Owl and Lizard: Patterns of Head Pose and Eye Pose in Driver Gaze Classification

Accurate, robust, inexpensive gaze tracking in the car can help keep a driver safe by facilitating the more effective study of how to improve (1) vehicle interfaces and (2) the design of future Advanced Driver Assistance Systems. In this paper, we estimate head pose and eye pose from monocular video using methods developed extensively in prior work and ask two new interesting questions. First, how much better can we classify driver gaze using head and eye pose versus just using head pose? Second, are there individual-specific gaze strategies that strongly correlate with how much gaze classification improves with the addition of eye pose information? We answer these questions by evaluating data drawn from an on-road study of 40 drivers. The main insight of the paper is conveyed through the analogy of an "owl" and "lizard" which describes the degree to which the eyes and the head move when shifting gaze. When the head moves a lot ("owl"), not much classification improvement is attained by estimating eye pose on top of head pose. On the other hand, when the head stays still and only the eyes move ("lizard"), classification accuracy increases significantly from adding in eye pose. We characterize how that accuracy varies between people, gaze strategies, and gaze regions.Comment: Accepted for Publication in IET Computer Vision. arXiv admin note: text overlap with arXiv:1507.0476

Clarity of View: An Analytic Hierarchy Process (AHP)-Based Multi-Factor Evaluation Framework for Driver Awareness Systems in Heavy Vehicles

Several emerging technologies hold great promise to improve the situational awareness of the heavy vehicle driver. However, current industry-standard evaluation methods do not measure all the comprehensive factors contributing to the overall effectiveness of such systems. The average commercial vehicle driver in the USA is 54 years old with many drivers continuing past retirement age. Current methods for evaluating visibility systems only consider field of view and do not incorporate measures of the cognitive elements critical to drivers, especially the older demographic. As a result, industry is challenged to evaluate new technologies in a way that provides enough information to make informed selection and purchase decisions. To address this problem, we introduce a new multi-factor evaluation framework, “Clarity of View,” that incorporates several important factors for visibility systems including: field of view, image detection time, distortion, glare discomfort, cost, reliability, and gap acceptance accuracy. It employs a unique application of the Analytic Hierarchy Process (AHP) that involves both expert participants acting in a Supra-Decision Maker role alongside driver-level participants giving both actual performance data as well as subjective preference feedback. Both subjective and objective measures have been incorporated into this multi-factor decision-making model that will help industry make better technology selections involving complex variables. A series of experiments have been performed to illustrate the usefulness of this framework that can be expanded to many types of automotive user-interface technology selection challenges. A unique commercial-vehicle driving simulator apparatus was developed that provides a dynamic, 360-degree, naturalistic driving environment for the evaluation of rearview visibility systems. Evaluations were performed both in the simulator and on the track. Test participants included trucking industry leadership and commercially licensed drivers with experience ranging from 1 to 40 years. Conclusions indicated that aspheric style mirrors have significant viability in the commercial vehicle market. Prior research on aspheric mirrors left questions regarding potential user adaptation, and the Clarity of View framework provides the necessary tools to reconcile that gap. Results obtained using the new Clarity of View framework were significantly different than that which would have previously been available using current industry status-quo published test methods. Additional conclusions indicated that middle-aged drivers performed better in terms of image detection time than young and elderly age categories. Experienced drivers performed better than inexperienced drivers, regardless of age. This is an important conclusion given the demographic challenges faced by the commercial vehicle industry today that is suffering a shortage of new drivers and may be seeking ways to retain its aging driver workforce. The Clarity of View evaluation framework aggregates multiple factors critical to driver visibility system effectiveness into a single selection framework that is useful for industry. It is unique both in its multi-factor approach and custom-developed apparatus, but also in its novel approach to the application of the AHP methodology. It has shown significance in ability to discern more well-informed technology selections and is flexible to expand its application toward many different types of driver interface evaluations

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

The Effect of Age and Gender on Visual Search During Lane Changing

This study examined visual search behavior relative to three regions of interest (ROI) (side mirror, rear view mirror, and blind spot) for self-initiated lane changes in a sample of 108 drivers under actual highway conditions. As has been observed previously, few drivers scan all three of the ROI prior to executing a lane change, with turning around to inspect the blind spot being the lowest frequency behavior. Age, gender and direction (left or right lane change) were found to influence visual search behaviors. For lane changes to the right, blind spot checking occurred less than 32% of the time in females and less than 15% of the time in males. This low level of blind spot checking to the right was consistent across younger and older age groupings. Interestingly, the most notable age discrepancy was in checking the left blind spot. Younger drivers checked their left blind spot 53.3% of the time compared to a rate of 23.9% for drivers in their 60s. Implications of these findings for both driver remediation programs and the increasing availability of blind spot identification systems are considered

