<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>Model-Free Speed Tracking Control for Automated Cars</title>
      <link>https://trid.trb.org/View/2685727</link>
      <description><![CDATA[Ensuring that longitudinal control in autonomous driving is accurate, robust, and smooth is key to enhance vehicle autonomy and reduce driver intervention, improving user acceptance of autonomous vehicles. Vehicles have complex dynamics that make accurately following the speed reference in various driving situations a challenging task. Model-Free Control (MFC) has shown its performance and robustness in systems which are difficult to model or with time-varying dynamics, making it relevant for this application. In this paper, a cascade control architecture based on MFC is proposed. This strategy keeps the MFC principle of simplicity in control while, due to the cascade structure, using all the information generated by the motion planner and the measured speed and acceleration, which are easy to obtain. Regulators with this structure have been systematically designed to keep the tracking quality, safety and passenger comfort in a wide variety of driving situations.These regulators have been evaluated both in simulation and real-world scenarios, showing improvements in robustness and performance when compared with the baseline.]]></description>
      <pubDate>Thu, 13 Aug 2026 17:00:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2685727</guid>
    </item>
    <item>
      <title>From Safety-I to Safety-II: Longitudinal Control Strategies for Autonomous Vehicles Based on Risk Homeostasis Theory</title>
      <link>https://trid.trb.org/View/2702258</link>
      <description><![CDATA[Although Safety-Ⅰ measures can help autonomous vehicles mitigate risks, they often introduce new secondary risks. For this reason, current autonomous vehicles (AVs) frequently face a dilemma between preventing rear-end collisions and avoiding being rear-ended. To tackle this, this study proposes a Safety-II category Steady-state Risk Control (SRC) strategy to ensure safety resilience in longitudinal control and enhance adaptability within mixed traffic flows. The strategy indirectly characterizes risk by evaluating drivers' risk prevention capabilities, moving beyond reliance on traditional traffic conflict techniques. It further explores drivers’ adaptive mechanisms to uncertainty through steady-state risk theory, introducing a Safety-II paradigm for longitudinal control in autonomous driving. Under this framework, longitudinal trajectories are planned based on personalized risk anticipation, and an SRC strategy is developed using a linear time-varying model predictive control algorithm. By replacing conventional Safety-I boundary constraints on state and control variables, SRC employs an efficient analytical solution. Finally, simulation experiments are conducted with variables such as the lead vehicle’s speed range and emergency braking deceleration, using the autonomous emergency braking (AEB) system as the representative implementation of the Safety-I category for comparison. Results show that while the AEB system’s collision rate reaches 41.67%, the SRC strategy consistently achieves safe, smooth, and reliable braking. Additionally, SRC effectively reduces the deceleration required when following human-driven vehicles, substantially lowering the risk of rear-end collisions and improving coordination within mixed traffic flows.]]></description>
      <pubDate>Fri, 07 Aug 2026 09:21:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2702258</guid>
    </item>
    <item>
      <title>Study on the influence mechanism of elastic appendage on the longitudinal stability of high-speed planing craft</title>
      <link>https://trid.trb.org/View/2737454</link>
      <description><![CDATA[Aiming at the problem of porpoising during high-speed navigation of planing crafts, this study designed a localized elastic appendage installed in the high-pressure area of the boat bottom, which can improve the longitudinal stability of the planing craft through its passive deformation. The ship model towing test and numerical simulation of rigid model (RM) and local elastic model (LEM) are carried out under still water conditions. The flow field calculation of RM based on CFD and the two-way fluid-structure interaction simulation of LEM based on CFD-FEM are all in good agreement with the experimental data. The test and numerical results indicate that in the speed range of 1–11 m/s, the maximum deformation of the LEM elastic appendage can approach 4 mm. There is an obvious improvement in longitudinal stability following deformation. The stable speed range is increased by 1.2 times and the trim angle of LEM is reduced by a maximum of 15% when compared to RM at the same speed. In terms of resistance, RM and LEM exhibit almost identical characteristics. Based on the details of flow field and the pressure distribution obtained by the numerical simulation, the mechanical properties of the planing craft after the deformation of the local elastic appendage have been analyzed, which provides a new design idea for the planing craft to improve the longitudinal stability.]]></description>
      <pubDate>Thu, 06 Aug 2026 16:31:01 GMT</pubDate>
      <guid>https://trid.trb.org/View/2737454</guid>
    </item>
