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    <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>
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    <item>
      <title>Assessing the Reliability of Hard Braking and Other Vehicle-Based Metrics as Surrogate Safety Measures</title>
      <link>https://trid.trb.org/View/2712175</link>
      <description><![CDATA[Transportation safety is a critical concern for agencies nationwide, and recent technological advances have enabled the collection of vast amounts of vehicle trajectory and behavioral data. Among these data, hard-braking events have emerged as a surrogate safety measure, which can be used to infer the possibility of near misses and crashes. Agencies and data providers increasingly rely on metrics such as hard braking, excessive acceleration, and high-speed cornering, derived from connected vehicle (CV) data, to identify risky driving behavior and to proactively address safety risks.

Unlike traditional crash data, which are retrospective and often delayed, surrogate safety measures offer real-time insights into roadway conditions and driver behavior. This immediacy allows for earlier identification of emerging safety concerns and the implementation of timely countermeasures. However, the reliability and validity of these surrogate measures are not universally established. Their effectiveness can vary depending on factors such as sight distance, geometric design, speed limits, traffic control devices, work zone configurations, and queue warning locations. For instance, a hard-braking event at a congested urban intersection may indicate different risks than one on a rural freeway.

The objective of this research is to rigorously evaluate the reliability and validity of hard braking and other vehicle-based metrics, such as excessive acceleration and high-speed cornering, as surrogate safety measures across diverse transportation environments. ]]></description>
      <pubDate>Tue, 09 Jun 2026 13:01:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2712175</guid>
    </item>
    <item>
      <title>Global Coordinated Control Framework for Advanced All-Wheel Steering and Driving Vehicles: Unleashing Potential through Tire Slip State Assessment</title>
      <link>https://trid.trb.org/View/2703762</link>
      <description><![CDATA[To address the challenge of efficient actuator coordination in all-wheel steering and driving vehicles, this paper proposes a global coordinated control framework based on tire slip state assessment. First, a hybrid feedforward control method comprising steady-state control, dynamic compensation, and oblique steering compensation is proposed to respond rapidly to the driver’s demands under various operating conditions. Then, considering different steering modes of four-wheel steering vehicles, a driver intention interpretation method that integrates conventional steering and oblique steering is developed. Subsequently, a sliding mode control algorithm is utilized to track the driver’s desired motion states, improving the vehicle’s robustness against system disturbances. Moreover, taking lateral acceleration and yaw rate as inputs, a coordinated strategy for the four-wheel steering angles and driving torques is established based on tire slip state assessment. Finally, hardware-in-the-loop test results show that, compared to the model predictive control (MPC) algorithm, the proposed control scheme increases the maximum speed in double lane-change maneuvers by 13%, significantly improving the vehicle handling performance under different operating conditions.]]></description>
      <pubDate>Thu, 04 Jun 2026 15:13:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2703762</guid>
    </item>
    <item>
      <title>Energy-Transmissibility-Based Evaluation of Straight-Line Stability and Steering Components</title>
      <link>https://trid.trb.org/View/2674985</link>
      <description><![CDATA[This study presents a method for evaluating and designing vehicle straight-line stability and steering performance using an energy transmissibility model that incorporates in-plane and roll dynamics. First, the influence of crosswind-induced roll disturbances on straight-line stability is analyzed for different steering characteristics. Then, the effect of hub bearing friction is modeled as a resistive moment in the un-sprung mass system. Its influence on yaw and roll responses is examined through energy transmissibility, demonstrating how friction tuning can enhance the steering response while balancing stability.]]></description>
      <pubDate>Thu, 04 Jun 2026 11:57:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2674985</guid>
    </item>
    <item>
      <title>Reliability-Based Vibration Design of Vehicle Systems with Tuned Mass Dampers (TMD)</title>
      <link>https://trid.trb.org/View/2691884</link>
