<rss version="2.0" xmlns:atom="https://www.w3.org/2005/Atom">
  <channel>
    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
    <description></description>
    <language>en-us</language>
    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
    </image>
    <item>
      <title>A provable safety metric for autonomous driving via deep reinforcement learning and PAC verification</title>
      <link>https://trid.trb.org/View/2689826</link>
      <description><![CDATA[Real-time safety metrics are essential for autonomous driving safety, which can trigger warnings for human drivers to take over or activate the emergency system in the presence of collision risks. Due to the rarity of safety-critical events in high-dimensional driving environments, most existing quantitative metrics lack formal guarantees to their reliability, while some qualitative metrics are provable but can only provide binary safety judgments with high conservativeness. To overcome these limitations, we propose a provable quantitative safety metric that enables real-time prediction of a lower bound of collision time with a theoretical guarantee and acceptable conservativeness, termed Provable Minimum Collision Time (PMCT). We formulate the computation of PMCT as a risk-based optimal state set partition problem, and solve it through a framework combining deep reinforcement learning for the partition’s construction and probably approximately correct (PAC) verification for ensuring the reliability of the PMCT. Extensive experiments confirm the provability of PMCT and demonstrate its ability to identify hazardous situations with a higher recall rate while significantly reducing false alarms than existing metrics.]]></description>
      <pubDate>Thu, 09 Jul 2026 13:31:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2689826</guid>
    </item>
    <item>
      <title>Analyzing Trends in Micromobility Safety to Inform ODOT’s Safety Programming</title>
      <link>https://trid.trb.org/View/2726155</link>
      <description><![CDATA[The use of electric micromobility devices, including e-bikes, e-scooters, e-unicycles, and other emerging devices, has been steadily increasing as modes of transportation in Oregon over the past several years, generating many questions about how best to integrate these devices into the transportation system. Safety concerns, as the rate of injuries sustained while riding an electric micromobility devices that necessitated an ER visit or hospitalization has significantly increased between 2021 and 2024, rising from 414 potential injuries in 2021 to 1,229 in 2024, according to Oregon Health Authority (OHA) data. These numbers probably underrepresent total crashes. Oregon Department of Transportation's (ODOT’s) Transportation Safety Office (TSO) oversees safety education and training programs, including bicycle and motorcycle safety, but they currently do not have a clear picture of the crash data and magnitude of risk associated with e-micromobility devices given the relative nascency of the mode. Analyzing the available sources of data and developing a deeper understanding of the safety concerns of local agencies and community organizations are critical first steps. This research will help the agency and their transportation safety partners to be data driven in their development of e-micromobility safety materials, safety training programs, strategic project planning and funding investment decisions to reduce the crash and injury risk related to these devices.
OBJECTIVES: This research will help answer the following questions: What’s the extent and magnitude of injuries? What’s the rate of injury for youth compared to adults?  Through a safe system approach, what are the primary causes or factors of crashes involving people riding e-micromobility devices?  What types of devices are most involved in crashes? What are the current concerns for transportation partners and law enforcement related to e-micromobility safety and how are these concerns compared to what is seeing in the data?  