    <item>
      <title>Predictive Iterative Learning Control for High-Precision Longitudinal Coordination of Vehicular Platoons</title>
      <link>https://trid.trb.org/View/2731571</link>
      <description><![CDATA[This paper addresses the longitudinal control problem of autonomous vehicular platoons, with the objective of achieving high-precision coordination through predictive iterative learning control (PILC). The proposed PILC method integrates past coordination experience and future predictions of the following vehicle to enhance control accuracy and accelerate learning convergence. Specifically, a super-lifted model is developed, and the convergence condition of the linear time-varying system is derived. The longitudinal platoon control problem is tackled by designing a PILC-based feedforward-feedback coordination controller. Simulation results demonstrate the fast convergence and robustness of PILC, while real-world experiments on a vehicular platoon platform validate its superior precision compared with baseline platoon controllers.]]></description>
      <pubDate>Thu, 30 Jul 2026 16:36:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2731571</guid>
    </item>
    <item>
      <title>Classification of Longitudinal Driving Events Using Vehicle Response Signals for Profiling Driving Behaviors</title>
      <link>https://trid.trb.org/View/2671532</link>
      <description><![CDATA[Driving behavior profiling (DBP) involves evaluating driving patterns to determine a safety score for drivers. Proper classification of driving events increases the accuracy of profiling driving behaviors. Most driving event classification models consider lateral driving events, such as turning and lane changes. In heavy traffic conditions, it is impossible to perform lateral events independent of the position of other vehicles. This research aims to develop a model for classifying longitudinal driving events (acceleration and braking) and nonevents using vehicle response signals. To develop the model, naturalistic driving data were collected using a passenger car on a 19 km road stretch. Vehicle response signals were collected using Inertial Measurement Unit (IMU) sensors fixed on the test vehicle with a frequency of approximately 200 Hz with timestamps. The driver’s pedal operation was also captured with timestamps using a camera to map the ground truth labels with vehicle response signals. The data were collected from 5 drivers, totaling a dataset for approximately 190 km. The start and end times of all 634 events (444 driving events and 190 nonevents) were used to label the driving events in the IMU sensor data. These labeled driving events were split into the train (476 events) and test (158 events) datasets. Hidden Markov Model (HMM) algorithm was used to develop classification models for the driving events. The models were developed for various combinations of accelerations using the training dataset. The accuracy of these models was then compared to a test dataset. The models achieved 90.99% and 77.08% accuracy, respectively, in classifying events and nonevents using data from the accelerometer.]]></description>
      <pubDate>Mon, 27 Jul 2026 11:16:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2671532</guid>
    </item>
    <item>
      <title>Risk-Quantification Based Longitudinal Planning Model for Connected and Autonomous Vehicles</title>
      <link>https://trid.trb.org/View/2730752</link>
      <description><![CDATA[Current car-following models, mostly developed under the premise of simulating human driver behavior, struggle to fully meet the requirements for efficiency, safety, and stability of Connected and Autonomous Vehicles (CAVs). Therefore, this study introduces a risk-quantification based longitudinal planning model (RQM) hinged upon a novel omnidirectional risk indicator. Depending on the preceding vehicles state, the RQM exhibits distinct driving modes, with different parameters influencing its behavior and macroscopic characteristics. Besides, based on the linearized approximation, theoretical analysis confirms that the RQM can ensure overdamped properties and local string stability while maintaining comparable or even higher traffic capacity than existing models. Furthermore, various numerical experiments are designed to test the properties of the RQM and validate the theoretical analysis. Braking tests demonstrate that RQM consistently enables safe emergency braking under diverse velocity conditions while maintaining both overdamped behavior and string stability. Comparative experiments with the similarly traffic-capable Intelligent Driver Model (IDM) and Adaptive Cruise Control (ACC) further prove that vehicle platoon controlled by RQM can swiftly and efficiently dissipate oscillations originating downstream in both time and spatial dimensions. RQM proposed in this study offers a significant enhancement in stability and capacity over traditional car-following models while ensuring safety. It provides a highly stable model-driven longitudinal planning method for CAVs while preserving their traffic efficiency.]]></description>
      <pubDate>Mon, 27 Jul 2026 11:16:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2730752</guid>
    </item>
    <item>
      <title>Hybrid of Neural Network and Physics-Based Estimator for Vehicle Longitudinal Dynamics Modeling Using Limited Driving Data</title>
