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    <title>Transport Research International Documentation (TRID)</title>
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    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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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>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
    </image>
    <item>
      <title>An Energy-Efficient Lyapunov-Based Cooperative Adaptive Cruise Controller for Electric Vehicles</title>
      <link>https://trid.trb.org/View/2717669</link>
      <description><![CDATA[As electric vehicles (EVs) are increasingly adopted as platforms for connected and automated vehicles (CAVs), enhancing their energy efficiency becomes critical. With the emergence of vehicle-to-vehicle (V2V) communication, cooperative adaptive cruise control (CACC) offers improved traffic flow, safety, and energy efficiency by enabling real-time coordination among EVs. However, conventional CACC algorithms neglected acceleration and regenerative braking dynamics in their implementation. To address this gap, this paper proposes a third-order dynamic model for EVs which has been derived from real-world experimental data. The authors also propose a novel, practical, and energy-efficient Lyapunov-based CACC controller explicitly designed for EV platoons. The proposed controller is requiring lower control gains while ensuring string stability and energy efficiency. To validate its effectiveness, the authors conduct both simulation and experimental environments, demonstrating that the approach reduces velocity fluctuations, maintains string stability at lower headway times, and improves energy efficiency of the CACC platoon by up to 38.5% compared to a baseline CACC.]]></description>
      <pubDate>Wed, 15 Jul 2026 16:27:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2717669</guid>
    </item>
    <item>
      <title>Autonomous Intersection Management via Prior-Enhanced Multi-Agent Constrained Decision Transformer</title>
      <link>https://trid.trb.org/View/2617747</link>
      <description><![CDATA[Autonomous Intersection Management (AIM) systems present a novel paradigm for the cooperative control of Connected and Automated Vehicles (CAVs) at unsignalized intersections in future cities. Although Reinforcement Learning (RL) offers potential for increased computational efficiency and optimized solutions, challenges remain. These include limited inference capabilities and poor generalization due to simplified neural networks, along with insufficient safety-focused policy optimization. This study presents a novel offline-to-online framework, Prior-Enhanced Multi-Agent Constrained Decision Transformer (PE-MACDT), designed to tackle these challenges. The process begins with sequential decision-making using offline safe RL, which determines optimal actions through autoregressive modeling based on past states, actions, and both reward and cost returns. Leveraging the superior reasoning abilities and strong generalization of large language models like GPT-x and BERT, the sequence modeling challenges are addressed using the Transformer architecture, enhanced by sequence-level entropy regularizers to foster policy exploration. Subsequently, the safety policy learned from the offline dataset is deployed in the online environment and fine-tuned using the Multi-Agent Constrained Policy Optimization (MACPO) method combined with prior knowledge. This approach employs trust and constraint domains for policy updates, ensuring adherence to high standards of safety, comfort, and efficiency in dynamic traffic environments. Simulation results show our methodology outperforms state-of-the-art AIM methods in training convergence speed and asymptotic performance, as well as post-deployment outcomes in traffic efficiency, driving safety, and passenger comfort. The integration of offline pre-training with MACDT and online fine-tuning using MACPO offers a groundbreaking approach with significant potential for advancements in intelligent transportation systems.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617747</guid>
    </item>
    <item>
      <title>Hierarchical model predictive control for automatic high-speed train operation with multiple traction/braking units</title>
      <link>https://trid.trb.org/View/2643272</link>
      <description><![CDATA[This paper proposes an efficient train operation control scheme for automatic high-speed train operation with multiple traction/braking units by adopting a hierarchical model predictive control framework. The higher-level controller optimizes the dynamic reference speed trajectory for the whole train by tracking off-line speed profiles through a sequential quadratic programming approach with a long sampling time. The lower-level controller focuses on detailed in-train dynamics and distributes control tasks across multiple traction/braking units via a fast alternating direction method of multipliers, operating at a faster rate. This hierarchical structure enables multi-rate execution, allowing each controller to adapt to different time scales and respond effectively to external disturbances. Simulation experiments demonstrate the effectiveness and performance benefits of the proposed method in improving speed tracking accuracy and operational robustness under various scenarios.]]></description>
      <pubDate>Wed, 22 Apr 2026 16:15:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643272</guid>
    </item>
    <item>
      <title>A Hierarchical Controller for Connected Truck Platoon: Analysis and Verification</title>
      <link>https://trid.trb.org/View/2561819</link>
