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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
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    <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>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Adaptive Model Predictive Control of Four-Wheel Drive Intelligent Electric Vehicles Based on Stability Probability under Extreme Braking Conditions</title>
      <link>https://trid.trb.org/View/2709447</link>
      <description><![CDATA[Under extreme braking conditions, the sharp decrease in vehicle speed causes the probability of vehicle states to change rapidly between stable and unstable, significantly impacting its overall safety and stability. Concurrently, the intricate nonlinear coupling of the vehicle's longitudinal, lateral, and vertical dynamics poses significant challenges in maintaining stability. To address these challenges and enhance vehicle stability comprehensively, this paper proposes an adaptive stability probability-based model predictive control (ASPMPC) algorithm for four-wheel drive intelligent electric vehicles. This algorithm aims to mitigate the safety risks associated with speed changes and redundant control. Initially, a driving simulator is utilized to gather vehicle stability data, which is then categorized into four states–stable, trending stable, trending unstable, and unstable–using the density-based spatial clustering of applications with noise (DBSCAN) algorithm. This categorization forms the basis of a stable dataset. Subsequently, this dataset is used to train a one-dimensional convolutional neural network (1D-CNN), generating a real-time stability probability spectrum. Then, based on model predictive control framework, a correlation function of control objective weight factors is established according to the real-time stability probability of the vehicle. This enables dynamics adjustment of the vehicle's longitudinal, lateral, and vertical stability under extreme braking conditions. Simulations and hardware-in-loop tests have demonstrated that ASPMPC outperforms existing methods, minimizing errors in lateral velocity, yaw rate, and roll angle, thus enhancing maneuverability and safety under extreme braking conditions.]]></description>
      <pubDate>Mon, 31 Aug 2026 10:31:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709447</guid>
    </item>
    <item>
      <title>Unbiased Bearing-Only Localization and Circumnavigation of a Constant Velocity Target</title>
      <link>https://trid.trb.org/View/2709445</link>
      <description><![CDATA[This paper studies the problem of controlling an autonomous vehicle (the agent) to localize and circumnavigate an unknown target (which can be an Unmanned Ground Vehicle or an Unmanned Surface Vehicle) moving with a constant but also unknown velocity. We suppose that the agent can only measure the bearings to the target. An adaptive estimation algorithm using only bearing measurements (without explicit differentiation) is proposed to simultaneously estimate the position and the velocity of the target. Unlike existing results, unbiased target estimation is attained such that the estimation errors in both target's position and velocity converge to zero. The proposed target estimator also does not explicitly demand persistent excitation, a condition commonly required by most bearing-based localization methods in the literature. Then, a circumnavigation controller is designed to steer the agent to approach and encircle the target at a prescribed distance. Asymptotic stability of the overall system including the target estimator and circumnavigation controller is rigorously analyzed. Numerical simulations verify the performance of the proposed algorithms.]]></description>
      <pubDate>Mon, 31 Aug 2026 10:31:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709445</guid>
    </item>
    <item>
      <title>Provably Safe Design Methodology for Automatic Longitudinal Control of Autonomous Vehicles</title>
      <link>https://trid.trb.org/View/2709446</link>
      <description><![CDATA[Formally verified automatic longitudinal control (ALC) of road vehicles in complex driving scenarios, including traffic light and road geometry constraints and adverse weather conditions, with or without vehicle-to-vehicle and vehicle-to-infrastructure communication, remains a challenge. The safety conditions for an ALC system are often time-varying with potential discontinuities, and correct-by-construction approaches, e.g., based on control barrier functions (CBFs), often require smooth approximations of these time-varying specifications. This article introduces piecewise continuously differentiable CBFs (PCD-CBFs) and presents a systematic and provably correct design methodology for ALC based on PCD-CBFs to address this challenge. Specifications capturing safety, comfort, and traffic rules are represented in terms of forward-invariance conditions on safe sets characterized by PCD-CBFs. A provably safe controller is then synthesized from the specifications via quadratic programming. The methodology is illustrated for a vehicle on a road with complex road geometry and multiple traffic signals. Simulation results show its effectiveness under a broad range of driving environments.]]></description>
