Driving Style Recognition Model Based on NEV High-Frequency Big Data and Joint Distribution Feature Parameters

Xia, Lina; Kang, Zejun

doi:10.3390/wevj12030142

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…pedal use, as described in [40][41][42], but how to translate this data into an autonomous driver model is still an open issue.…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…However, it is still challenging to understand each model parameter's influence on the resultant driving style to make it as suitable as possible for others. The classification and recognition of driving style are usually obtained by using statistical and machine learning algorithms based on vehicular signals such as speed, acceleration, trajectory, and gas pedal use, as described in [40][41][42], but how to translate this data into an autonomous driver model is still an open issue.…”

Section: Introductionmentioning

confidence: 99%

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From Human to Autonomous Driving: A Method to Identify and Draw Up the Driving Behaviour of Connected Autonomous Vehicles

2022

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The driving behaviour of Connected and Automated Vehicles (CAVs) may influence the final acceptance of this technology. Developing a driving style suitable for most people implies the evaluation of alternatives that must be validated. Intelligent Virtual Drivers (IVDs), whose behaviour is controlled by a program, can test different driving styles along a specific route. However, multiple combinations of IVD settings may lead to similar outcomes due to their high variability. The paper proposes a method to identify the IVD settings that can be used as a reference for a given route. The method is based on the cluster analysis of vehicular data produced by a group of IVDs with different settings driving along a virtual road scenario. Vehicular data are clustered to find IVDs representing a driving style to classify human drivers who previously drove on the same route with a driving simulator. The classification is based on the distances between the different vehicular signals calculated for the IVD and recorded for human drivers. The paper includes a case study showing the practical use of the method applied on an actual road circuit. The case study demonstrated that the proposed method allowed identifying three IVDs, among 29 simulated, which have been subsequently used as a reference to cluster 26 human driving styles. These representative IVDs, which ideally replicate the driving style of human drivers, can be used to support the development of CAVs control logic that better fits human expectations. A closing discussion about the flexibility of the method in terms of the different natures of data collection, allowed for depicting future applications and perspectives.

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“…pedal use, as described in [40][41][42], but how to translate this data into an autonomous driver model is still an open issue.…”

Section: The Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

From Human to Autonomous Driving: A Method to Identify and Draw Up the Driving Behaviour of Connected Autonomous Vehicles

2022

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Application of Multi-Criteria Decision Making Methods for Evaluation of Selected Passenger Electric Cars: A Case Study

Stopka¹,

Stopková²,

Pecman³

2022

Komunikácie

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The article deals with the analysis of selected technical and economic aspects that influence decision making process when choosing a car with an electric drive -an electric car. Environmental friendliness is beginning to be one of the key aspects in the context of electric vehicle selection, but its final choice is still affected by the standards offered by internal combustion vehicles. In our case, the relevant standards were defined when using the results of a questionnaire survey, the criteria of scales, which are used to compare selected types of electric passenger cars in the conditions of the Czech Republic. The aim of the article is to select an appropriate electric car by using specific techniques of multi-criteria decision making -the Basic Variant method and the Analytic Hierarchy Process (AHP). The scientific value of the article lies primarily in its applicability to the global environment and the variability possibilities of criteria set and their significance.

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A Method of Intelligent Driving-Style Recognition Using Natural Driving Data

Zhang,

Zhao

2024

Applied Sciences

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At present, achieving efficient, sustainable, and safe transportation has led to increasing attention on driving behavior recognition and advancements in autonomous driving. Identifying diverse driving styles and corresponding types is crucial for providing targeted training and assistance to drivers, enhancing safety awareness, optimizing driving costs, and improving autonomous driving systems responses. However, current studies mainly focus on specific driving scenarios, such as free driving, car-following, and lane-changing, lacking a comprehensive and systematic framework to identify the diverse driving styles. This study proposes a novel, data-driven approach to driving-style recognition utilizing naturalistic driving data NGSIM. Specifically, the NGSIM dataset is employed to categorize car-following and lane-changing groups according to driving-state extraction conditions. Then, characteristic parameters that fully represent driving styles are optimized through correlation analysis and principal component analysis for dimensionality reduction. The K-means clustering algorithm is applied to categorize the car-following and lane-changing groups into three driving styles: conservative, moderate, and radical. Based on the clustering results, a comprehensive evaluation of the driving styles is conducted. Finally, a comparative evaluation of SVM, Random Forest, and KNN recognition indicates the superiority of the SVM algorithm and highlights the effectiveness of dimensionality reduction in optimizing characteristic parameters. The proposed method achieves over 97% accuracy in identifying car-following and lane-changing behaviors, confirming that the approach based on naturalistic driving data can effectively and intelligently recognize driving styles.

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Driving Style Recognition Model Based on NEV High-Frequency Big Data and Joint Distribution Feature Parameters

Cited by 3 publications

References 22 publications

From Human to Autonomous Driving: A Method to Identify and Draw Up the Driving Behaviour of Connected Autonomous Vehicles

From Human to Autonomous Driving: A Method to Identify and Draw Up the Driving Behaviour of Connected Autonomous Vehicles

Application of Multi-Criteria Decision Making Methods for Evaluation of Selected Passenger Electric Cars: A Case Study

A Method of Intelligent Driving-Style Recognition Using Natural Driving Data

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