The use of volumetric projections in Digital Human Modelling software for the identification of large goods vehicle blind spots

The aim of the study is to understand the nature of blind spots in the vision of drivers of Large Goods Vehicles caused by vehicle design variables such as the driver eye height, and mirror designs. The study was informed by the processing of UK national accident data using cluster analysis to establish if vehicle blind spots contribute to accidents. In order to establish the cause and nature of blind spots six top selling trucks in the UK, with a range of sizes were digitized and imported into the SAMMIE Digital Human Modelling (DHM) system. A novel CAD based vision projection technique, which has been validated in a laboratory study, allowed multiple mirror and window aperture projections to be created, resulting in the identification and quantification of a key blind spot. The identified blind spot was demonstrated to have the potential to be associated with the scenarios that were identified in the accident data. The project led to the revision of UNECE Regulation 46 that defines mirror coverage in the European Union, with new vehicle registrations in Europe being required to meet the amended standard after June of 2015

Assessment of Vehicular Vision Obstruction Due to Driver-Side B-Pillar and Remediation with Blind Spot Eliminator

Blind spots created by the driver-side B-pillar impair the ability of the driver to assess their surroundings accurately, significantly contributing to the frequency and severity of vehicular accidents. Vehicle manufacturers are unable to readily eliminate the B-pillar due to regulatory guidelines intended to protect vehicular occupants in the event of side collisions and rollover incidents. Furthermore, assistance implements utilized to counteract the adverse effects of blind spots remain ineffective due to technological limitations and optical impediments. This paper introduces mechanisms to quantify the obstruction caused by the B-pillar when the head of the driver is facing forward and turning 90 degrees, typical of an over-the-shoulder blind spot check. It uses the metrics developed to demonstrate the relationship between B-pillar width and the obstruction angle. The paper then creates a methodology to determine the movement required of the driver to eliminate blind spots. Ultimately, this paper proposes a solution, the Blind Spot Eliminator, and demonstrates that it successfully decreases both the obstruction angle and, consequently, the required driver movement. A prototype of the Blind Spot Eliminator is also constructed and experimented with using a mannequin to model human vision in a typical passenger vehicle. The results of this experiment illustrated a substantial improvement in viewing ability, as predicted by earlier calculations. Therefore, this paper concludes that the proposed Blind Spot Eliminator has excellent potential to improve driver safety and reduce vehicular accidents. Keywords: B-pillar, driver vision, active safety, blind spots, transportation, crash avoidance, side-view assist.Comment: 31 pages, 34 figure

The Driving Simulator Visual Field in Glaucoma – A Novel Task to Test Available Field of View

Glaucoma causes peripheral vision loss and impaired driving performance. We developed a novel driving simulator visual field task (DSVF) in a panoramic driving simulator to map the available field of view under different perceptual task loads in naturalistic settings. Our hypothesis is that “available field of view” will decrease with increasing task load in both glaucoma subjects and controls. This is a cross-sectional study with 28 glaucoma subjects and 19 controls. DSVF (60̊ x 20̊ visual field at 2.5 m) was tested in a high-fidelity interactive driving simulator in 4 different scenarios: a) no distractions b) no driving condition with unrestricted head/eye movements c) driving d) driving with PASAT (Paced Auditory Serial Addition Test). Each test was repeated twice. The main outcome measure was a visual field index (DSVF-VFI). DSVF-VFI was compared to the Humphrey Visual field -HVF-VFI monocularly and binocularly to validate the test. The DSVF task was highly reproducible and comparable to HVF. An A-pillar scotoma appeared in all DSVF trials. In both glaucoma subjects and controls, the DSVF-VFI decreased with increasing task load. The DSVFI decreased significantly more in the glaucoma group as compared to the control group. We developed a predictive formula to predict available field of view while driving from clinic based HVF. Glaucoma subjects were impaired in completing multiple task demands, such as driving and DSVF- either because a) compensation for peripheral vision loss acts as a continuously present load on attention capacity b) glaucoma is associated with diminished cognitive capacity as compared to controls