      <description><![CDATA[This paper proposes a novel hierarchical controller for connected truck platoons. To this end, the predecessor following topology is used to characterize the communication connectivity between connected trucks. Then, a longitudinal efficient controller consisting of upper-level and lower-level controllers is proposed. In particular, the upper-level controller is designed based on the kinematic model to handle the car-following interactions between connected trucks and delays in communication and input. The lower-level controller comprises a feedforward and a feedback control law. The feedforward control law converts the desired acceleration from the upper-level controller into the vehicle throttle or braking pressure using the inverse dynamic model, while the feedback control law compensates for the control error caused by unknown vehicle parameters. In addition, in the linear region, the internal stability is analyzed based on the second-order kinematic model using s-domain analysis and linearization method, respectively. Then, the string stability is proved. The influence of parameters on the stability performance is extensively discussed using the stability diagram. Finally, the feasibility of the proposed controller is verified via co-simulations in PreScan and TruckSim, in terms of acceleration, velocity, and spacing error profiles.]]></description>
      <pubDate>Mon, 23 Mar 2026 17:14:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2561819</guid>
    </item>
    <item>
      <title>Research on the influence of autonomous controller on human–machine shared control system in obstacle avoidance scenario</title>
      <link>https://trid.trb.org/View/2666946</link>
      <description><![CDATA[Although controller design plays a pivotal role in the human–machine shared control system, a systematic comparison of different control paradigms is lacking. This study addresses this issue by developing a comprehensive evaluation framework to investigate the impact of different types of controllers on system performance. First, six categories of mainstream controllers are taxonomically classified based on their control logic and implementation principles. Representative controller prototypes are then developed for each category. Next, a human–machine shared control system framework incorporating multi-dimensional performance evaluation metrics is established. A human–machine cooperative driving experimental platform is then used to evaluate these controllers in obstacle avoidance scenarios. The experimental results reveal three key findings: (1) Controller type significantly affects driver operation behavior and system performance metrics; (2) Controller aggressiveness is a critical factor in system compatibility; and (3) Authority allocation outcomes are more influenced by controller type than driver experience. These findings challenge conventional design assumptions and provide an evidence-based methodology for selecting and optimizing controllers to achieve the desired performance characteristics of a human–machine shared control system.]]></description>
      <pubDate>Wed, 18 Mar 2026 09:00:00 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666946</guid>
    </item>
    <item>
      <title>Low-Complexity Control for Uncertain Time-Varying SbW Systems With Input Nonlinearity and Dual-Channel Event-Triggering Communication</title>
      <link>https://trid.trb.org/View/2561802</link>
      <description><![CDATA[This paper addresses the low-complexity prescribed performance control problem for steer-by-wire (SbW) systems with input nonlinearity, model uncertainty, full-state constraints, and dual-channel (i.e., the controller-to-actuator and sensor-to-controller channels) event-triggering communication. Firstly, considering the coupling of longitudinal and lateral dynamics, the nonlinearity of tire force, the time-varying characteristics of longitudinal velocity and road adhesion coefficient, the time-varying nonlinear models of the 8-DOF vehicle dynamics system and SbW system are established. Secondly, a dual-channel event-triggering mechanism and low-complexity prescribed performance control method is proposed, in which the communication resources of sensor-to-controller and controller-to-actuator channels and the computing resources of the controller can be saved, also, the model uncertainty and measurement error can be against by the inherent robustness of the proposed methods. Thirdly, theoretical analysis is presented to show that the full-state and tracking errors of the SbW systems can be constrained to the prescribed ranges, and all signals of the closed-loop system are globally uniformly bounded. Finally, simulations and experiments are given to verify the validity of developed methods.]]></description>
      <pubDate>Tue, 24 Feb 2026 09:00:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2561802</guid>
    </item>
    <item>
      <title>Explaining Visual Attention for Autonomous Vehicle Controllers</title>
      <link>https://trid.trb.org/View/2640186</link>
      <description><![CDATA[End to end deep learning controllers can produce strong driving performance, but their internal decision processes are difficult to interpret. This lack of clarity makes it harder for engineers to diagnose failures and can reduce public confidence in automated systems. This project will create a counterfactual explanation framework that identifies which elements in camera images, such as vehicles, pedestrians, or traffic control devices, guide actions like braking or steering. The research will apply generative video inpainting to remove or alter specific visual elements and then observe how the autonomous controller responds to these modified scenarios.