The findings of this important research project will help ODOT identify the core strategies to better tailor ODOT safety programming and partnerships. The results will also be used to educate policymakers, interested partners, and the public. ODOT is seeking a deeper understanding of safety issues and concerns related to the emerging field of small devices that have varying amounts of assisted power beyond human propulsion, such as e-bikes, e-scooters, and e-unicycles. While usage of these devices has emerged in the last decade—and in higher numbers in the last five years—safety research and recommendations have been slow to catch up. Analyzing the available data will help inform the development of data-driven strategies and investments within ODOT, as well as the external partners ODOT works with.]]></description>
      <pubDate>Wed, 08 Jul 2026 17:27:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2726155</guid>
    </item>
    <item>
      <title>Dalmo Victor Active Beacon Collision Avoidance System Flight Test Dated September 28, 1981</title>
      <link>https://trid.trb.org/View/2714186</link>
      <description><![CDATA[The purpose of this flight test program is to evaluate the performance of the prototype Active Beacon Collision Avoidance System (ABCAS), manufactured by the Dalmo Victor Corporation, according to the objectives of the Test Plan for Dalmo Victor Active Beacon Collision Avoidance System (ABCAS) dated July 1981. This report contains the results of the seventh flight in a series of test plan performance flight tests which were initiated on August 19, 1981. The purpose of this report is to provide flight descriptions, test results, and preliminary evaluations of the September 28, 1981, flight test to program participants. These descriptions and results are provided in the form of encounter and logic plots, encounter data summaries, flight profiles, and a mission report. In addition to the present flight results, previous flight data will be reported in a cumulative table of results.]]></description>
      <pubDate>Tue, 07 Jul 2026 17:29:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2714186</guid>
    </item>
    <item>
      <title>Transport Aircraft Accident Dynamics</title>
      <link>https://trid.trb.org/View/2713606</link>
      <description><![CDATA[This report is the final technical report covering the review of survivable transport aircraft accidents, the association between structural systems and accident injuries, and the identification of typical scenarios. This report also includes a review of the five volumes of the "Aircraft Crash Survival Design Guide", an overview of crash testing techniques and test recommendations, an overview and recommendations for analytical techniques, and advanced material usage.]]></description>
      <pubDate>Tue, 07 Jul 2026 17:29:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2713606</guid>
    </item>
    <item>
      <title>Time Matters: Evaluating a Road Operator Event Management System Using Vehicle eCalls</title>
      <link>https://trid.trb.org/View/2671785</link>
      <description><![CDATA[This study investigates the potential integration of eCalls, mandated by the eCall Legislation for vehicles from March 31, 2018, into road operator Incident Management Systems (IMS). We utilize eCall data from the “SRTI Ecosystem” and match it with IMS incidents to verify the eCall data and enhance the IMS with the most accurate available timestamp for accident occurrence. To assess the potential temporal gains of eCall integration, we introduce a metric to quantify the time saved by incorporating eCalls into event management systems. The proposed metric was evaluated over a three-month period in 2023, and the results indicate that integrating eCalls into IMS is a viable step to expedite the incident management process.]]></description>
      <pubDate>Tue, 30 Jun 2026 16:05:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2671785</guid>
    </item>
    <item>
      <title>A Parametric Study of Altitude Tracking Performance for the En Route Conflict Alert Function</title>
      <link>https://trid.trb.org/View/2713590</link>
      <description><![CDATA[The altitude tracking algorithm presently used for en route air traffic control is a fixed-parameter α-β filter. Altitude tracking data are required to support the operation of the Conflict Alert algorithm which detects potentially hazardous conditions between two aircraft. A mathematical theory is developed to evaluate the average warning time before a collision provided by the Conflict Alert. It was found that the average warning time is not a useful measure of tracking performance since the warning time is primarily determined by other factors, such as the prediction time used by Conflict Alert and the initial separation of the targets. A more significant measure of altitude tracking performance is the variance of the warning time. The variations in warning time will measure any performance degradation resulting from the practical implementation of the tracker; e.g., the accuracy of the time measurement, the computational precision used and the quantization errors in the altitude measurements. It is possible that a substantial loss in warning time will be observed if the altitude tracker suffers performance degradation due to finite precision effects which are well-known defects of recursive digital filters.]]></description>
