Lane Departure Warning System Based on New Feature Fusion Algorithm and Departure Judgment Rule

Jia, Huijie; Xiao, Zhongjun; Ji, Peng

doi:10.2139/ssrn.4145253

Cited by 1 publication

(2 citation statements)

References 14 publications

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“…It can provide an intuitive insight into the driving behavior of an individual driver [17]. Common analysis methods for naturalistic driving characteristics mainly include the descriptive statistics method [6,10], the parameter estimation method [18,19], and the non-parameter estimation method [20]. The descriptive statistics method refers to using basic statistical metrics such as the mean and standard deviation of various variables during a driver's naturalistic driving process to describe each characteristic.…”

Section: Lateral Naturalistic Driving Characteristic Analysis Methodsmentioning

confidence: 99%

“…Classification-based methods involve categorizing drivers into different groups based on their naturalistic driving characteristics, followed by configuring distinct ADAS features for each group. Specifically, drivers are classified into categories such as "conservative", "normal", and "aggressive" based on metrics from driving data, such as lateral position and speed within the lane [5] and time to lane-crossing [6,7]. Some studies, not relying on metrics, directly employ non-parametric methods like Gaussian mixture models [8] for driver classification.…”

Section: Introductionmentioning

confidence: 99%

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A Method to Develop the Driver-Adaptive Lane-Keeping Assistance System Based on Real Driver Preferences

Chen,

Lan

et al. 2024

Sensors

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To satisfy the preference of each driver, the development of a Lane-Keeping Assistance (LKA) system that can adapt to individual drivers has become a research hotspot in recent years. However, existing studies have mostly relied on the assumption that the LKA characteristic aligned with the driver’s preference is consistent with this driver’s naturalistic driving characteristic. Nevertheless, this assumption may not always hold true, causing limitations to the effectiveness of this method. This paper proposes a novel method for a Driver-Adaptive Lane-Keeping Assistance (DALKA) system based on drivers’ real preferences. First, metrics are extracted from collected naturalistic driving data using action point theory to describe drivers’ naturalistic driving characteristics. Then, the subjective and objective evaluation method is introduced to obtain the real preference of each test driver for the LKA system. Finally, machine learning methods are employed to train a model that relates naturalistic driving characteristics to the drivers’ real preferences, and the model-predicted preferences are integrated into the DALKA system. The developed DALKA system is then subjectively evaluated by the drivers. The results show that our DALKA system, developed using this method, can enhance or maintain the subjective evaluations of the LKA system for most drivers.

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Section: Lateral Naturalistic Driving Characteristic Analysis Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Method to Develop the Driver-Adaptive Lane-Keeping Assistance System Based on Real Driver Preferences

Chen,

Lan

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

Lane Departure Warning System Based on New Feature Fusion Algorithm and Departure Judgment Rule

Cited by 1 publication

References 14 publications

A Method to Develop the Driver-Adaptive Lane-Keeping Assistance System Based on Real Driver Preferences

A Method to Develop the Driver-Adaptive Lane-Keeping Assistance System Based on Real Driver Preferences

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