      <link>https://trid.trb.org/View/2617816</link>
      <description><![CDATA[An accurate longitudinal dynamics model is essential for state estimation and control of autonomous vehicles. However, existing physical models suffer from limited working conditions and large errors, while pure data-driven models require massive amounts of driving data to cover working conditions fully. To address these issues, a hybrid architecture composed of a neural network-based traction model, a recursive least square-based parameter estimator, and a physics-based dynamics model is proposed for longitudinal dynamics modeling, in which the parameter estimator is used for mass and modified rolling friction coefficient estimation. Under this architecture, the longitudinal dynamics model can be established using limited driving data collected on a test field with a given load, and achieve precise vehicle dynamics characterization under various roads and loads. To design the neural network for traction description, the dynamics of vehicle powertrain and braking systems are analyzed, and a physics-guided neural network, which fully considers the traction transmission characteristics, is formulated. For model training, a two-stage hybrid model training method is proposed, which can train the hybrid model with the co-existence of unknown network and physical parameters. Results demonstrate that the proposed hybrid model can realize accurate parameter estimation and vehicle longitudinal dynamics modeling using limited driving data collected at a test field under a given load, especially with excellent generalization performance under different loads and roads.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617816</guid>
    </item>
    <item>
      <title>Perception-Based Feedback Tracking via Scalarized Pixel Data of Visual Frames</title>
      <link>https://trid.trb.org/View/2658988</link>
      <description><![CDATA[Perception-based control has gained increasing attention due to its potential in autonomous systems. However, effectively integrating high-dimensional data from perception sensors, such as RGB cameras, into control design remains challenging. This paper investigates a car-following longitudinal control problem in autonomous driving, where a following car equipped with an RGB camera senses visual information from a leading car. To address the complexities of data integration, we propose a Binarized Digital Image Pixels Mask (BDIPM) method integrated with a backstepping control framework. The BDIPM method enhances useful visual pixels while filtering out irrelevant signals, enabling the scalarization of high-dimensional image data into a format suitable for control design with rigorous analysis footing. By constructing an error model that maps image-derived features to real-state variables, we establish a sensory pixel-based feedback control law. CARLA results demonstrate the effectiveness of the proposed approach. Compared to the baseline methods, our algorithm shows improvement in both stability and computational speed, which highlights its potential for robust perception-driven control in autonomous driving scenarios.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658988</guid>
    </item>
    <item>
      <title>Linking string stability and traffic hysteresis: implications for oscillation absorption in vehicle longitudinal control</title>
      <link>https://trid.trb.org/View/2667131</link>
      <description><![CDATA[While the fundamental mechanisms of traffic flow remain elusive—especially under non-steady or oscillatory conditions—existing research has shown that Automated Vehicles (AVs) have the potential to suppress traffic oscillations. However, this often depends on aggressive deceleration and large headways, raising concerns about real-world applicability. To address this gap, this study theoretically analyzes the relationship between traffic hysteresis and string stability in AV control laws. A concise condition is derived to determine the direction of traffic hysteresis, offering a practical indicator of control law stability. Based on this, we propose the Hysteretic Parameter Adaptation Framework (HyPAF), which converts time-invariant control laws into time-varying systems. HyPAF reduces oscillation propagation by slowing ego vehicle responses to upstream fluctuations, while constraining parameter adjustments within stability-preserving bounds. Simulation results demonstrate that the relationship identified in this study between traffic hysteresis and string stability depends solely on the control law, implying that time delays simultaneously influence both phenomena. Moreover, simulations confirm the theoretical findings and show that, under both periodic oscillations and real-world trajectories, HyPAF can significantly reduce oscillation amplitudes and reduces energy consumption, at the cost of a slight increase in risk that remains within acceptable safety margins. These findings offer new insights into the evolution of non-equilibrium traffic flow, provide a practical guideline for parameter tuning in vehicle control systems, and may offer a new perspective for understanding human driving behavior under non-steady conditions.]]></description>
      <pubDate>Tue, 26 May 2026 09:40:58 GMT</pubDate>