      <pubDate>Mon, 31 Aug 2026 10:31:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709446</guid>
    </item>
    <item>
      <title>Edge-Assisted Ml-Aided Uncertainty-Aware Vehicle Collision Avoidance at Urban Intersections</title>
      <link>https://trid.trb.org/View/2709444</link>
      <description><![CDATA[Intersection crossing represents one of the most dangerous sections of the road infrastructure and Connected Vehicles (CVs) can serve as a revolutionary solution to the problem. In this work, we present a novel framework that detects preemptively collisions at urban crossroads, exploiting the Multi-access Edge Computing (MEC) platform of 5G networks. At the MEC, an Intersection Manager (IM) collects information from both vehicles and the road infrastructure to create a holistic view of the area of interest. Based on the historical data collected, the IM leverages the capabilities of an encoder-decoder recurrent neural network to predict, with high accuracy, the future vehicles' trajectories. As, however, accuracy is not a sufficient measure of how much we can trust a model, trajectory predictions are additionally associated with a measure of uncertainty towards confident collision forecasting and avoidance. Hence, contrary to any other approach in the state of the art, an uncertainty-aware collision prediction framework is developed that is shown to detect well in advance (and with high reliability) if two vehicles are on a collision course. Subsequently, collision detection triggers a number of alarms that signal the colliding vehicles to brake. Under real-world settings, thanks to the preemptive capabilities of the proposed approach, all the simulated imminent dangers are averted.]]></description>
      <pubDate>Mon, 31 Aug 2026 10:31:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709444</guid>
    </item>
    <item>
      <title>Neural Network Observer Based Lpv Fault Tolerant Control of a Flying-Wing Aircraft</title>
      <link>https://trid.trb.org/View/2709443</link>
      <description><![CDATA[For the problem of fault tolerant trajectory tracking control for a large Flying-Wing (FW) aircraft with Linear Parameter-Varying (LPV) model, a gain scheduled H8 controller is designed by dynamic output feedback. Robust synthesis of this gain scheduled H∞ control is carried out by an affine Parameter Dependent Lyapunov Function (PDLF). The problem of trajectory tracking control for the LPV plant is transformed into solving an infinite number of linear matrix inequalities by the PDLF design, and the linear matrix inequalities are solved by convex optimization techniques. To overcome model uncertainties due to linearization and external disturbances, a radial basis function neural network disturbance observer is proposed, and to estimate actuator faults, an LPV fault estimator is designed. Furthermore, a composite controller is proposed to realize fault tolerant trajectory tracking control, which combines the LPV control with the fault estimator and disturbance observer, as well as an active-set based control allocation to avoiding actuator saturation. The approach is tested by simulation of two scenarios that show responses of the altitude, speed and heading angle to (i) unknown disturbances and (ii) actuator faults. The results show that the proposed neural network observer based LPV control has better performances for both disturbance rejecting and fault-tolerant trajectory tracking.]]></description>
      <pubDate>Mon, 31 Aug 2026 10:31:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709443</guid>
    </item>
    <item>
      <title>Machine Learning Based Drive Fault Diagnosis and Isolation Algorithm for 4wis4wid Vehicle Using Residual Visualization</title>
      <link>https://trid.trb.org/View/2709442</link>
      <description><![CDATA[This study proposes an innovative fault diagnosis algorithm developed for the 4WIS4WID vehicle system, aiming to overcome fault location and identification challenges. The 4WIS4WID system utilizes hardware redundancy and fault-tolerant control to maintain vehicle operation even when a fault occurs. However, because the number of degrees of freedom of the vehicle is less than the number of faults, it is impossible to distinguish analytically between faults. The proposed algorithm employs residual analysis to address this limitation, capturing the differences between predicted vehicle behavior and actual sensor data. The residuals are converted into a three-channel image using a 2-D histogram and a logical function to form a single fault image. A convolutional neural network (CNN) learns these fault images to detect the occurrence of a fault and accurately determine its location. The Recursive Least Square (RLS) algorithm is utilized to classify fault types. This method identifies the fault's size and type, and the vehicle's output in which the fault occurred is estimated. The proposed algorithm's fault diagnosis and classification performance are verified through CarMaker-based simulation. This systematic approach to fault diagnosis enhances vehicle safety and reliability in intelligent vehicle applications.]]></description>
