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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
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    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
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      <title>Research in Progress (RIP)</title>
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      <title>Explore the USDOT / Leidos CAVe-In-A-Box Tool </title>
      <link>https://rip.trb.org/View/2122519</link>
      <description><![CDATA[Ground transportation has one of the most significant impacts on human life. In the blink of an eye, valuable lives of humankind and wildlife could be lost without either of their fault.  Intelligent Surface Transportation Management Systems (ISTMS) are aimed at solving these catastrophes to provide comfortable and safe transit to humans while saving infrastructure and nature. To provide safer transportation to the public, for instance, the United States Department of Transportation (USDOT) has prioritized several applications of artificial intelligence (AI) to develop intelligent transportation systems (ITS).  Naturally, AI had made its way into transportation in two diverse ways. First, the connected and automated vehicles (CAVs) to solve transportation complexities in driving passenger cars and supplying trucking services. Second, the existing and highly sophisticated transportation infrastructure such as freeways, roadways, traffic signs, intersections, and roundabouts.  As cyber-physical systems such as CAVs evolve to intersect with the existing transportation infrastructure, there is a huge risk of potential security threats to humans and wildlife. Specifically, due to limited historical data, access to advanced technology innovations powered by AI and potential exploitation of cyber security vulnerabilities, the risks are significantly raised to cause loss of life.  The CAVs are transforming the ISTMS due to their ability to communicate through Vehicle to Everything (V2X) to send and receive data with other Vehicles (V2V), Devices (V2D), Pedestrians (V2P), Cloud (V2C), and Infrastructure (V2I), at the same time.  To support the education about the evolution of CAVs in ISTMS, the Federal Highway Administration (FHWA) has developed a connected and automated vehicles education (CAVe)-In-A-box which allows the transportation researchers to access and test the data models used in the ISTMS such as basic safety messages (BSM), signal phase and timing (SPaT), traveler information messages (TIM), and personal safety messages. 

The goals and objectives of this research are to acquire, assemble, research, design, prototype, test, implement, and deliver a toolbox of software routines with capabilities necessary to assist Ohio Department of Transportation (ODOT) Traffic Monitoring personnel in processing video to detect a pedestrian crossing with or without ASC in the loop. A specific outreach program will also be developed to engage grades 5-12 at schools in the Greater Cincinnati area and undergraduate students in a 1-day workshop to educate and engage in demonstration and testing of the pedestrian crossing use case with the assembled CAVe-In-A-Box tool.   ]]></description>
      <pubDate>Tue, 28 Feb 2023 10:58:56 GMT</pubDate>
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      <title>Some Core Techniques for Safe Autonomous Driving</title>
      <link>https://rip.trb.org/View/1841396</link>
      <description><![CDATA[Reliable curb detection is critical for safe autonomous driving in urban contexts. Curb detection and tracking are also useful in vehicle localization and path planning. Past work utilized a 3D LiDAR sensor to determine accurate distance information and the geometric attributes of curbs. However, such an approach requires dense point cloud data and is also vulnerable to false positives from obstacles present on both road and off-road areas. In this effort, the research team proposes an approach to detect and track curbs by fusing together data from multiple sensors: sparse LiDAR data, a mono camera and low-cost ultrasonic sensors. The detection algorithm will be based on a single 3D LiDAR and a mono camera sensor used to detect candidate curb features and it effectively removes false positives arising from surrounding static and moving obstacles. The detection accuracy of the tracking algorithm is boosted by using Kalman filter-based prediction and fusion with lateral distance information from low-cost ultrasonic sensors. The team will also conduct a complementary effort with the following goals. Autonomous vehicles promise significant advances in transportation safety, efficiency and comfort. However, achieving the goal of full autonomy is impeded by the need to address several operational challenges encountered in practice. Gesture recognition of flagmen on roads is one such set of challenges. An autonomous vehicle needs to make safe decisions and facilitate forward progress in the presence of road construction workers and flagmen. However, human gestures under diverse environmental conditions are very varied and represent significant complexity. In this effort, the team proposes (1) a taxonomy of challenges for organizing traffic gestures, (2) a sizeable flagman gesture dataset, and (3) extensive experiments on practical algorithms for gesture recognition. The team will categorize traffic gestures according to their semantics, flagman appearances, and the environmental context. The team will then collect a dataset covering a range of common flagman gestures with and without props such as signs and flags. Finally, the team will develop a recognition algorithm using different feature representations of the human pose and perform extensive ablation experiments on each component.]]></description>
      <pubDate>Wed, 17 Mar 2021 16:23:36 GMT</pubDate>
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