4,027 research outputs found
Multimodal classification of driver glance
—This paper presents a multimodal approach to invehicle
classification of driver glances. Driver glance is a
strong predictor of cognitive load and is a useful input to
many applications in the automotive domain. Six descriptive
glance regions are defined and a classifier is trained on video
recordings of drivers from a single low-cost camera. Visual
features such as head orientation, eye gaze and confidence
ratings are extracted, then statistical methods are used to
perform failure analysis and calibration on the visual features.
Non-visual features such as steering wheel angle and indicator
position are extracted from a RaceLogic VBOX system. The
approach is evaluated on a dataset containing multiple 60
second samples from 14 participants recorded while driving in
a natural environment. We compare our multimodal approach
to separate unimodal approaches using both Support Vector
Machine (SVM) and Random Forests (RF) classifiers. RF
Mean Decrease in Gini Index is used to rank selected features
which gives insight into the selected features and improves the
classifier performance. We demonstrate that our multimodal
approach yields significantly higher results than unimodal
approaches. The final model achieves an average F1 score of
70.5% across the six classes
Detecting Distracted Driving with Deep Learning
© Springer International Publishing AG 2017Driver distraction is the leading factor in most car crashes and near-crashes. This paper discusses the types, causes and impacts of distracted driving. A deep learning approach is then presented for the detection of such driving behaviors using images of the driver, where an enhancement has been made to a standard convolutional neural network (CNN). Experimental results on Kaggle challenge dataset have confirmed the capability of a convolutional neural network (CNN) in this complicated computer vision task and illustrated the contribution of the CNN enhancement to a better pattern recognition accuracy.Peer reviewe
Integration of an adaptive infotainment system in a vehicle and validation in real driving scenarios
More services, functionalities, and interfaces are increasingly being incorporated into current vehicles and may overload the driver capacity to perform primary driving tasks adequately. For this reason, a strategy for easing driver interaction with the infotainment system must be defined, and a good balance between road safety and driver experience must also be achieved. An adaptive Human Machine Interface (HMI) that manages the presentation of information and restricts drivers’ interaction in accordance with the driving complexity was designed and evaluated. For this purpose, the driving complexity value employed as a reference was computed by a predictive model, and the adaptive interface was designed following a set of proposed HMI principles. The system was validated performing acceptance and usability tests in real driving scenarios. Results showed the system performs well in real driving scenarios. Also, positive feedbacks were received from participants endorsing the benefits of integrating this kind of system as regards driving experience and road safety.Postprint (published version
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ASSESSING THE IMPACT OF BICYCLE TREATMENTS ON BICYCLE SAFETY: A MULTI-METHODS APPROACH
Compared to other modes, bicyclists are disproportionally affected by crashes considering their low mode share. There is evidence that crashes between bicyclists and motorized vehicle take place at road segments and signalized intersections where bicycle treatments (e.g., bike lanes) are present, urging for in-dept analysis of the safety impact of the various bicycle treatment types. Additionally, it is important to identify sensor types that have the potential to advance field data collection and traffic monitoring in multi-modal road environments. In this dissertation, three approaches, namely crash analysis, traffic conflict analysis, and analysis of driver speeding and glancing behavior, were implemented to investigate the safety impact of bicycle treatments at the segment- and the intersection-levels on bicycle safety. Prediction models were developed to predict bicycle-motorized vehicle crashes at road segments and signalized intersections, and traffic conflicts between straight-going bicyclists and right-turning vehicles at signalized intersections. Driver speeding and glancing behavior was analysed for the segment and the intersection levels. A mode classification framework to classify trajectories recorded using a radar-based sensor was developed to test the feasibility of using radar-based sensors in field studies. The findings of this dissertation contribute to bicycle safety research in terms of quantifying the safety impact of various bicycle treatment types and how to assess and also, by showing how to assess bicycle safety. The findings of this research have the potential to stand as a valuable tool for transportation policymakers and officials in charge of establishing safe bicycle networks
Studying Person-Specific Pointing and Gaze Behavior for Multimodal Referencing of Outside Objects from a Moving Vehicle
Hand pointing and eye gaze have been extensively investigated in automotive
applications for object selection and referencing. Despite significant
advances, existing outside-the-vehicle referencing methods consider these
modalities separately. Moreover, existing multimodal referencing methods focus
on a static situation, whereas the situation in a moving vehicle is highly
dynamic and subject to safety-critical constraints. In this paper, we
investigate the specific characteristics of each modality and the interaction
between them when used in the task of referencing outside objects (e.g.
buildings) from the vehicle. We furthermore explore person-specific differences
in this interaction by analyzing individuals' performance for pointing and gaze
patterns, along with their effect on the driving task. Our statistical analysis
shows significant differences in individual behaviour based on object's
location (i.e. driver's right side vs. left side), object's surroundings,
driving mode (i.e. autonomous vs. normal driving) as well as pointing and gaze
duration, laying the foundation for a user-adaptive approach
Ambient hues and audible cues: An approach to automotive user interface design using multi-modal feedback
The use of touchscreen interfaces for in-vehicle information, entertainment, and for the control of comfort settings is proliferating. Moreover, using these interfaces requires the same visual and manual resources needed for safe driving. Guided by much of the prevalent research in the areas of the human visual system, attention, and multimodal redundancy the Hues and Cues design paradigm was developed to make touchscreen automotive user interfaces more suitable to use while driving. This paradigm was applied to a prototype of an automotive user interface and evaluated with respects to driver performance using the dual-task, Lane Change Test (LCT). Each level of the design paradigm was evaluated in light of possible gender differences. The results of the repeated measures experiment suggests that when compared to interfaces without both the Hues and the Cues paradigm applied, the Hues and Cues interface requires less mental effort to operate, is more usable, and is more preferred. However, the results differ in the degradation in driver performance with interfaces that only have visual feedback resulting in better task times and significant gender differences in the driving task with interfaces that only have auditory feedback. Overall, the results reported show that the presentation of multimodal feedback can be useful in design automotive interfaces, but must be flexible enough to account for individual differences
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