      <description><![CDATA[Tuned Mass Dampers (TMDs) are widely used in the automotive industry to mitigate Noise, Vibration, and Harshness (NVH) issues across various vehicle systems. These passive devices are particularly effective in reducing structural vibrations in components subjected to resonant excitation. However, real-world applications often face challenges due to manufacturing variability and system-level build differences, which can cause deviations in both the TMD’s tuned frequency (up to ±15%) and the vibration characteristics of the host structure. These uncertainties—in both the TMD properties and the vehicle subsystem dynamics—can be modeled using statistical distributions. This paper presents a generalized methodology for vibration analysis and design under uncertainty, combining reliability engineering with dynamic vibration modeling. The approach formulates a unified mathematical framework that incorporates probabilistic and stochastic modeling to assess TMD performance under a range of build and environmental conditions. As a case study, the method is applied to assess steering column vibrations, with a focus on quantifying the probability that system performance meets specified NVH targets. Multiple statistical distribution models are considered to predict the likelihood that vibrations exceed customer acceptance thresholds, potentially leading to unfavorable subjective and objective ratings. The results are validated using population-level vehicle data. While demonstrated on the steering system, the proposed methodology is applicable to any vehicle subsystem equipped with a TMD, provided that the relevant random variables—such as modal properties, excitation inputs, and build tolerances—are properly characterized. This enables robust TMD design across vehicle domains, ensuring performance consistency despite system variability.]]></description>
      <pubDate>Wed, 03 Jun 2026 09:07:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691884</guid>
    </item>
    <item>
      <title>Data-Driven Feedforward Compensation-Based Trajectory Tracking Control</title>
      <link>https://trid.trb.org/View/2691829</link>
      <description><![CDATA[Advanced autonomous driving is a critical component in the intelligent development of new-generation electric vehicles. Research on reliable chassis control algorithms ensures the safety and stability of autonomous vehicles during operation. To enhance the control performance of autonomous vehicles and improve the accuracy of trajectory tracking, this paper proposes a data-driven feedforward compensation trajectory tracking control approach. By optimizing the design of the feedforward compensation loop, systematic errors and latency in the vehicle’s steering system are mitigated, thereby enhancing the precision and robustness of the control algorithm. Initially, the paper analyzes the control errors present when the vehicle responds to controller commands. Subsequently, the paper focuses on the steering angle errors in trajectory tracking, identifying and analyzing the most relevant factors. A time-delay neural network (TDNN) based on data-driven principles is designed to model and predict these errors. This network captures the temporal characteristics of steering angle errors, enabling accurate predictions. Finally, the feedforward controller compensates for prediction errors by integrating feedforward compensation with the Model Predictive Control (MPC) controller’s predictions. This approach enables high-precision trajectory tracking by delivering precise control inputs. Experimental results demonstrate that the data-driven feedforward compensation control algorithm reduced trajectory tracking error by approximately 19% in co-simulations using Matlab/Simulink and Carsim, and by 56% in real-vehicle tests, thereby validating the effectiveness of the proposed approach.]]></description>
      <pubDate>Wed, 03 Jun 2026 09:07:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691829</guid>
    </item>
    <item>
      <title>Integrated Levitation–Propulsion–Guidance Implementation and Operation Control for Active-Steering Maglev Car</title>
      <link>https://trid.trb.org/View/2665500</link>
      <description><![CDATA[To achieve integrated levitation–propulsion–guidance and precise longitudinal, lateral, and yaw motion control for the active-steering maglev car with permanent magnet electrodynamic wheels (PMEDWs), this article proposes a lateral guidance force compensation scheme and a multimode operation control method based on deflected PMEDWs. First, the triaxial electromagnetic forces are regulated through precise control of the PMEDW’s rotational speed and deflection angle, realizing the integrated levitation–propulsion–guidance functionality. Second, the vehicle’s longitudinal, lateral, and yaw operation modes based on the PMEDWs’ differential mode and deflection mode are proposed, followed by corresponding dynamics analyses. Finally, a dual-loop cascade controller is designed for vehicle motion control, and a prototype with its experimental platform is developed for validation. Experimental results demonstrate that the active-steering maglev car can effectively realize forward/backward motion, lateral translation, and in situ steering under multimodal operation control. Specifically, compared to the PMEDWs’ differential mode, the deflection mode significantly enhances longitudinal and yaw operating control performance: the average settling time and overshoot for longitudinal motion are reduced by 43.48% and 36.01%, respectively, while those for yaw motion are reduced by 37.42% and 26.04%, which verifies the effectiveness of the deflection mode and the controller.]]></description>