      <guid>https://trid.trb.org/View/2667131</guid>
    </item>
    <item>
      <title>Direct Robust Adaptive Tracking Control of Electric Vehicles Based on Radial Basis Function Neural Networks</title>
      <link>https://trid.trb.org/View/2694334</link>
      <description><![CDATA[This paper presents a direct robust adaptive tracking control strategy for the Iongitudinal motion of electric vehicles (EVs) subject to parametric uncertainties, nonlinear dynamics, and external disturbances. The vehicle longitudinal dynamics are formulated as a second-order nonlinear system with unknown nonlinearities. Unlike conventional indirect adaptive approaches that first identify unknown system dynamics, a radial basis function neural network (RBFNN) is employed to directly approximate the ideal feedback control law derived from sliding mode theory and Lyapunov synthesis. A robust adaptive law incorporating [Formula: see text]-modification is designed for online neural network weight update, enhancing robustness against approximation errors and bounded disturbances without requiring prior knowledge of their bounds. Lyapunov-based stability analysis rigorously demonstrates that all closed-loop signals are uniformly ultimately bounded (UUB), with tracking error converging to a tunable residual set around zero. The controller achieves model-independent operation, requiring no exact knowledge of vehicle nonlinear dynamics. Comprehensive simulations under step commands, and multi-frequency trajectories, together with parametric variations and road grade disturbances, validate the effectiveness of the proposed scheme in achieving accurate velocity tracking and superior robustness compared to conventional PID and sliding mode control. The main source code of this paper, including all simulation scripts and neural network modules, can support information found in S1 Pdf file.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2694334</guid>
    </item>
    <item>
      <title>Fixed-Time Sliding-Mode Lateral-Longitudinal Control for Vehicle Platoon With Strict Lane Constraints and Recoverable Spacing Policy</title>
      <link>https://trid.trb.org/View/2617687</link>
      <description><![CDATA[This paper investigates the lateral and longitudinal platoon control problem under user-specified lane and inter-vehicle spacing constraints. By modeling in the Frenét Frame, the lateral and longitudinal movements of the vehicles are decomposed. A novel lateral control strategy is proposed to strictly enforce a preset lane departure accuracy by transforming lane constraints into heading angle constraints. In the longitudinal control, considering the presence of a non-ideal leading vehicle, a prescribed performance controller is designed to regulate its velocity. Additionally, a longitudinal control strategy based on a double-ended smooth transition function is proposed to mitigate the effects of non-zero initial errors and restore the standard constant time headway policy after a preset time. To guarantee practical fixed-time stability, a continuous variable exponent coefficient fixed-time sliding surface is constructed, and adaptive sliding mode controllers are designed. The effectiveness of the proposed method is validated through both simulations and experiments. The source code is available on GitHub (https://github.com/Mudianrui/FSC-VPuLLC.git) to support further research on lateral-longitudinal platoon control.]]></description>
      <pubDate>Tue, 24 Mar 2026 17:01:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617687</guid>
    </item>
    <item>
      <title>Vehicle Longitudinal Stochastic Control for Connected and Automated Vehicle Platooning in Highway Systems</title>
      <link>https://trid.trb.org/View/2591127</link>
      <description><![CDATA[The vehicle platoon control for highway traffic can help to improve traffic flow efficiency, enhance traffic safety, and reduce fuel consumption. In previous platoon control research, most of the driving behaviors are described using deterministic car-following models. Nevertheless, random factors that may come from the vehicle power train and additional stimuli, can have a greater impact on platoon control in highway traffic. In this paper, a novel stochastic control method is proposed based on a stochastic car-following model which considers microscopic driving behavior. Firstly, the stochastic car-following model is designed that fully accounts for the impact of random factors on platoon control. Secondly, an optimal objective function is constructed and the Hamilton-Jacobi-Bellman equation is used to solve this stochastic control problem, thereby completing the upper-level controller design and obtaining the optimal desired acceleration of the vehicle. Thirdly, the stochastic stability method is applied to analyze the proposed model and obtain the stochastic stability conditions satisfied by the model. Finally, tests are conducted for three different scenarios: stable speed, acceleration, and deceleration with additive and multiplicative noise, as well as the case where the lead vehicle’s speed is based on real vehicle trajectory data. These tests validate the stability and effectiveness of the stochastic car-following model predictive control method from the perspective of control strategy and model respectively. The experimental results show that under the stable parameter conditions of the model, the connected and automated vehicle platoon can achieve accurate speed tracking and maintain an appropriate safe distance in highway system.]]></description>
