Driving under adverse weather conditions can be extremely risky, as demonstrated by the fact that over 20% of all crashes occur under these conditions, yet these conditions exist less than 10% of the time. Better understanding driver behavior and the circumstances under which these crashes occur will help inform transportation managers, and provide for more effective safety countermeasures.
Effects of Winter Road Surface on Driver's Risk Avoidance Behavior when the Vehicles Are Entering a Curve operated with Adaptive Cruise Control
Naoyuki Shiraishi, Hokkaido UniversityShow Abstract
Sho Takahashi, Hokkaido University
Toru Hagiwara, Hokkaido University
Minoru Okada, Denso Corporation
Toshiyuki Naito, Docon Co., Ltd.
Kazunori Munehiro, Civil Engineering Research Institute for Cold Region
In winter, to drive a vehicle has a lot of difficulties due to road slipperiness, low visibility and narrowed road by lying snow. Understanding the driving environment where the driver feels danger is necessary to introduce SAE Level 2 or 3 in winter, so the present study aims to clarify driver’s risk avoidance behavior when the drivers are using adaptive cruise control (ACC) under the winter road conditions. In an experiment on the public road, a total of 6 participants drove the test vehicle with ACC-ON conditions on the expressway and several local highways. We measured the driver’s risk avoidance behavior when the vehicles are entering a curve. Also, we simultaneously measured road slipperiness, road geometry, and weather condition. Results indicated that road slipperiness, road geometry, and the vehicle speed have a significant effect on the occurring driver’s risk avoidance behavior entering the curve. Slippery road, hard geometry, and high-speed driving often lead the driver’s risk avoidance behavior. According to these results, it is suggested that the driver’s risk avoidance behavior can reduce if the driving support system slows down before the vehicle approach such a slippery or hard geometry condition road. Also, it is clarified that driver’s risk avoidance behavior was reduced on the expressway which the road geometry design was high level even if the road surface conditions were slippery.
Weather and Surface Condition Detection Using Road-Side Webcams: Application of Pre-trained Convolutional Neural Network
MD Nasim Khan, University of WyomingShow Abstract
Mohamed Ahmed, University of Wyoming
Adverse weather has long been recognized as one of the major causes of motor vehicle crashes due to its negative impact on visibility and road surface. Providing drivers with real-time weather information is therefore extremely important to ensure safe driving in adverse weather. However, identification of road weather and surface conditions is a challenging task because it requires the deployment of expensive weather stations and often needs manual identification and/or verification. Most of the Department of Transportations (DOTs) in the U.S. have installed roadside webcams mostly for operational awareness. This study leveraged these easily accessible data sources to develop affordable automatic road weather and surface condition detection systems. The developed detection models are focused on three weather conditions; clear, light snow, and heavy snow; as well as three surface conditions: dry, snowy, wet/slushy. Several pre-trained Convolutional Neural Network (CNN) models, including AlexNet, GoogLeNet, and ResNet18, were applied with proper modification via transfer learning to achieve the classification tasks. The best performance was achieved using ResNet18 architecture with an unprecedented overall detection accuracy of 97% for weather detection and 99% for surface condition detection. The proposed study has the potential to provide more accurate and consistent weather information in real-time that can be made readily available to be used by road users and other transportation agencies. The proposed models could also be used to generate temporal and spatial variations of adverse weather for proper optimization of maintenance vehicles’ route and time.
Development and Evaluation of Geostatistical Methods for Estimating Weather-Related Collisions – A Large Scale Case Study
Andy Wong, University of AlbertaShow Abstract
Tae J. Kwon, University of Alberta
Winter driving conditions pose a real hazard to road users as it increases the chance for collisions during inclement weather events. As such, road authorities strive to service the hazardous roads or collision hot spots by increasing road safety, mobility, and accessibility. One measure of a hot spot would be winter collision statistics. Using the ratio of winter collisions (WC) to all collisions, roads that show a high ratio of WC should be given a high priority for further diagnosis and countermeasure selection. This study presents a unique methodological framework that is built upon one of the least explored yet powerful geostatistical techniques; namely, Regression Kriging (RK). Unlike other variants of kriging, RK uses auxiliary variables to gain a deeper understanding of contributing factors while also utilizing the spatial autocorrelation structure for predicting WC ratios. The applicability and validity of RK for a large-sale hot spot analysis is evaluated using the northeast quarter of Iowa state spanning 5 winter seasons from 2013-14 to 2017-18. The findings of the case study assessed via three different statistical measures (RMSE, MSE, and RMSSE) suggest that RK is very effective for modeling WC ratios thereby further supporting its robustness and feasibility for a statewide implementation.
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