A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

Lethaus, Firas; Baumann, Martin; Köster, Frank; Lemmer, Karsten

doi:10.1016/j.neucom.2013.04.035

Cited by 52 publications

(42 citation statements)

References 20 publications

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“…After collecting data on drivers' visual characteristics in real vehicle tests, they proposed driving behavioral predictors using vision related parameters, drawing the conclusion that eye movements precede other behavioral features. In their simulation study, they compared the accuracies of different driving behavior prediction algorithms [4]. In another simulation, Salvucci and Liu [5] found that drivers have different degrees of attention to rearview mirrors during lane keeping and lane changing which is consistent with Henning's results [1].…”

Section: Introductionmentioning

confidence: 56%

Division of Area of Fixation Interest for Real Vehicle Driving Tests

Guo

Shao

et al. 2017

Mathematical Problems in Engineering

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The area of interest (AOI) reflects the degree of attention of a driver while driving. The division of AOI is visual characteristic analysis required in both real vehicle tests and simulated driving scenarios. Some key eye tracking parameters and their transformations can only be obtained after the division of AOI. In this study, 9 experienced and 7 novice drivers participated in real vehicle driving tests. They were asked to drive along a freeway section and a highway section, wearing the Dikablis eye tracking device. On average, 8132 fixation points for each driver were extracted. After coordinate conversion, the MSAP (Mean Shift Affinity Propagation) method is proposed to classify the distribution of fixation points into a circle type and a rectangle type. Experienced drivers’ fixation behavior falls into the circle type, in which fixation points are concentrated. Novice drivers’ fixation points, which are decentralized, are illustrated in the rectangle type. In the clustering algorithm, the damping coefficient λ determines the algorithm convergence, and the deviation parameter p mainly affects the number of clusters, where larger p values generate more clusters. This study not only provides the cluster type and cluster counts, but also presents the borderlines for each cluster. The findings provide significant contribution to eye tracking research.

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Section: Introductionmentioning

confidence: 56%

Division of Area of Fixation Interest for Real Vehicle Driving Tests

Guo

Shao

et al. 2017

Mathematical Problems in Engineering

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“…Although head/eye movement can be caused by distraction, most of the time, the driver will shift the eye gaze in purpose, which makes the eye movement an important signal for the intention decoding and inference [65]- [67]. It has been proved that the eye movement information improves the intention prediction accuracy and help to decrease the false alarm rate [68] [69]. A significant challenge to the eye information gathering is the eye tracking task.…”

Section: ) Driver Behaviorsmentioning

confidence: 99%

“…Generative models like HMM are widely used in existing LCII studies [52] [57]- [62] [87] [88]. In [69], the authors used three different algorithms, which were ANN, Bayesian network (BN), and Naive Bayesian. In [89], a new feature named comprehensive decision index (CDI) was introduced.…”

Section: ) Generative Modelmentioning

confidence: 99%

Driver Lane Change Intention Inference for Intelligent Vehicles: Framework, Survey, and Challenges

Xing

Wang

et al. 2019

IEEE Trans. Veh. Technol.

198

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Intelligent vehicles and advanced driver assistance systems (ADAS) need to have proper awareness of the traffic context as well as the driver status since ADAS share the vehicle control authorities with the human driver. This study provides an overview of the ego-vehicle driver intention inference (DII), which mainly focus on the lane change intention on highways. First, a human intention mechanism is discussed in the beginning to gain an overall understanding of the driver intention. Next, the ego-vehicle driver intention is classified into different categories based on various criteria. A complete DII system can be separated into different modules, which consists of traffic context awareness, driver states monitoring, and the vehicle dynamic measurement module. The relationship between these modules and the corresponding impacts on the DII are analyzed. Then, the lane change intention inference (LCII) system is reviewed from the perspective of input signals, algorithms, and evaluation. Finally, future concerns and emerging trends in this area are highlighted.

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“…Machine learning techniques, such as support vector machines (SVM) (Mandalia and Salvucci, 2005;Kumar et al, 2013), Naive Bayes (NB), Decision Tree (DT), k-nearest neighbor (KNN) (Lethaus et al, 2013), artificial neural networks (ANN) (Peng et al, 2015) and Bayesian Networks (BN) (Kasper et al, 2012;Li et al, 2016;Weidl et al, 2018), have been implemented to recognize driver LC maneuvers based on a well-trained classifier using labeled datasets. Then new data are fed to the classifier to determine the classification of either LC or LK maneuver.…”

Section: Lane-change Maneuver Recognitionmentioning

confidence: 99%

Effects of feature selection on lane-change maneuver recognition: an analysis of naturalistic driving data

Wang

Zhang

et al. 2018

JICV

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Purpose Feature selection is crucial for machine learning to recognize lane-change (LC) maneuver as there exist a large number of feature candidates. Blindly using feature could take up large storage and excessive computation time, while insufficient feature selection would cause poor performance. Selecting high contributive features to classify LC and lane-keep behavior is effective for maneuver recognition. This paper aims to propose a feature selection method from a statistical view based on an analysis from naturalistic driving data. Design/methodology/approach In total, 1,375 LC cases are analyzed. To comprehensively select features, the authors extract the feature candidates from both time and frequency domains with various LC scenarios segmented by an occupancy schedule grid. Then the effect size (Cohen’s d) and p-value of every feature are computed to assess their contribution for each scenario. Findings It has been found that the common lateral features, e.g. yaw rate, lateral acceleration and time-to-lane crossing, are not strong features for recognition of LC maneuver as empirical knowledge. Finally, cross-validation tests are conducted to evaluate model performance using metrics of receiver operating characteristic. Experimental results show that the selected features can achieve better recognition performance than using all the features without purification. Originality/value In this paper, the authors investigate the contributions of each feature from the perspective of statistics based on big naturalistic driving data. The aim is to comprehensively figure out different types of features in LC maneuvers and select the most contributive features over various LC scenarios.

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A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

Cited by 52 publications

References 20 publications

Division of Area of Fixation Interest for Real Vehicle Driving Tests

Division of Area of Fixation Interest for Real Vehicle Driving Tests

Driver Lane Change Intention Inference for Intelligent Vehicles: Framework, Survey, and Challenges

Effects of feature selection on lane-change maneuver recognition: an analysis of naturalistic driving data

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