      <pubDate>Mon, 31 Aug 2026 10:31:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709442</guid>
    </item>
    <item>
      <title>Influence of the Pedestrian Geometry in Collision Avoidance Systems</title>
      <link>https://trid.trb.org/View/2709441</link>
      <description><![CDATA[The majority of fatalities between cars and pedestrians occur in urban areas. One approach to reduce these accidents are cooperative collision avoidance systems, in which cars and pedestrians exchange movement information to detect collisions. Typically, a scenario in such systems is modelled in two dimensions using a bird's eye perspective. One of the challenges in designing a collision avoidance system is the modelling of pedestrians for collision detection. Depending on which geometry is used for the pedestrian, it can influence the collision detection performance, leading to the risk that collisions are not detected correctly. This risk is further increased by sensor errors, resulting in inaccurate position and movement information. To address these challenges, we propose the following contributions in this work. First, we propose a novel set of collision scenarios based on the assessment protocol for Vulnerable Road User Protection of the European New Car Assessment Programme (Euro NCAP) and extend it by non-collision scenarios. Second, we introduce a new metric (detection score) which measures the overall collision detection performance based on our proposed set of scenarios. Third, we simulate and analyse how different shapes and sizes for the pedestrian model influence the detection score.]]></description>
      <pubDate>Mon, 31 Aug 2026 10:31:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709441</guid>
    </item>
    <item>
      <title>Deep Reinforcement Learning Enabled Persistent Surveillance with Energy-Aware Uav-Ugv Systems for Disaster Management Applications</title>
      <link>https://trid.trb.org/View/2767158</link>
      <description><![CDATA[Integrating Unmanned Aerial Vehicles (UAVs) with Unmanned Ground Vehicles (UGVs) provides an effective solution for persistent surveillance in disaster management. UAVs excel at covering large areas rapidly, but their range is limited by battery capacity. UGVs, though slower, can carry larger batteries for extended missions. By using UGVs as mobile recharging stations, UAVs can extend mission duration through periodic refueling, leveraging the complementary strengths of both systems. To optimize this energy-aware UAV-UGV cooperative routing problem, we propose a planning framework that determines optimal routes and recharging points between a UAV and a UGV. Our solution employs a deep reinforcement learning (DRL) framework built on an encoder-decoder transformer architecture with multi-head attention mechanisms. This architecture enables the model to sequentially select actions for visiting mission points and coordinating recharging rendezvous between the UAV and UGV. The DRL model is trained to minimize the age periods (the time gap between consecutive visits) of mission points, ensuring effective surveillance. We evaluate the framework across various problem sizes and distributions, comparing its performance against heuristic methods and an existing learning-based model. Results show that our approach consistently outperforms these baselines in both solution quality and runtime. Additionally, we demonstrate the DRL policy's applicability in a real-world disaster scenario as a case study and explore its potential for online mission planning to handle dynamic changes. Adapting the DRL policy for priority-driven surveillance highlights the model's generalizability for real-time disaster response.]]></description>
      <pubDate>Thu, 27 Aug 2026 16:32:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2767158</guid>
    </item>
    <item>
      <title>Detection of Undeclared Ev Charging Events in a Green Energy Certification Scheme</title>
      <link>https://trid.trb.org/View/2767153</link>
      <description><![CDATA[The green potential of electric vehicles (EVs) can be fully realized only if their batteries are charged using energy generated from renewable (i.e. green) sources. For logistic or economic reasons, however, EV drivers may be tempted to avoid charging stations certified as providing green energy, instead opting for conventional ones, where only a fraction of the available energy is green. This behaviour may slow down the achievement of decarbonisation targets of the road transport sector. In this paper, we use GPS data to infer whether an undeclared charging event has occurred. Specifically, we construct a Bayesian hypothesis test for the charging behaviour of the EV. Extensive simulations are carried out for an area of London, using the mobility simulator, SUMO, and exploring various operating conditions. Excellent detection rates for undeclared charging events are reported. We explain how the algorithm can serve as the basis for an incentivization scheme, encouraging compliance by drivers with green charging policies.]]></description>
      <pubDate>Thu, 27 Aug 2026 16:32:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2767153</guid>