      <pubDate>Fri, 29 May 2026 14:09:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2665500</guid>
    </item>
    <item>
      <title>Multi-Kernel Enhanced Receding-Horizon Reinforcement Learning for Steering Control of Intelligent Vehicles</title>
      <link>https://trid.trb.org/View/2658888</link>
      <description><![CDATA[Achieving optimal control in the path-tracking of intelligent vehicles is crucial for enhancing driving performance, yet it remains challenging due to model uncertainties and nonlinear dynamics. Reinforcement learning (RL), as a class of approximated optimal control methods, has gained attention for tackling complex problems with uncertain or nonlinear dynamics. However, effective feature representation and online learning efficiency are two major issues that persist in RL methods for adaptive optimal control. To address these challenges, this paper proposes a Receding-horizon Multi-kernel Reinforcement Learning (RM-RL) algorithm, which integrates an efficient online learning mechanism with improved feature representations. RM-RL operates within a receding-horizon control framework that facilitates online policy optimization and deployment. Meanwhile, employing a multi-kernel features learning approach for the actor-critic structure and the dynamics model further improves learning efficiency and generalization. Besides, the convergence and closed-loop stability are analyzed in depth. Real-world experiments conducted on the Hongqi E-HS3 vehicle and simulations on the CarSim platform demonstrate the effectiveness and the superiority of RM-RL over advanced comparative methods.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658888</guid>
    </item>
    <item>
      <title>Assessing the Effectiveness of a Dynamic Driving Simulator for ADAS Development—A Back-to-Back Subjective Validation Study on Lateral Guidance Systems</title>
      <link>https://trid.trb.org/View/2658755</link>
      <description><![CDATA[Rapid developing driving simulator technologies enable the possibility of assisting the human-centered development of Advanced Driver Assistance Systems and Autonomous Driving. It boosts the efficiency of functional testing and professional subjective assessment of the system under test and thus greatly shortens the development cycles of ADAS functions. Furthermore, standardized and transparent assessment procedures contribute to the robustness and transferability of the test results. However, the application of driving simulators in the practical development process is based upon the validity of the test results in the virtual environment. The aim of this study is to determine the subjective validity of a high-fidelity dynamic driving simulator. A back-to-back study was designed to subjectively evaluate two lateral guidance systems on an objectively validated simulator and in a real vehicle respectively. The results show that the professional drivers evaluate the system characteristics similarly in the aspects of driver interaction, perceived safety and functional performance as well as in most of their sub-aspects in the virtual and in the physical test environment and that absolute subjective validity can be established. Although the intervention intensity of the lane departure avoidance system and the general reproducibility of the lane keeping assist system show significant differences between the test environments, relative subjective validity can also be confirmed. In addition, the results of regression analysis reveal the influencing factors of driver’s subjective evaluation of the three main system characteristics and confirm the effectiveness of the evaluation methods.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658755</guid>
    </item>
    <item>
      <title>MICH-Net: A Novel Deep Learning Architecture With African Fire Hawk Optimization for Steering Angle Prediction in an Advanced Driver Assistance System</title>
      <link>https://trid.trb.org/View/2658987</link>