      <pubDate>Tue, 30 Jun 2026 10:54:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/2713590</guid>
    </item>
    <item>
      <title>Extrinsic-and-Intrinsic Reward-Based Multi-Agent Reinforcement Learning for Multi-UAV Cooperative Target Encirclement</title>
      <link>https://trid.trb.org/View/2617885</link>
      <description><![CDATA[Due to their high flexibility and strong maneuverability, unmanned aerial vehicles (UAVs) have attracted lots of attention and are widely employed in many fields. Especially in target encirclement applications, UAVs have shown great advantages in adaptability and reliability, and can efficiently fly to and evenly surround the targets in complex and dynamic environments. In this paper, we concentrate on the cooperative target encirclement problem of heterogeneous UAVs and try to propose a multi-agent reinforcement learning approach to solve the problem. First, with the models of heterogeneous UAVs and obstacles, we analyze the collision avoidance, motion continuity, and energy consumption constraints of UAVs, and formulate the cooperative target encirclement problem as a multi-constraint combinatorial optimization one. Then, inspired by the humans’ learning experience that curiosity provides a powerful motivator for humans to explore, discover, and acquire new knowledge, we propose an extrinsic-and-intrinsic reward-based multi-agent reinforcement learning approach to cooperatively control the behaviors of UAVs and achieve the target encirclement missions. Simulation experiments with randomly generated environments are conducted to evaluate the performance of our approach, and the results show that our approach has a significant advantage in terms of average reward, encirclement success rate, encirclement time, and encirclement energy consumption.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617885</guid>
    </item>
    <item>
      <title>A Lightweight Transformer-Based Collision Detection and Load Estimation Scheme for Massive Random Access in 6G Satellite-Ground Integrated Vehicular Networks</title>
      <link>https://trid.trb.org/View/2617884</link>
      <description><![CDATA[As an indispensable component of the 6G-enabled intelligent transportation systems, the satellite-ground integrated vehicular networks (SGIVN) have attracted widespread attention in recent years for its ability to provide continuous and ubiquitous connectivity services. However, in view of a huge number of access requirements from vehicle terminals and the restricted contention resources, the conventional random access (RA) schemes will suffer from severe overload issues when applied to the emerging SGIVN. To address this challenge, we propose a novel deep learning (DL) assisted collision detection and load estimation scheme to efficiently support massive access in the SGIVN. Specifically, a reliable RA preamble based on cyclically shifted Zadoff-Chu sequences is first designed as the precondition of collision detection, which can achieve an optimal performance trade-off between interference mitigation and user identification. By making full use of the intrinsic properties of preamble correlation results and the relevance analysis capability of attention mechanism, we further present a correlation feature extraction based deep RA collision detection framework embedded with a lightweight transformer network, thereby enabling the global dependencies of the few and important features associated with collided loads to be thoroughly acquired from the local correlation results with low overhead. Extensive simulation results validate the feasibility of our scheme in high-dynamic non-terrestrial network scenarios involving large-scale RA collisions, and demonstrate that it can obtain remarkably enhanced detection performance with short computational time, in comparison with state-of-the-art DL-based schemes.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617884</guid>
    </item>
    <item>
      <title>A Multiple Artificial Potential Functions Approach for Collision Avoidance in UAV Systems</title>
      <link>https://trid.trb.org/View/2617812</link>
      <description><![CDATA[Collision avoidance is a problem largely studied in robotics, particularly in uncrewed aerial vehicle (UAV) applications. The main challenges in this area are hardware limitations, the need for rapid response, and the uncertainty associated with obstacle detection. Artificial potential functions (APOFs) are a prominent method to address these challenges. However, existing solutions lack assurances regarding closed-loop stability and may result in chattering effects. Hence, we propose a high-level control method for static obstacle avoidance based on multiple artificial potential functions (MAPOFs), with a set of switching rules with conditions on the parameter tuning ensuring the stability of the final position. The stability proof is established by analyzing the closed-loop system using tools from hybrid systems theory. Furthermore, we validate the performance of the MAPOF control through simulations and real-life experiments, showcasing its effectiveness in avoiding static obstacles.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617812</guid>
