Identifying drunk driving behavior through a support vector machine model based on particle swarm algorithm

Li, Min; Wang, Wuhong; Ranjitkar, Prakash; Chen, Tao

doi:10.1177/1687814017704154

Cited by 1 publication

(1 citation statement)

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“…2022). There are some studies that have listed the advantages of SVM for the identification of behaviors as described by Li et al (2017) and Wang et al (2019), stating its ability to develop high accuracy values and to handle high-dimensional feature space. Although there are limited studies that have reviewed and compared the three algorithms, many of these studies only summarize other driving behaviors, such as driving styles as by Martinez et al (2018).…”

Section: Introductionmentioning

confidence: 99%

A review on machine learning-based models for lane-changing behavior prediction and recognition

David

Söffker

2023

Front. Future Transp.

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A major aspect in the development of advanced driving assistance systems (ADASs) is the research in developing human driving behavior prediction and recognition models. Recent contributions focus on developing these models for estimating different driving behaviors like lane or speed change. Thus, the models are incorporated into the ADAS to generate warnings and hints for safe maneuvers. Driving behavior recognition and prediction models are generally developed based on machine learning (ML) algorithms and are proven to generate accurate estimations. Previous review research contributions tend to focus on ML-based models for the prediction and recognition of speed change, trajectory change, and even driving styles. Due to high number of driving errors occurring during a lane change, a state-of-art review of different ML-based models for lane-changing behavior prediction and recognition is helpful to present a comparison between different models in terms of structure, influencing input variables, and performance. This enables the integration of the most efficient model for the development of ADASs to avoid accidents during a lane change. First, definitions and terms related to the model’s task and evaluation metrics used to evaluate the model’s performance are described to improve the readability. Then, the different input variables of the models affecting the lane-changing behaviors are presented. Next, a review of the models developed based on well-known approaches, such as artificial neural network (ANN), hidden Markov model (HMM), and support vector machine (SVM), using different input variables is given. Three lane-changing behaviors are focused on here: left/right lane change and lane keeping. The advantages and disadvantages of the different ML models with a comparison are summarized as well. Finally, the improvements required in the future are discussed.

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Section: Introductionmentioning

confidence: 99%