      <description><![CDATA[Steering angle prediction plays a vital role in the control of Autonomous Vehicles (AVs) and has attracted significant interest from technology firms, transportation authorities, and researchers in the automotive industry. Various Deep Learning (DL) architectures have been employed to predict the steering angle under various driving conditions. An accurate prediction of steering angles is important in ensuring the vehicle is maintained in the corresponding lane. The major challenge in predicting the steering angle is various lane marking styles, heterogeneous road types, texture, color, lighting conditions, and so on. This issue can be solved by developing an effective DL model and maintaining the vehicle in the designated lane. In this paper, an African Fire Hawk Optimization-based hybrid Deep Learning (AFHO-hybrid DL) model is proposed, where the steering angle is predicted by a novel hybrid DL model, named as Multi-Input Control with Hierarchy Network (MICH-Net). For predicting the steering angle, this research considers the Driving footage under different road, weather, and illumination conditions as an input. The proposed MICH-Net is designed by combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) through the MICH layer. Moreover, the layer size is determined by the AFHO, which combines the African Vultures Optimization Algorithm (AVOA) and the Fire Hawk Optimizer (FHO). Additionally, AFHO-hybrid DL outperformed the existing techniques with a maximal accuracy of 93.1%, a maximal Positive Predictive Value (PPV) of 92.5%, a minimal Root Mean Square Error (RMSE) of 0.324, and a maximal f1-score of 92.2%, respectively.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658987</guid>
    </item>
    <item>
      <title>Measurement and modelling of driver learning of steering control during successive obstacle avoidance manoeuvres</title>
      <link>https://trid.trb.org/View/2673000</link>
      <description><![CDATA[Understanding of driver behaviour can provide invaluable insight for the design of vehicles and driver assistance systems. Most existing human driver models do not incorporate driver learning or the effect of a driver's confidence in their predictions of future states, both of which affect human-generated control actions. In this paper, human driver steering control is assessed based on experimental data, then a driver model is proposed which captures driver learning and is capable of reproducing a wide range of human control styles by the selection of appropriate parameters. The driver model learns an internal model of the vehicle dynamics from experience, using a Gaussian Process, then selects control actions via Model Predictive Control. The results verify the model's capacity to capture the learning drivers achieve over time and to replicate various observed cautious and adventurous behaviours.]]></description>
      <pubDate>Wed, 27 May 2026 13:10:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2673000</guid>
    </item>
    <item>
      <title>Analysis of Cogging Torque Under Eccentricity of PMSMs for EV Steer-by-Wire System</title>
      <link>https://trid.trb.org/View/2659222</link>
      <description><![CDATA[Cogging torque is an inherent characteristic of permanent magnet synchronous machines (PMSMs). Eccentricity caused by manufacturing errors and assembly deviations would aggravate cogging torque, further deteriorate control accuracy and torque ripple of electrical machine-based traction systems. In this article, an improved magnetic co-energy method based on the airgap field modulation theory (AFMT) is developed to investigate the effects of different eccentricity types on the cogging torque of a 22-pole/24-slot outer-rotor surface-mounted PMSM (OR-SPMSM) for electric vehicles (EVs) steer-by-wire systems. The proposed analytical model demonstrates that static eccentricity (SE) induces the specific orders of cogging torque associated with rotor pole multiples, whereas dynamic eccentricity (DE) introduces the orders related to stator slots multiples. For mixed eccentricity (ME), the orders of cogging torque not only contain the main harmonic orders of SE and DE but also modulate other additional characteristic harmonics.]]></description>
      <pubDate>Tue, 26 May 2026 11:56:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659222</guid>
    </item>
    <item>
      <title>Neural Network-Based Adaptive Nonsingular Fixed-Time Sliding Mode Control for Steer-by-Wire System Considering Dead-Zone Constraint</title>
      <link>https://trid.trb.org/View/2665464</link>
      <description><![CDATA[The steer-by-wire (SBW) system provides flexibility in vehicle design layout and steering ratio customization. However, the mechanical transmission components in the SBW system can cause a dead-zone effect, which degrades steering angle tracking performance and consequently affects steering maneuverability. To address this issue, a generalized regression neural network-based adaptive nonsingular fixed-time sliding mode control (GRNN-ANFSMC) method is proposed for the SBW system of vehicles. First, a dynamic model of the steering actuator considering input dead zone is established. Next, an improved fixed-time stable system is designed to achieve a faster convergence rate and a smoother convergence process. Building on this, a segmented sliding mode surface is designed to prevent singularity issues, and a reaching law with adaptive parameters is constructed to prevent chattering, which can enhance the dynamic response of the SBW system. Meanwhile, GRNN is introduced, and its weights are updated in real time by the designed adaptive law, which can effectively approximate and compensate for the dead zone of the SBW system within a fixed time. Finally, the effectiveness of the proposed method is validated by the SBW-equipped vehicle. The experimental results demonstrate that compared to other fixed-time sliding mode control (SMC), the proposed method can effectively reduce the angle tracking error and improve the dynamic response and robustness of the SBW system with a time-varying nonlinear dead zone.]]></description>