    </item>
    <item>
      <title>A Proactive Decision Support Method for Ship Collision Awareness Incorporating Ship Maneuvering</title>
      <link>https://trid.trb.org/View/2617803</link>
      <description><![CDATA[This paper aims to enhance ship collision awareness by providing decision support in collision avoidance. To enable real-time computation in navigation, a Non-iterative Action Distance Determination Algorithm (NADDA) is developed. NADDA reduces the time complexity from O (T log n) to O (T) by eliminating iterative processes, while maintaining the same space complexity as conventional bisection-based methods. This improvement significantly enhances computational efficiency, facilitating real-time applications. Based on NADDA, a robust and interpretable method for quantifying collision stages is developed by incorporating operational uncertainties, ship man- euverability, and the International Regulations for Preventing Collisions at Sea (COLREGs), thereby providing proactive colli- sion avoidance timing. Simulation experiments and a historical collision accident validated the effectiveness of the proposed methods. The results demonstrate that NADDA reduces computa- tion time by up to 93.1% compared to existing bisection-based methods, and the proposed collision stages quantification method achieves higher accuracy due to the consideration of operational uncertainties. The proposed method exhibits strong robustness and can provide real-time decision support in multi-ship encounter scenarios involving up to four target ships.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617803</guid>
    </item>
    <item>
      <title>Incorporating Probabilistic Mutual Interactions in Simulation-Based Safety Evaluation of Maritime Autonomous Surface Ships</title>
      <link>https://trid.trb.org/View/2720262</link>
      <description><![CDATA[This paper proposes a “Probabilistic Reactive Target Model” that generates avoidance behaviors for target ships to evaluate the collision avoidance algorithms of Maritime Autonomous Surface Ships (MASS) in a realistic simulation environment. Focusing on the “shift of the Points of Potential Collision (PPC)” resulting from collision avoidance maneuvers in one-on-one head-on situations, we conducted indirect probabilistic modeling using AIS data. Specifically, we constructed a state transition probability model by estimating the directional probability of the PPC shifting to either the starboard or port side using a linear binary classification model, and by estimating the parameters of the passing distance distribution for each side using a neural network, assuming a log-normal distribution. Furthermore, by iteratively sampling and evaluating transitions to target states that follow this model, we demonstrated that it is possible to generate behaviors in a simulation environment where target ships react to the movements of the MASS.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2720262</guid>
    </item>
    <item>
      <title>Efficient Response Mechanism for Bird Strike Risk Prevention of Low Altitude Logistics Drones</title>
      <link>https://trid.trb.org/View/2711495</link>
      <description><![CDATA[There are common problems with perception and avoidance technology, such as lagging bird recognition, insufficient accuracy in predicting disturbance behavior, and lack of refined quantitative basis for avoidance decisions. Therefore, this article constructs an efficient response framework for bird strike risks in low altitude scenarios, and introduces a collaborative mechanism between artificial intelligence and the Internet of Things to solve the core bottleneck of intelligent perception and control. This article achieves synchronous collection of key variables such as bird density, relative velocity, and invasion angle through real-time interconnection between IoT multi-source nodes and UAV onboard sensors. At the same time, AI behavior modeling is used to dynamically predict the escape trend, approach probability, and disturbance sensitivity of bird flocks. In terms of control strategy, based on the risk gradient output by the behavior model, the maneuver avoidance path with the minimum trajectory deviation is automatically generated, and the triggering timing and threshold of sound, light, and airflow are finely controlled to balance safety and energy consumption constraints in the avoidance process. The experimental results show that the method performs well in detailed indicators such as bird situation recognition accuracy, disturbance prediction error, trajectory exposure, and avoidance energy consumption: in the four test routes, the trajectory exposure is 1.403, 0.240, 2.831, and 1.122 (arb. nits), respectively; avoiding energy consumption is 17.4%, 6.0%, 29.3%, and 17.2% respectively; the number of drone avoidance actions is 4, 2, 2, and 4 times. The proposed mechanism can finely control the trajectory of unmanned aerial vehicles in actual low altitude flight segments, while achieving efficient risk response.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:06 GMT</pubDate>