    </item>
    <item>
      <title>Hdvo: Hybrid Dense Direct Visual Odometry</title>
      <link>https://trid.trb.org/View/2767152</link>
      <description><![CDATA[Dense direct visual odometry (DDVO) is a well-known robust model-based visual odometry method, that computes robot self-localization with visual data. However, to obtain a metric localization depth information is needed, which is not available in most applications, especially in outdoor scenarios. For this reason, we focus on a hybrid dense direct visual odometry (HDVO) that combines a model-based localization module and a data-based stereo depth estimation module. Since depth is estimated from a stereo sensor, the proposed HDVO can achieve better localization performance than methods using only monocular RGB data. Moreover, the learning part of HDVO is optimized with the photometric loss of DDVO without any depth supervision signal. For this loss, occlusion and low-texture will affect the optimization convergence. This paper investigates a binary mask based on stereo-temporal consistency to address the occlusion problem, along with another binary mask based on local patch consistency to solve the low-texture problem. Finally, it is shown that HDVO can achieve a significant advantage in terms of both inference speed and odometry error on public benchmarks.]]></description>
      <pubDate>Thu, 27 Aug 2026 16:32:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2767152</guid>
    </item>
    <item>
      <title>Design and Analysis of Low-Complexity Communication Receiver for PSK–LFM Joint Sensing and Communication Waveform</title>
      <link>https://trid.trb.org/View/2685931</link>
      <description><![CDATA[In this article, we propose novel strategies for preamble detection and synchronization to detect the symbols from the phase shift keying-linear frequency modulated (PSK-LFM) joint sensing and communication waveform at the communication receiver of an uncrewed aerial vehicle (UAV). Since radar waveform requires huge bandwidth (order of 100 MHz–2 GHz) for better range resolution, processing the same waveform at the UAV's communication receiver necessitates high analog-to-digital converter (ADC) sampling rates. To reduce the ADC sampling requirements, in this paper, we propose two signal processing schemes for the communication receiver. In first, a part of the received signal is filtered using a low pass filter (LPF) and sampled for preamble detection, symbol synchronisation, and detecting the symbols. In the other case, the entire signal undergoes undersampling for subsequent processing. Furthermore, we obtain bit error rate (BER) performance for all the cases by considering time, phase, and carrier frequency offsets. We show that processing the undersampled signal yields superior BER performance compared to the filtering approach, even when both operate at an equivalent sampling rate. Furthermore, for all cases, we compare the simulation results with and without offsets, along with the analytical results obtained without offsets.]]></description>
      <pubDate>Thu, 20 Aug 2026 16:53:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685931</guid>
    </item>
    <item>
      <title>A Review of Cooperative Intersection: From Design to Management</title>
      <link>https://trid.trb.org/View/2685929</link>
      <description><![CDATA[Urban intersections are critical areas where traffic flows converge and conflict, significantly influencing traffic safety, economic benefits, and energy consumption. Effective management and control of intersections have become a central focus in transportation research. With the advancement of automation technology, intersection management methods combined with connected autonomous vehicles (CAVs) have been rapidly developed. However, a comprehensive analysis of these emerging approaches remains lacking, from intersection design to management. This paper systematically reviews recent research on cooperative intersection management (CIM). Firstly, it explores the design of intersections. Secondly, various management objectives and evaluation methods are outlined. Thirdly, relevant research on vehicle trajectory control at the micro level, intersection management at the meso level, and arterial traffic flow regulation and network management at the macro level are discussed in detail. Based on this analysis, this paper identifies future research themes, emphasizing the need for trade-offs, integration, and coordination. Key areas for further study include enhancing the alignment between abstract models and real-world applications, balancing the performance of control methods with their implementation efficiency, and integrating various intersection control strategies to collectively enhance traffic efficiency, sustainability, and safety cooperatively.]]></description>
      <pubDate>Thu, 20 Aug 2026 16:53:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685929</guid>
    </item>
    <item>
      <title>Longitudinal Force Estimation in Intelligent Tires Using Key Features and Tread Dynamics Validation</title>
      <link>https://trid.trb.org/View/2685928</link>