The study will integrate this method with the ADAPT architecture and evaluate it using benchmark datasets and both real and simulated environments, including the QCar testbed. The goal is to provide clear, intuitive explanations for controller decisions that support transparency and improve safety analysis. The framework will help engineers understand system behavior, locate potential weaknesses, and develop autonomous vehicle (AV) technologies that behave in ways that can be evaluated and verified.]]></description>
      <pubDate>Thu, 11 Dec 2025 13:37:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2640186</guid>
    </item>
    <item>
      <title>Reinforcement Learning Control for a Class of Discrete-Time Non-Strict Feedback Multi-Agent Systems and Application to Multi-Marine Vehicles</title>
      <link>https://trid.trb.org/View/2598808</link>
      <description><![CDATA[A novel control design problem for a class of non-strict feedback multi-agent systems (MAS) in discrete-time form is studied based on reinforcement learning (RL) and applied to multi-marine vehicles (MMV). Firstly, for this kind of discrete-time MAS, a novel system transformation, which can not only solve the noncausal problem that exists in the backstepping method but also reduce the computational complexity, is proposed. Secondly, the algebraic-loop problem inherent in the conventional controller design is solved by compensating the dynamics and using the property of neural network (NN). Thirdly, the multi-gradient recursive (MGR) RL scheme is developed for the sake of designing the optimal controller. Finally, the stability analysis is presented, and all signals are ensured to be semi-global uniformly ultimately bounded (SGUUB) in the Lyapunov's sense. Besides, this scheme is applied to the MMV which can be described in the non-strict feedback form to extend the application of the designed controller. The MMV simulation demonstrates the validation of this scheme.]]></description>
      <pubDate>Mon, 08 Dec 2025 17:05:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2598808</guid>
    </item>
    <item>
      <title>Modeling and Control of a Coaxial Pendulum Drone</title>
      <link>https://trid.trb.org/View/2598798</link>
      <description><![CDATA[Given the high energy utilization efficiency of coaxial drones compared to quadrotors, an inverted pendulum coaxial drone is designed with a focus on its modeling and control. Based on the Lagrangian modeling method, a 6-DoF dynamical model of the pendulum drone is established. The strong coupling and under-actuated nature of the model pose significant control challenges. Controllers suitable for such system are proposed to stabilize the fully-actuated part and the two under-actuated parts of the dynamics, respectively. A theoretical stability analysis of the closed-loop dynamics is presented. Finally, in the simulation examples under static reference, dynamic reference, impulse disturbance and model uncertainties, the effectiveness of the proposed controller is verified, and its superior performance is demonstrated in comparative simulations.]]></description>
      <pubDate>Mon, 08 Dec 2025 17:05:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2598798</guid>
    </item>
    <item>
      <title>Direct Data-Driven Tracking Control for Nonlinear Marine Vehicles</title>
      <link>https://trid.trb.org/View/2598765</link>
      <description><![CDATA[This paper addresses the tracking control problem of nonlinear fully-actuated/under-actuated marine vehicles (MVs) with unknown disturbances by establishing a novel direct data-driven control framework. We begin with fully-actuated MVs, characterized as a second-order strict-feedback system. This structure enables us to employ the recursive backstepping design method by introducing a virtual control law. To avoid identifying the complex kinetic model of MVs, we establish a data-based closed-loop representation for the error system of the tracking problem. Leveraging this representation and the backstepping technique, a data-driven tracking controller is derived by solving a semidefinite program with pre-collected noisy data. The proposed controller ensures the global uniform ultimate boundedness of solutions of the closed-loop system by approximately canceling the system's nonlinearity. Next, we tackle the more challenging under-actuated MVs that possess fewer control inputs than generalized coordinates, making direct nonlinearity cancellation inapplicable. To address this issue, we solve the tracking problem by stabilizing a new error system derived through a coordinate transformation. Notably, this new error system shares a similar structure with that of fully-actuated MVs but with a reduced dimension. This similarity enables us to seamlessly extend the results of fully-actuated MVs to under-actuated MVs. Finally, numerical simulations are conducted to demonstrate the effectiveness of the proposed method.]]></description>
      <pubDate>Wed, 26 Nov 2025 16:13:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2598765</guid>
    </item>
    <item>
      <title>An Online Self-Learning Graph-Based Lateral Controller for Self-Driving Cars</title>
      <link>https://trid.trb.org/View/2617957</link>
      <description><![CDATA[The hype around self-driving cars has been growing over the past years and has sparked much research. Several modules in self-driving cars are thoroughly investigated to ensure safety, comfort, and efficiency, among which the controller is crucial. The controller module can be categorized into longitudinal and lateral controllers in which the task of the former is to follow the reference velocity, and the latter is to reduce the lateral displacement error from the reference path. Generally, a tuned controller is not sufficient to perform in all environments. Thus, a controller that can adapt to changing conditions is necessary for autonomous driving. Furthermore, these controllers often depend on vehicle models that also need to adapt over time due to varying environments. This paper uses graphs to present novel techniques to learn the vehicle model and the lateral controller online. First, a heterogeneous graph is presented depicting the current states of and inputs to the vehicle. The vehicle model is then learned online using known physical constraints in conjunction with the processing of the graph through a Graph Neural Network structure. Next, another heterogeneous graph – depicting the transition from current to desired states – is processed through another Graph Neural Network structure to generate the steering command on the fly. Finally, the performance of this self-learning model-based lateral controller is evaluated and shown to be satisfactory on an open-source autonomous driving platform called CARLA.]]></description>
