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

Car crashes with two-wheelers in China: Proposal and assessment of C-NCAP automated emergency braking test scenarios

In China, around 15,000 users of two-wheelers (TWs) die on the road every year. Passenger cars are the dominating crash opponent of TWs in road traffic crashes. Understanding the characteristics of car crashes with TWs is essential to enhance cars’ safety performance and improve the safety of TW riders in China. This thesis has three objectives. First, to define test scenarios of Automated Emergency Braking systems for cars encountering TWs (TW-AEB) in China (Paper I). Second, to assess whether cars with good ratings in consumer safety rating programs (e.g., New Car Assessment Program: NCAP) are also likely to perform well in the real-world. Finally, to understand the characteristics of the car crashes with TWs after the TW-AEB application. To achieve the first objective, cluster analysis was applied to the China In-Depth Accident Study (CIDAS). The results were six test scenarios (Paper I), which are proposed for the Chinese NCAP (C-NCAP) TW-AEB testing. To achieve the second and third objectives, counterfactual virtual simulations were performed with and without TW-AEB to a) a C-NCAP TW-AEB test scenario set ; b) an alternative scenario set based on the results of Paper I; and c) real-world crashes in China. Results show much higher crash avoidance rate and lower impact speed were found for C-NCAP scenario set than for the other two sets. To better reflect car crashes with TW in China, longitudinal same-direction scenarios with the car or TW turning and perpendicular scenarios with high TW traveling speed are recommended to be included in C-NCAP future releases. Future work will focus on assessing the combined benefit of preventive and protective safety systems for car-to-TW crashes in China

How do drivers overtake pedestrians? Evidence from field test and naturalistic driving data

For pedestrians, the risk of dying in a traffic accident is highest on rural roads, which are often characterized by a lack of sidewalks and high traffic speed. In fact, hitting the pedestrian during an overtaking attempt is a common crash scenario. To develop active safety systems that avoid such crashes, it is necessary to understand and model driver behavior during the overtaking maneuvers, so that system interventions are acceptable because they happen outside drivers’ comfort zone. Previous modeling of driver behavior in interactions with pedestrians primarily focused on road crossing scenarios. The aim of this study was, instead, to address pedestrian-overtaking maneuvers on rural roads. We focused our analysis on how drivers adjust their behavior with respect to three safety metrics (in order of importance): 1) minimum lateral clearance when passing the pedestrian, 2) overtaking speed at that moment, and 3) the time-to-collision at the moment of steering away to start the overtaking maneuver.The influence of three factors on the safety metrics was investigated: 1) walking direction (same as the overtaking vehicle or opposite), 2) walking position (on the edge of the vehicle lane or 0.5 m away from the edge on the paved shoulder), and 3) oncoming traffic (absent or present). Seventy-seven overtaking maneuvers in France from the naturalistic driving study UDRIVE and 297 maneuvers in Sweden from field tests were analyzed. Bayesian regression was used to model how minimum lateral clearance and overtaking speed depended on the three factors. Results showed that drivers maintained smaller minimum lateral clearance and lower overtaking speed when the pedestrian was walking in the opposite direction, on the lane edge, or when oncoming traffic was present. Minimum lateral clearance and time-to-collision were only weakly correlated with overtaking speed. The regression models predicted distributions similar to those actually observed in the data. The time-to-collision at the moment of steering away was comparable in value to the time-to-collision used by Euro NCAP for testing active safety systems in car-to-pedestrian longitudinal scenarios since 2018.This study is the first to analyze driver behavior when overtaking pedestrians, based on field test and naturalistic driving data. Results suggest that pedestrian safety is particularly endangered in situations when the pedestrian is walking opposite to traffic, close to the lane, and when oncoming traffic is present. The Bayesian regression models from this study can be used in active safety systems to model drivers’ comfort in overtaking maneuvers

Creating a driving profile for older adults using GPS devices and naturalistic driving methodology

Background/Objectives: Road tests and driving simulators are most commonly used in research studies and clinical evaluations of older drivers. Our objective was to describe the process and associated challenges in adapting an existing, commercial, off-the-shelf (COTS), in-vehicle device for naturalistic, longitudinal research to better understand daily driving behavior in older drivers. Design: The Azuga G2 Tracking DeviceTM was installed in each participant’s vehicle, and we collected data over 5 months (speed, latitude/longitude) every 30-seconds when the vehicle was driven.  Setting: The Knight Alzheimer’s Disease Research Center at Washington University School of Medicine. Participants: Five individuals enrolled in a larger, longitudinal study assessing preclinical Alzheimer disease and driving performance.  Participants were aged 65+ years and had normal cognition. Measurements:  Spatial components included Primary Location(s), Driving Areas, Mean Centers and Unique Destinations.  Temporal components included number of trips taken during different times of the day.  Behavioral components included number of hard braking, speeding and sudden acceleration events. Methods:  Individual 30-second observations, each comprising one breadcrumb, and trip-level data were collected and analyzed in R and ArcGIS.  Results: Primary locations were confirmed to be 100% accurate when compared to known addresses.  Based on the locations of the breadcrumbs, we were able to successfully identify frequently visited locations and general travel patterns.  Based on the reported time from the breadcrumbs, we could assess number of trips driven in daylight vs. night.  Data on additional events while driving allowed us to compute the number of adverse driving alerts over the course of the 5-month period. Conclusions: Compared to cameras and highly instrumented vehicle in other naturalistic studies, the compact COTS device was quickly installed and transmitted high volumes of data. Driving Profiles for older adults can be created and compared month-to-month or year-to-year, allowing researchers to identify changes in driving patterns that are unavailable in controlled conditions