      <description><![CDATA[Intelligent tire systems have garnered considerable interest as a technology that enhances tire safety through the monitoring of tire characteristics and tire–road interactions, which directly influence vehicle dynamics. However, battery life limitations owing to computational demands constrain their practical implementation. This study focused on studless winter tires to improve safety on icy and snowy roads, where freezing and snow accumulation increase braking distances, elevating the risk of accidents. Specifically, we developed computationally efficient features from tire acceleration signals to estimate longitudinal force, a key factor in tire–road interaction. Acceleration signals were analyzed to extract features most effective for force estimation. To reduce power consumption, only the most relevant features were selected. The selected features were applied to a machine learning model (ExtraTree regressor) to estimate longitudinal force. The method achieved high estimation accuracy with a normalized root mean square error (NRMSE) of 3.3%, while significantly minimizing computational load and power consumption. Compared to transmitting raw signals, the proposed approach reduced power consumption from 49.4 mW to 0.11 mW per second. Direct observations of the tire–road contact patch using a high-speed camera were conducted to validate the features. Time–frequency analysis of acceleration signals further supported the features' effectiveness, revealing that they correspond to tread vibrations caused by the relaxation phenomenon, where deformed tread elements recover after road contact. The proposed approach offers a promising method to enhance safety and efficiency in winter driving conditions by providing accurate, real-time tire–road interaction data while conserving energy.]]></description>
      <pubDate>Thu, 20 Aug 2026 16:53:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685928</guid>
    </item>
    <item>
      <title>Tactical Planning Interception Enhancement Using Expert Learning: Twin Delayed Deep Deterministic Policy Gradient</title>
      <link>https://trid.trb.org/View/2685927</link>
      <description><![CDATA[The accurate interception of adversarial uncrewed aerial vehicles (UAVs) is paramount for the protection of people and national facilities. Urban cities pose several challenges for target interception algorithms due to the presence of buildings and flying constraints that limit the manoeuvrability of UAVs for target interception. Deep Reinforcement Learning (DRL) algorithms have been deployed to solve the task effectively. However, the design of its inner elements such as the reward function and action distribution limits its generalisation to different environments. To solve this issue, this paper proposes a novel twin-delayed deep deterministic policy gradient (TD3) based expert learning algorithm that combines previous expert experiences with on-line learning to regularise and improve the policy learning effectively. This is done by following an action distribution algorithm that allows a learner agent to mix its own actions with expert ones for learning improvement and fast convergence. Extensive simulation studies are carried out under diverse urban cities configurations to show the robustness and high-accuracy of the proposed approach compared with traditional DRL baseline algorithms.]]></description>
      <pubDate>Thu, 20 Aug 2026 16:53:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685927</guid>
    </item>
    <item>
      <title>Adaptive Fault-Tolerant Tracker Design for Perturbed Quadrotors Subject to Actuator Faults</title>
      <link>https://trid.trb.org/View/2685926</link>
      <description><![CDATA[The trajectory tracking of the quadrotor in the presence of an external disturbance and an actuator failure is the main topic of the upcoming paper. As a result, the novel adaptive fault-tolerant control technique is developed in two stages, which are described below. The nominal model of the quadrotor is taken into consideration first. Thus, LaSalle's Lemma is used to design a powerful controller that certifies the global asymptotic trajectory tracking. Second, the nominal model of the quadrotor is extended to include the actuator defect and external perturbation. Global asymptotic stability of the following error is then certified by developing an adaptive fault-tolerant control method for the disturbed quadrotor. In order to strengthen the system's implementation against the unknown upper-bounded disturbance, the adaptive control process is used in the final section to estimate the external disruption's unknown upper bound. The suggested approach's primary benefits are the tracking error's global asymptotic reachability, the introduction of a fault-tolerant control strategy, and the suggestion of a chattering-free controller. Lastly, the viability and efficiency of the proposed controller for the trajectory tracking of the disrupted quadrotor with the actuator defect are verified using simulation and hardware-in-loop implementations.]]></description>
      <pubDate>Thu, 20 Aug 2026 16:53:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685926</guid>
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