      <pubDate>Tue, 26 May 2026 11:56:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2665464</guid>
    </item>
    <item>
      <title>Safe trajectory planning and tracking for tractor semi-trailers in steering collision avoidance scenarios</title>
      <link>https://trid.trb.org/View/2680786</link>
      <description><![CDATA[Collision avoidance technology provides an effective solution for improving vehicle driving safety. However, current research primarily focuses on passenger cars and small commercial vehicles, neglecting tractor semi-trailers, which have elongated body lengths and complex dynamic characteristics. There is still a significant research gap in the application of collision avoidance control technology for tractor semi-trailers. To address this, this paper proposes a safe trajectory planning and tracking strategy for steering collision avoidance scenarios, aimed at enhancing the driving safety of tractor semi-trailers. To tackle the challenge of accurately predicting the pose of the semi-trailer during collision avoidance trajectory planning, a real-time trajectory planning method combining model predictive control and artificial potential field is proposed. Then, a tracking error model considering both lateral and yaw errors of the tractor semi-trailer is established to address the off-tracking phenomenon, and a linear quadratic regulator control strategy is proposed. Finally, static and dynamic collision avoidance scenarios are designed to validate the proposed strategy. Simulation and experimental results show that the proposed control strategy effectively ensures the safe collision avoidance maneuver of the tractor semi-trailer.]]></description>
      <pubDate>Wed, 20 May 2026 09:10:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680786</guid>
    </item>
    <item>
      <title>Black box identification of vehicle lateral dynamics using an automotive simulator</title>
      <link>https://trid.trb.org/View/2680782</link>
      <description><![CDATA[This article proposes a black box identification of vehicle lateral dynamics using the automotive simulation software VI-CarRealTime (VI-CRT). The model uses standard steering angle maneuvers commonly employed on test tracks, including Fishhook, Step Steer, Double Lane Change, Sweep Steer, and Sine, with Dwell as input data. Models were developed for two key variables in lateral dynamics analysis: the side-slip angle and yaw rate. The reference model used for correlation was derived from a parameterized VI-CRT automotive simulator based on post-processed experimental measurements from a real vehicle provided by the manufacturer Stellantis. The results demonstrated a significant correlation between linear models represented as transfer functions and the reference from the VI-CRT simulator.]]></description>
      <pubDate>Wed, 20 May 2026 09:10:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680782</guid>
    </item>
    <item>
      <title>An integrated control of AFS, DYC, and ASS for distributed electric driving commercial vehicle</title>
      <link>https://trid.trb.org/View/2680780</link>
      <description><![CDATA[To ensure commercial vehicle stability during trajectory tracking, this paper proposes a comprehensive stability control system based on stability analysis that incorporates Active Front Steering (AFS), Direct Yaw Moment Control (DYC), and Active Suspension System (ASS). A stability region for lateral speed and yaw rate is created by quantitatively describing the evolution behavior of the vehicle model using the Lyapunov exponent theory. Considering vehicle rollover stability, the yaw rate is further constrained based on the Load Transfer Ratio (LTR). To track vehicle dynamics in unstable regions, an upper-level controller called Model Predictive Control (MPC) calculates additional front wheel steering angles, yaw moments, and roll moments. A lower-level allocation controller determines the actual actuator commands, such as front wheel steering angle, additional hub motor torque, and additional vertical force. This control architecture ensures that the vehicle maintains lateral, yaw, and rollover stability. The feasibility of this approach is supported through co-simulations using TruckSim and MATLAB-Simulink, which demonstrates its efficacy in maintaining vehicle stability across multiple dimensions.]]></description>
      <pubDate>Wed, 20 May 2026 09:10:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2680780</guid>
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