Hydroplaning Risk Evaluation of Highway Pavements based on IMU and 1 mm 3D Texture Data

Abstract

Automobiles, when traveling at a sufficiently high speed during wet weather, can experience hydroplaning causing out-of-control accidents. Annually, 14% of all accidents with fatal victims occur on wet pavements. However, there are few research studies on detecting and identifying hydroplaning sections in a pavement network. This is primarily due to the fact that it is difficult and sometimes impossible to collect complete pavement surface data with geometric and geographical accuracies which are required to conduct texture, profiling, and cross slope analyses as the basis of hydroplaning studies. The advent of PaveVision3D Ultra technology, which is able to collect true 1mm 3D pavement data with complete coverage and at highway speed, makes it possible to collect and analyze virtual pavement surfaces at sufficient resolution for safety purposes. As a matter of fact, PaveVision3D Ultra is able to provide 0.3mm vertical resolution and 1mm resolution in x and y directions while the data vehicle is traveling at 60mph. Coupled with high-performance Inertial Measurement Unit (IMU) data for geometric information, the collected 3D and position data sets are used to formulate a virtual pavement surface at high resolution for the proposed study. The hydroplaning velocity is a critical index to detect segments with aquaplaning potential; the influencing factors of hydroplaning velocity include the tire characteristics, pavement subsurface structure, pavement surface characteristics, and rainfall intensity. Recognizing that a highway with a high speed limit and heavy traffic can lead to higher accident risk due to hydroplaning than other types of road, this study uses a section of interstate highway I-67S in Arkansas as the case study to present the pavement surface characteristics on hydroplaning evaluation. The significance of the presented data in the paper is three-fold. First, coupled with roll angles from IMU data, the collected 3D transverse profile data are used for cross slope calibration. Second, it integrates the real-time 1mm 3D surface data and IMU data into the Gallaway hydroplaning speed prediction model. Third, this method can identify hazardous locations with high hydroplaning potential so that pavement engineers may take remedial measures such as constructing superior grooving texture, posting proper speed traffic signs, etc., to minimize potential traffic accidents.