Intelligent, In-Vehicle Autonomous Decision-Making Functionality for Driving Style Reconfigurations

Panagiotopoulos, Ilias; Dimitrakopoulos, George

doi:10.3390/electronics12061370

Cited by 2 publications

(1 citation statement)

References 40 publications

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“…ML algorithms are fundamental in processing and interpreting the barrage of sensor data that autonomous vehicles generate -a methodological use. However, the same ML systems are directly responsible for the decision-making process of the vehicle, such as navigation and obstacle avoidance, thereby acting as a solution to the complex problem of autonomous control and safety [9]. Furthermore, in the domain of natural language processing (NLP), ML demonstrates its dual role.…”

Section: Blurring Lines Between Methods and Solutionmentioning

confidence: 99%

Machine Learning - Method or Solution?

Tymoteusz

2023

Scientific Practice: Modern and Classical Research Methods

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This article explores the dualistic nature of machine learning (ML) in scientific research, dissecting its role as both a methodological tool and a direct solution to complex problems. It begins by defining ML and its foundational principles, algorithms, and statistical models. The versatility of ML is highlighted through its applications across various disciplines, such as biology, astrophysics, healthcare, environmental science, and business. The article then delves into the challenges and limitations of ML, including data quality issues, ethical concerns, and the risk of over-reliance on automated decisions. It advocates for the integration of ML with classical research methods to ensure robust and ethical scientific practice. Concluding with future perspectives, the article speculates on the evolving role of ML in advancing both methodology and problem-solving in diverse fields, emphasizing the importance of responsible and effective utilization of ML in scientific research.

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Section: Blurring Lines Between Methods and Solutionmentioning

confidence: 99%

Machine Learning - Method or Solution?

Tymoteusz

2023

Scientific Practice: Modern and Classical Research Methods

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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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Intelligent, In-Vehicle Autonomous Decision-Making Functionality for Driving Style Reconfigurations

Cited by 2 publications

References 40 publications

Machine Learning - Method or Solution?

Machine Learning - Method or Solution?

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

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