      <pubDate>Thu, 20 Nov 2025 17:06:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617957</guid>
    </item>
    <item>
      <title>Finite-Time Event-Triggered Containment Maneuvering of Marine Surface Vehicles With Tracking Error Constraints: Theory and Experiment</title>
      <link>https://trid.trb.org/View/2591846</link>
      <description><![CDATA[In this article, a novel class of containment maneuvering controllers is developed for a fleet of marine surface vehicles (MSVs) capable of tracking a parameterized path within finite time. Besides, the along-track errors and cross-track errors of parameterized line-of-sight (LOS) are constrained. For kinematic level, to address tracking error constraints and finite time convergence, the coordinated guidance laws are derived by developing LOS based tan-type barrier Lyapunov functions (BLFs), nonlinear tracking differentiator (NLTD) and finite-time theory. Specifically, a novel finite-time extended state observer (FTESO) is developed to estimate the uncertain kinematics. For coordination level, the finite-time switching threshold event-triggered containment control strategy for MSV is derived to reduce the updating frequency of the path variables containment controller. Theoretical analysis illustrates that all signals are ultimately uniformly bounded (UUB). Both simulation and experimental results are provided to verify the effective performance of the proposed containment maneuvering strategy.]]></description>
      <pubDate>Wed, 05 Nov 2025 10:02:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2591846</guid>
    </item>
    <item>
      <title>Robust attitude trajectory tracking control for a quadrotor under external disturbance</title>
      <link>https://trid.trb.org/View/2593965</link>
      <description><![CDATA[Attitude stability plays an important role in quadrotor aircraft. However, it is difficult to design a robust controller to precisely track a desired attitude trajectory in the presence of external disturbance. To address this problem, a nonlinear disturbance estimator with finite-time convergence is proposed to estimate external disturbance. Then, a dynamic surface control scheme based on the disturbance estimator is developed. Therefore, the compensation for external disturbance can be achieved in the designed controller. Furthermore, the L∞ performance of transient attitude tracking error is achieved by analyzing solution of Lyapunov function. The finite-time convergence of disturbance estimation error and the asymptotical convergence of attitude tracking error of closed-loop system are rigorously proved. Finally, the numerical simulations are carried out to demonstrate the effectiveness of the developed disturbance estimator and control scheme.]]></description>
      <pubDate>Wed, 29 Oct 2025 09:13:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2593965</guid>
    </item>
    <item>
      <title>Modified circular curve-based impact angle control guidance</title>
      <link>https://trid.trb.org/View/2593790</link>
      <description><![CDATA[To plan the path for unmanned aerial vehicles (UAVs) considering the impact angle and acceleration constraints, a novel geometric rule is proposed in this paper. Firstly, under the nonlinear engagement kinematics, the geometric rule is derived from the modified circular curve including two parameters, one for the convergence of the acceleration, the other for extending the flight envelope. Furthermore, the convergence of the nominal guidance law developed from the geometric rule is rigorously proved. Secondly, to eliminate the angle tracking error regardless of the initial error size, a fixed-time convergent controller is adopted to formulate the impact angle control guidance law. Moreover, the implementation of the proposed guidance law only requires angle information, which can be readily obtained by the on-board device. The proposed guidance law is also applicable for varying-speed UAVs and moving targets. The simulation results and the comparative studies demonstrate the validity and superiority of the proposed guidance law.]]></description>
      <pubDate>Wed, 29 Oct 2025 09:13:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2593790</guid>
    </item>
    <item>
      <title>Digital robust nonlinear controller for position and attitude stabilization of an autonomous quadrotor unmanned aerial vehicle</title>
      <link>https://trid.trb.org/View/2593789</link>
      <description><![CDATA[Unmanned aerial vehicles, which are aircraft without human pilots or passengers on board, expanded to many applications as digital control technologies improved and costs fell. However, the highly nonlinear dynamics in these systems cause traditional methods to be inadequate, so the importance of nonlinear control methods for these systems has increased. This study focuses on nonlinear stabilizing control of an autonomous quadrotor UAV based on an approximate dynamical model. Position and attitude digital controllers are designed using a backstepping controller with integral action directly in the discrete-time domain. Firstly, the discretized dynamics of the quadrotor are introduced. Due to the underactuated structure of autonomous quadrotor UAVs, the digital controllers are designed to track the altitude positions and yaw angle of the quadrotor to their reference trajectories, while also stabilizing the pitch and roll angles. In the design procedure, position controllers automatically generate the desired trajectories of pitch and roll angles. Robustness against parametric deviations, unmodeled dynamics, and external disturbances is achieved with integral action, thus ensuring an offset-less steady-state response. The asymptotic stability of the closed-loop system under the proposed controllers is demonstrated according to Lyapunov theory. Detailed simulation results, along with comparative studies, are presented to illustrate the effectiveness and feasibility of the proposed controller.]]></description>
      <pubDate>Wed, 29 Oct 2025 09:13:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2593789</guid>
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