      <pubDate>Fri, 20 Mar 2026 14:10:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2591127</guid>
    </item>
    <item>
      <title>Formal Modeling and Synthesis of Longitudinal Dynamics Controller for Train Platoons</title>
      <link>https://trid.trb.org/View/2591244</link>
      <description><![CDATA[Train platoons are a really innovative application for future railways, in which trains are virtually-coupled via Train-Train communication, so drastically reducing their headways and increasing line capacity. However, as the spacing between trains becomes closer, the influence of disturbances from preceding trains becomes more significant and cannot be ignored. This influence can have a substantial impact on the operational behavior of the following trains, leading to fluctuating spacing within the platoon and potentially compromising the safety and stability of the train spacing. This paper introduces a formalized modeling framework and a controller synthesis method for train platoons, aiming to achieve absolute safety and stable operation in train platoon operations. The framework utilizes Hybrid Priced Two-player Timed Game Automaton (HPTTGA) to describe train platoon dynamics and synthesizes controllers that satisfy safety requirements using an On-the-fly Controller Synthesis (OCS) algorithm, where the effectiveness of the algorithm is demonstrated through a two-train tracking example, and Q-learning is applied to select controller which satisfy local and string stability. Owing to model-based feature, the proposed method is compared to classical Model Predictive Control (MPC) in terms of safety, local stability, and string stability. Experimental results demonstrate that the proposed method consistently ensures train platoon safety and achieves similar control performance to MPC in extreme operating scenarios. Moreover, under velocity limit scenarios, the proposed method successfully achieves the desired following behavior of the trailing train according to the control objectives.]]></description>
      <pubDate>Fri, 20 Feb 2026 09:03:47 GMT</pubDate>
      <guid>https://trid.trb.org/View/2591244</guid>
    </item>
    <item>
      <title>Integration of braking control systems in electric vehicles with active safety and driver-assistance technologies</title>
      <link>https://trid.trb.org/View/2620536</link>
      <description><![CDATA[Technology and science keep improving, so cars get more competent and use more electricity. To keep up, they need better brakes. A longitudinal dynamics control system for cars was shown to speed them up and improve the stopping system. The motor and power make this setup work. The electric mechanical stopping system also improved when noise control was added. The electric mechanical stopping system can get the holding force it needs in 0.01 seconds when self-disturbance rejection control and proportional integral differential control are used together. The proportional integral differential system doesn't work well in that time range because it has too much power. This means that the binding force stays the same. Based on what was shown above, the car's longitudinal dynamics control system, works quickly and satisfactorily. This keeps the vehicle from going too fast, and it also helps you drive better in general.]]></description>
      <pubDate>Tue, 17 Feb 2026 13:12:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2620536</guid>
    </item>
    <item>
      <title>An Explainable Q-Learning Method for Longitudinal Control of Autonomous Vehicles</title>
      <link>https://trid.trb.org/View/2553289</link>
      <description><![CDATA[Various artificial intelligence (AI) algorithms have been developed for autonomous vehicles (AVs) to support environmental perception, decision making and automated driving in real-world scenarios. Existing AI methods, such as deep learning and deep reinforcement learning, have been criticized due to their black box nature. Explainable AI technologies are important for assisting users in understanding vehicle behaviors to ensure that users trust, accept, and rely on AI devices. In this paper, an explainable Q-learning method for AV longitudinal control is proposed. First, AI control of AVs is realized by constructing a deep Q-network (DQN) with an intelligent driver model, with the control objective maximizing vehicle speed while preventing collisions. Then, a deep explainer for humans is developed via a Shapley additive explanation (SHAP), and a novel positive SHAP method that defines new base values is proposed to explain how individual state features contribute to decisions. Finally, statistical analyses and intuitive explanations are quantified based on SHAP tools to improve clarity. Elaborate numerical simulations are conducted to demonstrate the effectiveness of the proposed algorithm. The code is available at https://github.com/limeng-1234/Pos_Shap.]]></description>
      <pubDate>Mon, 26 Jan 2026 14:17:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2553289</guid>
    </item>
  </channel>
</rss>