Development of a Situational Awareness Estimation Model Considering Traffic Environment for Unscheduled Takeover Situations
TLDR
A new SA estimation model considering driving-relevant objects and the relationship between parameters was developed and it was found that unscheduled TO led to maneuver error and glance behavior differed from individuals.Abstract:
In semi-autonomous vehicles (SAE level 3) that requires drivers to takeover (TO) the control in critical situations, a system needs to judge if the driver have enough situational awareness (SA) for manual driving. We previously developed a SA estimation system that only used driver’s glance data. For deeper understanding of driver’s SA, the system needs to evaluate the relevancy between driver’s glance and surrounding vehicle and obstacles. In this study, we thus developed a new SA estimation model considering driving-relevant objects and investigated the relationship between parameters. We performed TO experiments in a driving simulator to observe driver’s behavior in different position of surrounding vehicles and TO performance such as the smoothness of steering control. We adopted support vector machine to classify obtained dataset into safe and dangerous TO, and the result showed 83% accuracy in leave-one-out cross validation. We found that unscheduled TO led to maneuver error and glance behavior differed from individuals.read more
Citations
More filters
Journal ArticleDOI
Devil in the details: Systematic review of TOR signals in automated driving with a generic classification framework
TL;DR: In this paper , a classification framework was used to systematically review take-over request (TOR) signals, and a systematic review was performed on articles written in English that were published between 2014 and 2021 using Web of Science, as well as articles retrieved from two previous TOR classification studies and three meta studies on takeover performance.
Journal ArticleDOI
Single Camera Face Position-Invariant Driver’s Gaze Zone Classifier Based on Frame-Sequence Recognition Using 3D Convolutional Neural Networks
TL;DR: The proposed 3D CNN-based approach outperforms a 2D CNN per-frame recognition approach in driving situations when the driver’s face has different distances from the camera.
Journal ArticleDOI
JointFlow: A Foot Motion Tracking Model Combining Pose Estimation Model with Optical Flow
TL;DR: In this article , a keypoint region-based convolutional neural network (keypoint R-CNN) is trained to detect the joints of the foot and the Lucas-Kanade algorithm of optical flow is used to calculate the motion of each foot joint between consecutive frames.
Journal ArticleDOI
Toward Error-Tolerant Robot Navigation: Sequential Inducement Based on Intent Conveyance from Robot to Human and Its Achievement
Mitsuhiro Kamezaki,Ryosuke Kono,Ayano Kobayashi,Hayato Yanagawa,Tomoya Onishi,Yusuke Tsuburaya,Moondeep C. Shrestha,Shigeki Sugano +7 more
TL;DR: In this paper , the authors propose a framework of error-tolerant navigation (ETN) with a process to actively estimate the human intent by iterative interaction from the robot.
References
More filters
Journal ArticleDOI
Least Squares Support Vector Machine Classifiers
TL;DR: A least squares version for support vector machine (SVM) classifiers that follows from solving a set of linear equations, instead of quadratic programming for classical SVM's.
Journal ArticleDOI
Toward a Theory of Situation Awareness in Dynamic Systems
TL;DR: A theoretical model of situation awareness based on its role in dynamic human decision making in a variety of domains is presented and design implications for enhancing operator situation awareness and future directions for situation awareness research are explored.
Journal ArticleDOI
Intelligent Transportation Systems
TL;DR: The scope of this article is to introduce novel functionality for providing knowledge to vehicles, thus jointly managing traffic and safety and to issue directives to the drivers and the overall transportation infrastructure valuable in context handling.
Journal ArticleDOI
Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation
TL;DR: The independence assumptions in cross validation are introduced, and the circumstances that satisfy the assumptions are also addressed, which are used to derive the sampling distributions of the point estimators for k-fold and leave-one-out cross validation.
Journal ArticleDOI
Effects of adaptive cruise control and highly automated driving on workload and situation awareness: A review of the empirical evidence
TL;DR: In this article, the authors investigated the effects of adaptive cruise control (ACC) and highly automated driving (HAD) on drivers' workload and situation awareness through a meta-analysis and narrative review of simulator and on-road studies.