to be charged will be processed properly. We collected urban road network data from Chongqing city and conducted experiments. The results illustrate the proposed method can be used to solve the problem, and its convergence performance was better than the genetic algorithm. The road capacity and the number of EVs affected the initial of Q-value, and not the convergence trends.

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Autonomous Path Planning Scheme Research for Mobile Robot

Cybernetics and Information Technologies ◽

10.1515/cait-2016-0072 ◽

2016 ◽

Vol 16 (4) ◽

pp. 113-125

Author(s):

Jianxian Cai ◽

Xiaogang Ruan ◽

Pengxuan Li

Keyword(s):

Path Planning ◽

Mobile Robot ◽

Autonomous Navigation ◽

Learning Algorithm ◽

Action Learning ◽

Cognitive Learning ◽

Learning Ability ◽

Q Learning ◽

Planning Strategy ◽

Navigation Path

Abstract An autonomous path-planning strategy based on Skinner operant conditioning principle and reinforcement learning principle is developed in this paper. The core strategies are the use of tendency cell and cognitive learning cell, which simulate bionic orientation and asymptotic learning ability. Cognitive learning cell is designed on the base of Boltzmann machine and improved Q-Learning algorithm, which executes operant action learning function to approximate the operative part of robot system. The tendency cell adjusts network weights by the use of information entropy to evaluate the function of operate action. The results of the simulation experiment in mobile robot showed that the designed autonomous path-planning strategy lets the robot realize autonomous navigation path planning. The robot learns to select autonomously according to the bionic orientate action and have fast convergence rate and higher adaptability.

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Regional Patch Detection of Road Traffic Network

Journal of Sensors ◽

10.1155/2020/6836091 ◽

2020 ◽

Vol 2020 ◽

pp. 1-6

Author(s):

Xia Zhu ◽

Weidong Song ◽

Lin Gao

Keyword(s):

Complex Network ◽

Road Traffic ◽

Geographic Distance ◽

Influence Factors ◽

Impact Factors ◽

Traffic Network ◽

The Road ◽

Key Points ◽

Path Distance ◽

Shortest Path Distance

Road traffic network (RTN) structure plays an important role in the field of complex network analysis. In this paper, we propose a regional patch detection method from RTN via community detection of complex network. Firstly, the refined Adapted PageRank algorithm, which combines with the influence factors of the location property weight, the geographic distance weight and the road level weight, is used to calculate the candidate ranking results of key nodes in the RTN. Secondly, the ranking result and the shortest path distance as two significant impact factors are used to select the key points of the RTN, and then the Adapted K-Means algorithm is applied to regional patch detection of the RTN. Finally, based on the experimental data of Zhangwu road traffic network, the analysis results are as follows: Zhangwu is divided into 9 functional structures with key node locations as the core. Regional patch structure is divided according to key points, and the RTN is actually divided into nine small functional communities. Nine functional regional patches constitute a new network structure, maintaining connectivity between the regional patches can improve the overall efficiency of the RTN.

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Quadcoptor Monitoring

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit1952158 ◽

2019 ◽

pp. 895-903

Author(s):

M. Ganesan ◽

S. S. Gayathri ◽

S. Krishnakumari ◽

S. Nivedithaa

Keyword(s):

Road Traffic ◽

Traffic Monitoring ◽

Pollution Level ◽

Public And Private ◽

Traffic System ◽

The Road ◽

Open Research ◽

Wide Range ◽

Ip Camera ◽

Extract Information

UAV technology has been an open research topic for many years. This is because of their potential huge benefits at an affordable cost in a wide range of tasks. UAV are commonly used in public and private places, yet also with few serious limitations. In this paper, we are going to design a drone that monitors the road traffic system as well as measures the pollution level emitted by the vehicle into the air. Most of the traffic monitoring systems based on UAV use a fixed trajectory to extract information about the vehicles, but we monitor the vehicle using the IP camera. During peak hours, the level of pollution will be higher so with the help of the drone we can measure the pollution level and take the required precautions.

