Lane departure warning system based on future driving path prediction

Kuo, Ying Che; Chang, Chin

doi:10.1504/ijvs.2014.060163

Cited by 3 publications

(4 citation statements)

References 11 publications

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“…From Equation ( 5), it can be found that the LKA intervention timing that the driver feels is most satisfactory is not a specific value of either DLC th or v y−lane alone. Instead, it depends on the specific relationship between these two metrics, which is determined by the coefficients in Equation (5). Therefore, we introduce two new metrics to denote the key metrics that heavily influence drivers' preferences: the virtual boundary offset distance offset VB and the virtual boundary crossing time TLC VB , as shown in Equation ( 6):…”

Section: Driver's Preference Regarding Lka Intervention Timingmentioning

confidence: 99%

“…A comprehensive subjective evaluation was used to assess the overall performance of the integrated DALKA system. Ratings were given on a scale of [1,5], with an acceptable range of [4,5]. For comparison, the drivers were also asked to provide a comprehensive evaluation of a fixed-characteristic LKA system.…”

Section: Validation Test Of Dalka Systemmentioning

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%

“…Some studies, not relying on metrics, directly employ non-parametric methods like Gaussian mixture models [8] for driver classification. After classification, personalized ADAS characteristics are tailored to different driving characteristic groups using methods like fuzzy rule tables [5] or averaging within-group driving data [8]. Individual-based methods involve configuring ADAS characteristics for each driver based on their individual driving characteristics.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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: Driver's Preference Regarding Lka Intervention Timingmentioning

confidence: 99%

Section: Validation Test Of Dalka Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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Lane Departure Detection and Early Warning System for Intelligent Vehicles Based on Yolov3 Algorithm

Liu

2024

Smart Innovation, Systems and Technologies

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A hybrid neural network for driving behavior risk prediction based on distracted driving behavior data

Meng

Wang

et al. 2022

PLoS ONE

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Distracted driving behavior is one of the main factors of road accidents. Accurately predicting the risk of driving behavior is of great significance to the active safety of road transportation. The large amount of information collected by the sensors installed on the vehicle can be identified by the algorithm to obtain the distracted driving behavior data, which can be used to predict the driving behavior risk of the vehicle and the area. In this paper, a new neural network named Driving Behavior Risk Prediction Neural Network (DBRPNN) is developed for prediction based on the distracted driving behavior data. The network consists of three modules: the Feature Processing Module, the Memory Module, and the Prediction Module. In this process, attribute data (time in a day, daily driving time, and daily driving mileage) that can reflect external factors and driver statuses, are added to the network to increase the accuracy of the model. We predicted the driving behavior risk of different objects (Vehicle and Area). For the applicability improvement of the model, we further classify the distracted driving behavior categories, and DBRPNN can provide more accurate risk prediction. The results show that compared with traditional models (Classification and Regression Tree, Support Vector Machines, Recurrent Neural Network, and Long Short-Term Memory), DBRPNN has better prediction performance. The method proposed in this paper has been fully verified and may be transplanted into active safety early warning system for more accurate and flexible application.

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Lane departure warning system based on future driving path prediction

Cited by 3 publications

References 11 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

Lane Departure Detection and Early Warning System for Intelligent Vehicles Based on Yolov3 Algorithm

A hybrid neural network for driving behavior risk prediction based on distracted driving behavior data

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