      <guid>https://trid.trb.org/View/2711495</guid>
    </item>
    <item>
      <title>Learning human collision avoidance behavior for autonomous ships using adaptive neuro-fuzzy inference system</title>
      <link>https://trid.trb.org/View/2712882</link>
      <description><![CDATA[Collision avoidance for Maritime Autonomous Surface Ships (MASS) remains challenging due to the need for safe, COLREGs-compliant decisions in close encounters. This study proposes a human-centric collision avoidance framework based on a computational intelligence approach called Adaptive Neuro-Fuzzy Inference Systems (ANFIS), trained using high-fidelity bridge simulator data that capture realistic navigator behavior. The fuzzy inference structure is generated using Fuzzy C-Means (FCM) clustering, with the optimal number of clusters determined using the Fuzzy Partition Coefficient (FPC) and Xie-Beni (XB) index. Separate ANFIS models are developed for crossing, head-on, and overtaking scenarios, and the effectiveness of the proposed method is validated by closed-loop validations using a second-order Nomoto model. Analysis of three simulation cases demonstrates that ANFIS models generate smooth and stable maneuvers, achieve collision free trajectories with safe passing distances, and comply with COLREGs. Moreover, the predicted rudder commands closely resemble human navigator actions, indicating the capability of the approach to capture underlying patterns. A global sensitivity analysis based on Sobol indices is conducted to quantify the influence of input variables on the ANFIS model output. The results show that different encounter scenarios are governed by distinct dominant features, with crossing and overtaking primarily influenced by geometric variables, while the head-on model exhibits stronger interaction effects among multiple inputs.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2712882</guid>
    </item>
    <item>
      <title>Crash performance analysis of the Oregon triple-tube bridge rail under MASH TL-4 impact conditions</title>
      <link>https://trid.trb.org/View/2709455</link>
      <description><![CDATA[Bridge protective barriers are crucial in preventing vehicle exits and mitigating the effects of collisions. This study evaluates the structural response of the Oregon triple-tube bridge rail under MASH 2016 Test Level 4 (TL-4) loading using a combination of full-scale crash testing and nonlinear finite element (FE) analysis. Comparison of FE simulations with available crash-test measurements showed acceptable agreement in key metrics, including peak dynamic deflection, roll and pitch behaviour, and occupant-risk measures. The FE simulations were then used to examine deformation patterns, load transfer within the rail–post–parapet assembly, and the development of strains and localised damage in steel and concrete components for three vehicle classes (1100 C, 2270 P and 10000S). These internal structural responses, which cannot be obtained directly from crash testing, were evaluated together with global deformation and vehicle-kinematic measures recorded in full-scale tests. Differences among vehicle classes showed the influence of vehicle mass on load transfer and deformation patterns. The 1100 C impact generated localised deformation near the contact point, whereas the 10000S impact distributed loads over a broader region and engaged the parapet reinforcement more significantly. The findings provide a detailed view of the internal mechanics governing the system’s TL-4 performance and complement the existing crash-test evidence by clarifying how different vehicle classes influence force distribution and localised damage within the barrier.]]></description>
      <pubDate>Mon, 29 Jun 2026 09:11:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709455</guid>
    </item>
    <item>
      <title>Air cargo container experimental characterisation and finite element modelling under crash related loads</title>
      <link>https://trid.trb.org/View/2709450</link>
      <description><![CDATA[This article presents experimental results of crash tests performed on air cargo containers, as well as aspects on the finite element modelling for integration of container models in aeroplane crash simulations. Containers of type AKE and AKH were tested and simulated. Following the building block approach, the container structures were tested at different structural scales, while material characterisation at the coupon level was not performed due to sufficient data available from manufacturer data sheets. In total, eight full-scale drop tests of AKE and AKH containers were performed with the force-displacement characteristics as main test result. Finite element models of both container types were developed and validated based on the experimental results. The simulations were performed using the explicit finite element software Abaqus/Explicit. The primary intent of this article is the provision of experimentally determined crash characteristics of air cargo containers that can be used in aeroplane crash analyses.]]></description>
      <pubDate>Mon, 29 Jun 2026 09:11:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709450</guid>
    </item>
  </channel>
</rss>