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Assessment of the Influence of Technology-Based Distracted Driving on Drivers’ Infractions and Their Subsequent Impact on Traffic Accidents Severity

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18137155 ◽

2021 ◽

Vol 18 (13) ◽

pp. 7155

Author(s):

Susana García-Herrero ◽

Juan Diego Febres ◽

Wafa Boulagouas ◽

José Manuel Gutiérrez ◽

Miguel Ángel Mariscal Saldaña

Keyword(s):

Traffic Accidents ◽

Road Traffic ◽

Learning Algorithm ◽

Distracted Driving ◽

Road Traffic Accidents ◽

The Road ◽

Young Drivers ◽

Gender And Age ◽

Serious Injuries ◽

The Impact

Multitasking while driving negatively affects driving performance and threatens people’s lives every day. Moreover, technology-based distractions are among the top driving distractions that are proven to divert the driver’s attention away from the road and compromise their safety. This study employs recent data on road traffic accidents that occurred in Spain and uses a machine-learning algorithm to analyze, in the first place, the influence of technology-based distracted driving on drivers’ infractions considering the gender and age of the drivers and the zone and the type of vehicle. It assesses, in the second place, the impact of drivers’ infractions on the severity of traffic accidents. Findings show that (i) technology-based distractions are likely to increase the probability of committing aberrant infractions and speed infractions; (ii) technology-based distracted young drivers are more likely to speed and commit aberrant infractions; (iii) distracted motorcycles and squad riders are found more likely to speed; (iv) the probability of committing infractions by distracted drivers increases on streets and highways; and, finally, (v) drivers’ infractions lead to serious injuries.

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A Study on the Improvement of Unprotected and U-turn System under the Road Traffic Act in Korea - Comparative review with foreign institutions such as Japan -

Gachon Law Review ◽

10.15335/glr.2019.12.3.003 ◽

2019 ◽

Vol 12 (3) ◽

pp. 75-128

Author(s):

Seungyup Baik ◽

Keyword(s):

Road Traffic ◽

The Road ◽

Comparative Review

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A Groundwork Based Novel Routing Protocol for Vehicular Ad hoc Networks

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i11.474 ◽

2017 ◽

Vol 7 (11) ◽

pp. 121

Author(s):

Amolkirat Singh ◽

Guneet Saini

Keyword(s):

Routing Protocol ◽

Packet Loss ◽

Secure Communication ◽

Vehicular Ad Hoc Networks ◽

Ad Hoc ◽

Road Traffic ◽

Traditional Approach ◽

Unexpected Events ◽

The Road ◽

On The Road

Many people lose their life and/or are injured due to accidents or unexpected events taking place on road networks. Besides traffic jams, these accidents generate a tremendous waste of time and fuel. Undoubtedly, if the vehicles are provided with timely and dynamic information related to road traffic conditions, any unexpected events or accidents, the safety and efficiency of the transportation system with respect to time, distance, fuel consumption and environmentally destructive emissions can be improved. In the field of computer and information science, Vehicular Ad hoc Network (VANET) have recently emerged as an effective tool for improving road safety through propagation of warning messages among the vehicles in the network about potential obstacles on the road ahead. VANET is a research area which is in more demand among the researchers, the automobile industries and scientists to discover about the loopholes and advantages of the vehicular networks so that efficient routing algorithms can be developed which can provide reliable and secure communication among the mobile nodes.In this paper, we propose a Groundwork Based Ad hoc On Demand Distance Vector Routing Protocol (GAODV) focus on how the Road Side Units (RSU’s) utilized in the architecture plays an important role for making the communication reliable. In the interval of finding the suitable path from source to destination the packet loss may occur and the delay also is counted if the required packet does not reach the specified destination on time. So to overcome delay, packet loss and to increase throughput GAODV approach is followed. The performance parameters in the GAODV comes out to be much better than computed in the traditional approach.

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