Prototype of Driving Behavior Monitoring System Using Naïve Bayes Classification Method

Pramunanto, Eko; Zaini, Ahmad; Rizkiana, Vathya

doi:10.1109/cenim48368.2019.8973232

Cited by 2 publications

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To validate the effectiveness of the proposed classification method, a comparison was made with two different approaches: a probabilistic classification method based on Naive Bayes, such as the one proposed in [ 26 ], and a Random Forest classifier. The classification was performed using the 10-fold cross-validation method with a dataset composed of 300 samples collected from the traffic simulator.…”

Section: Methodsmentioning

confidence: 99%

“…However, it requires detailed prior knowledge for the different parameters to be classified. The work in [ 26 ] proposes the use of a Naive Bayes classification method for monitoring driving behavior. Using accelerometer sensor data, the system can detect events such as turns, accelerations, and decelerations and then use the Naïve Bayes method to classify the driver behavior into three categories: normal, defensive, and aggressive.…”

Section: State-of-the-art On Driver Behavior Feature Identificationmentioning

confidence: 99%

See 1 more Smart Citation

Fuzzy Ontology-Based System for Driver Behavior Classification

Fernández

Ito

Cruz-Piris

et al. 2022

Sensors

View full text Add to dashboard Cite

Intelligent transportation systems encompass a series of technologies and applications that exchange information to improve road traffic and avoid accidents. According to statistics, some studies argue that human mistakes cause most road accidents worldwide. For this reason, it is essential to model driver behavior to improve road safety. This paper presents a Fuzzy Rule-Based System for driver classification into different profiles considering their behavior. The system’s knowledge base includes an ontology and a set of driving rules. The ontology models the main entities related to driver behavior and their relationships with the traffic environment. The driving rules help the inference system to make decisions in different situations according to traffic regulations. The classification system has been integrated on an intelligent transportation architecture. Considering the user’s driving style, the driving assistance system sends them recommendations, such as adjusting speed or choosing alternative routes, allowing them to prevent or mitigate negative transportation events, such as road crashes or traffic jams. We carry out a set of experiments in order to test the expressiveness of the ontology along with the effectiveness of the overall classification system in different simulated traffic situations. The results of the experiments show that the ontology is expressive enough to model the knowledge of the proposed traffic scenarios, with an F1 score of 0.9. In addition, the system allows proper classification of the drivers’ behavior, with an F1 score of 0.84, outperforming Random Forest and Naive Bayes classifiers. In the simulation experiments, we observe that most of the drivers who are recommended an alternative route experience an average time gain of 66.4%, showing the utility of the proposal.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: State-of-the-art On Driver Behavior Feature Identificationmentioning

confidence: 99%

Fuzzy Ontology-Based System for Driver Behavior Classification

Fernández

Ito

Cruz-Piris

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

Prediction of electric power performance of the exhaust waste heat recovery system of an automobile with thermoelectrical generator under real driving conditions by means of machine learning algorithms

Çelik,

Kunt,

Güneş

2023

Proceedings of the Institution of Mechanical Engineers, Part E:

View full text Add to dashboard Cite

In internal combustion engines, approximately 40% of the thermal power obtained from fuel burning is thrown out to the environment from the exhaust system. Implementations of waste heat recovery systems with thermoelectrical generator over exhaust system have become widespread as it is the waste heat resource with the highest temperature in a vehicle. During literature research, experiments related to waste heat recovery under real road conditions are very few and no study on estimation of system performance by machine learning algorithms has been found; therefore, Toyota Corolla has designed an air-cooled waste heat recovery system using 3 thermoelectrical generators for the exhaust system of the automobile. During the road tests, temperature and electric power generation values obtained as per gear, vehicle speed and engine speed have been recorded. In the study, a dataset consisting of 10 attributes was created with each record as a result of the path test. Using this dataset, Random Forest, Support Vector Machine (SVM) and Naive Bayes machine learning algorithms estimated the electrical power to be generated from the thermoelectric generator recovery system. In the study, 7 electric power classification estimates were made. In the estimation process, 76% of the dataset was used for training and 24% was used for testing. In terms of estimation success; an estimation success of 96.6% has been achieved by Random Forest method; 94.6% by Support Vector Machine (SVM) method; and 76.7% by Naive Bayes method. The results show that prospective electricity generation estimation can be achieved with high level of accuracy.

show abstract

Prototype of Driving Behavior Monitoring System Using Naïve Bayes Classification Method

Cited by 2 publications

References 5 publications

Fuzzy Ontology-Based System for Driver Behavior Classification

Fuzzy Ontology-Based System for Driver Behavior Classification

Prediction of electric power performance of the exhaust waste heat recovery system of an automobile with thermoelectrical generator under real driving conditions by means of machine learning algorithms

Contact Info

Product

Resources

About