We evaluate the object detection capabilities of deep learning based CNNs on midwave/longwave dual-band infrared (DBIR) video sequences for the first time. The characterization of CNN object detection performance on DBIR data, and in particular comparative analysis of the performance of DBIR systems relative to single-band longwave infrared (LWIR) and midwave infrared (MWIR) systems, has not been reported previously in the open literature. This is due at least in part to a general lack of labeled, publicly available DBIR data sets. In this paper, we apply a well-known, state-of-the-art CNN to DBIR data for the first time. A new labeled DBIR data set was generated comprising multiple classes of vehicles, people, airplanes, and birds. YOLOv4, pre-trained on the MS COCO dataset, was used for inference on the MWIR and LWIR channels of the DBIR sensor independently. The resulting detections from the two bands were considered both separately and jointly. The labeled objects of this DBIR data set were grouped into small, medium, and large classes. Detection performance on the medium and large objects was comparable to YOLOv4 performance reported previously in the open literature for visible wavelength objects in terms of average precision and average recall. Recall performance on small objects showed a significant size-dependent advantage for DBIR over LWIR or MWIR alone.

As part of ITS initiatives worldwide, Advanced Traveler Information Systems (ATIS) provide potential travelers with information and images from roadway and roadside technology. This information benefits the traveling public by improving pre-travel routing and other trip planning capabilities. As part of broader traffic management ITS strategies, these systems improve overall safety by reducing the number of congestion-related crashes and improve the efficiency of transportation systems by increasing roadway capacity. However, the development of an ATIS offers numerous challenges that are unique to the individual region served. The cost of deployment and operation is of critical importance, particularly for states with smaller populations and transportation budgets. This paper presents the design of the Oklahoma Pathfinder ATIS, addressing the state's constraints and exploiting the state's unique ITS deployment. It details the information capabilities, deployment experiences, and traveler reception of the system.

Short term traffic speed and volume prediction is an important component of well developed Intelligent Transportation Systems and Advanced Traveler Information Systems. In this paper, we examine the use of polled Remote Traffic Microwave Sensors as a data source for aggregate traffic predictors. Clock skew and data loss due to network transience pose significant challenges to integrating polled data into such a predictive system. To overcome these, we present a new interpolation and evaluation scheme for data regularization and predictor generation. A method for evaluating the validity of the test sets is proposed and illustrated in a case study using an aggregate predictor with real traffic sensor data acquired in Oklahoma City.

An architectural framework is studied that can perform dynamic reconfiguration. A basic objective is to dynamically reconfigure the architecture so that its configuration is well matched with the current computational requirements. The reconfigurable resources of the architecture are partitioned into N slots. The configuration bits for each slot are provided through a connection to one of N independent busses, where each bus can select from among K configurations for each slot. Increasing the value of K can increase the number of configurations that the architecture can reach, but at the expense of more hardware complexity to construct the busses. Our study reveals that it is often possible for the architecture to closely track ideal desired configurations even when K is relatively small (e.g., two or four). The input configurations to the collection of busses are defined as steering vectors; thus, there are K steering vectors, each having N equal sized partitions of configuration bits. A combinatorial approach is introduced for designing steering vectors that enables the designer to evaluate trade-offs between performance and hardware complexity associated with the busses.