Estimating Driver’s Lane-Change Intent Considering Driving Style and Contextual Traffic

Li, Xiaohan; Wang, Wenshuo; Roetting, Matthias

doi:10.1109/tits.2018.2873595

Cited by 84 publications

(46 citation statements)

References 44 publications

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“…And some studies have taken the driver's characteristics into the LC model. Li et al [12] used questionnaire survey to get the driver's driving style and took it into the LC intention model. The results showed that distinguishing driving styles could improve the prediction accuracy significantly.…”

Section: Related Workmentioning

confidence: 99%

“…Attention, the (6) and 7are valid on the premise of 12 xx  . If the reader is interested in other cases, you can derive it by yourself.…”

Section: Of Vehicles In Psychological Field On the Drivermentioning

confidence: 99%

“…Furthermore, DS also affects the LC strategy. Some researchers have taken the DS into the LC model [12], [13]. And there are many driving style classification models [14]- [16].…”

Section: Introductionmentioning

confidence: 99%

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DP and DS-LCD: A New Lane Change Decision Model Coupling Driver’s Psychology and Driving Style

Tao

et al. 2020

IEEE Access

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Lane-Changing Decision (LCD) behavior is complex, dangerous, and varied by the Driver's Psychology (DP) and Driving Style (DS). For lacking the consideration of DP and DS, the existing LCD models cannot predict the LCD process varying with the drivers and traffic conditions accurately. To deal with the problems, a new LCD model coupling DP and DS, named DP&DS-LCD, is put forward. In the model, a psychological field model is constructed to represent the DP effect on the scene vehicles. And then, K-means and K-Nearest Neighbor (KNN) algorithm are respectively adopted in learning and recognition phases to recognize the current driving style pattern. Finally, based on the DP and DS, the multi-Grained Cascade Forest (gcForest) algorithm is applied to predict the LCD behavior. In experiments, DP&DS-LCD is compared with other three LCD models by using the opening I-80 database from Next Generation Simulation project (NGSIM). And the results showed that the DP&DS-LCD model achieved the best performance. Therefore, the DP&DS-LCD model is effective and could provide support for the decision of autonomous vehicles by predicting the surrounding vehicles' Lane-Changing (LC) behavior. INDEX TERMS Autonomous vehicles, driver's psychology, driving style, lane change decision.

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Section: Related Workmentioning

confidence: 99%

“…Attention, the (6) and 7are valid on the premise of 12 xx  . If the reader is interested in other cases, you can derive it by yourself.…”

Section: Of Vehicles In Psychological Field On the Drivermentioning

confidence: 99%

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DP and DS-LCD: A New Lane Change Decision Model Coupling Driver’s Psychology and Driving Style

Tao

et al. 2020

IEEE Access

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“…To process the data, feature-based pattern recognition and machine learning techniques are frequently utilized [214][215][216]. These schemes are designed to either detect a single maneuver behavior such as lane change only, or turn only [211,214,[217][218][219] or multiple maneuver behaviors [220]. For instance, early detection of intention to change the lane was achieved in [221] using HMM-based steering behavior models.…”

Section: Data Processing Algorithmsmentioning

confidence: 99%

Gaze and Eye Tracking: Techniques and Applications in ADAS

Khan

Lee

2019

Sensors

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Tracking drivers’ eyes and gazes is a topic of great interest in the research of advanced driving assistance systems (ADAS). It is especially a matter of serious discussion among the road safety researchers’ community, as visual distraction is considered among the major causes of road accidents. In this paper, techniques for eye and gaze tracking are first comprehensively reviewed while discussing their major categories. The advantages and limitations of each category are explained with respect to their requirements and practical uses. In another section of the paper, the applications of eyes and gaze tracking systems in ADAS are discussed. The process of acquisition of driver’s eyes and gaze data and the algorithms used to process this data are explained. It is explained how the data related to a driver’s eyes and gaze can be used in ADAS to reduce the losses associated with road accidents occurring due to visual distraction of the driver. A discussion on the required features of current and future eye and gaze trackers is also presented.

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“…As a result, a number of researchers have aimed to develop similar systems on mobile phones, which are more easily accessible [7]. In addition to easily accessible systems, researchers also focused on augmenting vehicle control units with machine learning techniques to help reduce road accidents [8,9]. However, if a large benchmark dataset that is representative of all race ethnicities is not used for training, systems like these can easily fail.…”

Section: Introductionmentioning

confidence: 99%

Detecting inter-sectional accuracy differences in driver drowsiness detection algorithms

Ngxande

Tapamo

Burke

2020

2020 International SAUPEC/RobMech/PRASA Conference

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Convolutional Neural Networks (CNNs) have been used successfully across a broad range of areas including data mining, object detection, and in business. The dominance of CNNs follows a breakthrough by Alex Krizhevsky which showed improvements by dramatically reducing the error rate obtained in a general image classification task from 26.2% to 15.4%. In road safety, CNNs have been applied widely to the detection of traffic signs, obstacle detection, and lane departure checking. In addition, CNNs have been used in data mining systems that monitor driving patterns and recommend rest breaks when appropriate. This paper presents a driver drowsiness detection system and shows that there are potential social challenges regarding the application of these techniques, by highlighting problems in detecting dark-skinned drivers faces. This is a particularly important challenge in African contexts, where there are more dark-skinned drivers. Unfortunately, publicly available datasets are often captured in different cultural contexts, and therefore do not cover all ethnicities, which can lead to false detections or racially biased models. This work evaluates the performance obtained when training convolutional neural network models on commonly used driver drowsiness detection datasets and testing on datasets specifically chosen for broader representation. Results show that models trained using publicly available datasets suffer extensively from over-fitting, and can exhibit racial bias, as shown by testing on a more representative dataset. We propose a novel visualisation technique that can assist in identifying groups of people where there might be the potential of discrimination, using Principal Component Analysis (PCA) to produce a grid of faces sorted by similarity, and combining these with a model accuracy overlay.

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Estimating Driver’s Lane-Change Intent Considering Driving Style and Contextual Traffic

Cited by 84 publications

References 44 publications

DP and DS-LCD: A New Lane Change Decision Model Coupling Driver’s Psychology and Driving Style

DP and DS-LCD: A New Lane Change Decision Model Coupling Driver’s Psychology and Driving Style

Gaze and Eye Tracking: Techniques and Applications in ADAS

Detecting inter-sectional accuracy differences in driver drowsiness detection algorithms

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