PORTABLE WEIGH-IN-MOTION FOR PAVEMENT DESIGN - PHASES 1 AND 2

Keeping Oklahoma's roadways, highways, and bridges in good condition is necessary to our state’s safety and to avoid expenditures in billions of dollars each year for road repairs and replacement. According to a study done by state of Oregon in 2009, heavy vehicles account for 79% (or $60 million) of annual expenditures required for roadway repaving. Likewise, they were also responsible for 66.8$27 million) of pavement and shoulder reconstruction; 65.1% (or $145 million) of pavement and shoulder rehabilitations; and 61.5$140 million) of pavement maintenance. To weigh traveling trucks, the state of Oklahoma has installed 20 permanent Weigh-in-Motion (WIM) sites. Expanding site coverage to include additional roadways and highways improves data accuracy; however, it requires significant roadside construction and costly infrastructure support. This report presents deployment results of a novel portable WIM system and compares captured data with that collected at a nearby permanent WIM system. Design, development, and road-installation details of the heavy-vehicle centric, portable WIM system are also provided. Outcomes demonstrate that the portable system maintains data quality but for short intervals and provides a viable alternative to permanent systems at merely 10 percent of the cost. The portable WIM system uses off-the-shelf components and commercially available WIM controllers. The WIM controller used was IRD iSINC Lite. The fabricated portable system could be promoted as an alternative WIM monitoring solution to permanent WIM systems and/or static scale stations, both of which are extremely expensive to install on highways. The portable WIM uses RoadTrax BL piezoelectric class-1 sensors, galvanized metal fixtures equipped with pocket tapes to house the sensors, and a trailer with cabinet to house WIM electronics, batteries, and REECE device for real-time monitoring. The system is solar powered with three 100-Watt panels, and it costs roughly $20,000.Final report, October 2011-October 2014N

Towards Full Automated Drive in Urban Environments: A Demonstration in GoMentum Station, California

Each year, millions of motor vehicle traffic accidents all over the world cause a large number of fatalities, injuries and significant material loss. Automated Driving (AD) has potential to drastically reduce such accidents. In this work, we focus on the technical challenges that arise from AD in urban environments. We present the overall architecture of an AD system and describe in detail the perception and planning modules. The AD system, built on a modified Acura RLX, was demonstrated in a course in GoMentum Station in California. We demonstrated autonomous handling of 4 scenarios: traffic lights, cross-traffic at intersections, construction zones and pedestrians. The AD vehicle displayed safe behavior and performed consistently in repeated demonstrations with slight variations in conditions. Overall, we completed 44 runs, encompassing 110km of automated driving with only 3 cases where the driver intervened the control of the vehicle, mostly due to error in GPS positioning. Our demonstration showed that robust and consistent behavior in urban scenarios is possible, yet more investigation is necessary for full scale roll-out on public roads.Comment: Accepted to Intelligent Vehicles Conference (IV 2017

DEVELOPMENT OF INEXPENSIVE VEHICLE SENSOR NODE SYSTEM FOR VOLUME, TURN MOVEMENT AND COLLISION AVOIDANCE (FHWA-OK-16-06 2252)

Real-time traffic surveillance is essential in today’s intelligent transportation systems and will surely play a vital role in tomorrow’s smart cities. The work detailed in this paper reports on the development and implementation of a novel smart wireless sensor for traffic monitoring. Reliable and computationally efficient algorithms for vehicle detection, speed and length estimation, classification, and time-synchronization were fully developed, integrated, and evaluated. Comprehensive system evaluation and extensive data analysis were performed to tune and validate the system for a reliable and robust operation. Several field studies conducted on highway and urban roads for different scenarios and under various traffic conditions resulted in 99.98% detection accuracy, 97.11% speed estimation accuracy, and 97% length-based vehicle classification accuracy. The developed system is portable, reliable, and cost-effective. The system can also be used for short-term or long-term installment on surface of highway, roadway, and roadside. Implementation cost of a single node including enclosure is US $40.Final report, October 2012-December 2013N