An Experiment Analysis on Tracking and Detecting the Vehicle Speed using Machine Learning and IOT

2018 ◽

Vol 7 (4.5) ◽

pp. 654

Author(s):

M. S. Satyanarayana ◽

Aruna T.M ◽

Divyaraj G.N

Keyword(s):

Machine Learning ◽

Low Cost ◽

Machine Learning Techniques ◽

New Technique ◽

Vehicle Speed ◽

Learning Techniques ◽

Traffic Awareness ◽

Alarm Sound ◽

The Government ◽

A New Technique

Accidents have become major issue in Developing countries like India now a day. As per the Surveys 60% of the accidents are happening due to over speed. Though the government has taken so many initiatives like Traffic Awareness & Driving Awareness Week etc.., but still the percentage of accidents are not getting reduced. In this paper a new technique has been introduced to reduce the percentage of accidents. The new technique is implemented using the concept of Machine Learning [1]. The Machine Learning based systems can be implemented in all vehicles to avoid the accidents at low cost [1]. The main objective of this system is to calculate the speed of the vehicle at three various locations based on the place where the vehicle speed must be controlled and if the speed is greater than the designated speed in that road then the vehicle automatically detects the problem and same will be intimated to the driver to control the speed of the vehicle. If the speed is less or equal to the designated speed in that road then the vehicle will be passed without any disturbance. The system will be giving beep sound along with color indication to driver in each and every scenario. The other option implemented in this system is if the driver is driving the vehicle in the night and if he feel drowsy the system detects it immediately and alarm sound will be initiated to wake up the driver. This system though it won’t avoid 100% accidents at least it will reduce the percentage of accidents. This system is not only to avoid accidents it will also intelligently control the speed of the vehicles and creates awareness amongst the drivers.

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Implementation of modified SARSA learning technique in EMCAP

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i1.5.9161 ◽

2017 ◽

Vol 7 (1.5) ◽

pp. 274

Author(s):

D. Ganesha ◽

Vijayakumar Maragal Venkatamuni

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Decision Process ◽

Learning Algorithm ◽

Research Work ◽

Learning System ◽

State Action ◽

Learning Technique ◽

Markov Decision ◽

Experiment Analysis

This research work presents analysis of Modified Sarsa learning algorithm. Modified Sarsa algorithm. State-Action-Reward-State-Action (SARSA) is an technique for learning a Markov decision process (MDP) strategy, used in for reinforcement learning int the field of artificial intelligence (AI) and machine learning (ML). The Modified SARSA Algorithm makes better actions to get better rewards. Experiment are conducted to evaluate the performace for each agent individually. For result comparison among different agent, the same statistics were collected. This work considered varied kind of agents in different level of architecture for experiment analysis. The Fungus world testbed has been considered for experiment which is has been implemented using SwI-Prolog 5.4.6. The fixed obstructs tend to be more versatile, to make a location that is specific to Fungus world testbed environment. The various parameters are introduced in an environment to test a agent’s performance. This modified SARSA learning algorithm can be more suitable in EMCAP architecture. The experiments are conducted the modified SARSA Learning system gets more rewards compare to existing SARSA algorithm.

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Experiment analysis and computational optimization of the Atkinson cycle gasoline engine through NSGA Ⅱ algorithm using machine learning

Energy Conversion and Management ◽

10.1016/j.enconman.2021.113871 ◽

2021 ◽

Vol 238 ◽

pp. 113871

Author(s):

Ji Tong ◽

Yangyang Li ◽

Jingping Liu ◽

Ran Cheng ◽

Jinhuan Guan ◽

...

Keyword(s):

Machine Learning ◽

Gasoline Engine ◽

Computational Optimization ◽

Atkinson Cycle ◽

Experiment Analysis

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Intelligent traffic control system based on open IoT and machine learning

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189531 ◽

2020 ◽

pp. 1-12

Author(s):

Jiaona Chen ◽

Hailong Liu

Keyword(s):

Machine Learning ◽

Control System ◽

Traffic Control ◽

Road Traffic ◽

Control Process ◽

Vehicle Speed ◽

Smart Transportation ◽

The Road ◽

Traffic Control System ◽

On The Road

Smart transportation relies on data collection, transmission, processing, and release, involving various terminal devices, control systems, central platforms, and communication links, so its control process is more complicated. In order to improve the operation efficiency of the intelligent traffic control system, based on the open Internet of Things and machine learning, this paper builds an intelligent three-way intelligent traffic control system, sets various parameters, and builds a simulation model using cellular automata as a platform. Moreover, in order to study the performance of the model, the model constructed in this paper is compared with the model of the traditional road traffic control system. In addition, this paper analyzes the model constructed in this paper through the statistics of the highest vehicle flow on the road and the relationship between road occupancy and vehicle speed. The research results show that the model constructed in this paper has good performance and can be applied to intelligent traffic control.

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Machine Learning Techniques for Undertaking Roundabouts in Autonomous Driving

Sensors ◽

10.3390/s19102386 ◽

2019 ◽

Vol 19 (10) ◽

pp. 2386 ◽

Cited By ~ 6

Author(s):

Laura García Cuenca ◽

Javier Sanchez-Soriano ◽

Enrique Puertas ◽

Javier Fernandez Andrés ◽

Nourdine Aliane

Keyword(s):

Machine Learning ◽

Predictive Model ◽

Video Processing ◽

Autonomous Vehicles ◽

Autonomous Driving ◽

Machine Learning Techniques ◽

Support Vector ◽

Vehicle Speed ◽

Aggregated Data ◽

Supervised Learning Algorithms

This article presents a machine learning-based technique to build a predictive model and generate rules of action to allow autonomous vehicles to perform roundabout maneuvers. The approach consists of building a predictive model of vehicle speeds and steering angles based on collected data related to driver–vehicle interactions and other aggregated data intrinsic to the traffic environment, such as roundabout geometry and the number of lanes obtained from Open-Street-Maps and offline video processing. The study systematically generates rules of action regarding the vehicle speed and steering angle required for autonomous vehicles to achieve complete roundabout maneuvers. Supervised learning algorithms like the support vector machine, linear regression, and deep learning are used to form the predictive models.

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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