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    <title>Transport Research International Documentation (TRID)</title>
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    <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>Unveiling traffic wave of linear adaptive cruise control: A second-order macroscopic traffic flow model</title>
      <link>https://trid.trb.org/View/2707984</link>
      <description><![CDATA[Traffic waves, the spatiotemporal propagation of congestion, are a key feature of traffic flow. As Adaptive Cruise Control (ACC) systems gain widespread adoption and show promise for improving both efficiency and safety, understanding how these waves evolve under ACC becomes increasingly important. Yet most existing analyses rely on steady-state metrics (e.g., equilibrium spacing) and neglect the ACC control-law parameters, such as feedback gains, that fundamentally shape higher-order traffic dynamics. To overcome this limitation, we embed the ACC control law directly into the momentum equation while retaining mass conservation law. The result is a higher-order macroscopic model whose dynamics are governed by a second-order partial differential equation equivalent to the linear ACC feedback law. Analyzing the flux Jacobian confirms that the system is strictly hyperbolic, thereby preserving anisotropy and ensuring physical consistency. The derivation also shows that traffic wave evolution depends on both the initial state and the ACC control parameters. We analyze wave-propagation characteristics, linear degeneracy, admissible discontinuities, and their connection to ACC string stability, with the corresponding derivations. Numerical experiments confirm that the second-order model yields markedly lower vehicle-pair speed deviations along wave paths than a first-order model subject to the same non-steady disturbances, underscoring both the necessity of a second-order treatment and the soundness of the proposed framework.]]></description>
      <pubDate>Wed, 12 Aug 2026 15:14:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2707984</guid>
    </item>
    <item>
      <title>Efficient tactical decision making for trucks in highway traffic with deep reinforcement learning</title>
      <link>https://trid.trb.org/View/2752045</link>
      <description><![CDATA[This thesis investigates tactical decision making for autonomous heavy-duty trucks in highway traffic using deep reinforcement learning, with a particular emphasis on optimizing safety, efficiency and costs. The key aspects of decision making include Adaptive Cruise Control (ACC) and lane changes, which strongly influence energy consumption, travel time, and traffic interactions. To support a systematic study of this problem, we develop a scalable traffic model on a simulation platform, providing a controlled and extensible environment for autonomous truck driving in multi-lane highways. We propose a hierarchical control architecture in which reinforcement learn ing is used for high-level tactical decision making, while low-level tactical actions are handled by physics-based controllers. This separation is found to improve the performance by reducing safety risks and facilitates the integra tion of learning-based decision making with established control methods. A realistic reward function is designed to jointly capture safety, efficiency, and operational costs, and advanced training strategies such as curriculum learning are investigated to handle conflicting objectives within a scalarized framework. We further explore a multi-objective reinforcement learning formulation to explicitly represent trade-offs between competing objectives, enabling the learning of interpretable Pareto frontiers. The results demonstrate that learning based tactical decision making policies can achieve meaningful trade-offs between safety and various operational costs in abstracted highway scenarios, and that multi-objective formulations provide valuable insight into the structure of these trade-offs. Overall, this work contributes to methodological foundations and evaluation tools for economically meaningful and extensible learning-based tactical decision making for heavy-duty trucks]]></description>
      <pubDate>Fri, 07 Aug 2026 08:36:07 GMT</pubDate>
      <guid>https://trid.trb.org/View/2752045</guid>
    </item>
    <item>
      <title>Challenges and advantages in implementing ADAS systems in buses: a broad overview (Desafios e vantagens na implementação de sistemas ADAS em ônibus: uma visão abrangente)</title>
      <link>https://trid.trb.org/View/2742668</link>
      <description><![CDATA[The implementation of ADAS in buses represents both a significant opportunity and a complex challenge for the future of urban mobility. While ADAS technologies such as lane departure warning, adaptive cruise control, blind spot detection, and autonomous emergency braking have been widely adopted in passenger cars and trucks, their integration into buses has been slower due to unique operational and safety concerns. This paper provides a broad overview of the advantages and obstacles associated with ADAS deployment in public transport vehicles, with particular emphasis on passenger safety, regulatory frameworks, and operational efficiency. Key barriers include the vulnerability of standing passengers during sudden braking events, the unpredictability of pedestrians and cyclists in dense urban environments, and the economic constraints faced by bus operators. At the same time, regulatory initiatives such as Transport for London’s Bus Safety Standard, the European Union’s General Safety Regulation, and Brazil’s MOVER program are driving the gradual adoption of these systems. The benefits of ADAS in buses extend beyond accident reduction, encompassing improved driver ergonomics, reduced fatigue, lower maintenance costs, and enhanced passenger comfort. Case studies from Europe, Brazil, and Asia highlight both the safety potential and the reluctance of drivers to fully embrace these technologies, often due to knowledge gaps and perceived inconvenience. The analysis underscores that successful implementation requires not only technological adaptation but also comprehensive driver training, infrastructure readiness, and public policy support. Ultimately, ADAS in buses should be understood as a transitional step toward autonomous mobility, offering immediate safety gains while reshaping the paradigm of urban transport.]]></description>
      <pubDate>Mon, 03 Aug 2026 15:49:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2742668</guid>
    </item>
    <item>
      <title>Generalized Multiphase Car-Following Model for Vehicles with Adaptive Cruise Control: Behavioral Dynamics and Experimental Validation for Electric Vehicles</title>
      <link>https://trid.trb.org/View/2688608</link>
      <description><![CDATA[Electric vehicles (EVs) are revolutionizing the transportation industry by offering sustainability benefits over internal combustion engine vehicles. Concurrently, most commercially available vehicles are equipped with advanced driver assistance systems (ADAS), such as adaptive cruise control (ACC). However, existing car-following models used in microsimulation are unable to adequately capture the unique driving dynamics of electric vehicles with ACC and their resulting impacts on overall traffic flow stability, throughput, and energy consumption. To address this gap, this study develops a generalized modeling approach for EV-ACC vehicles. The contributions include calibrating a novel electric vehicle model (EVM) and assessing the stability impacts of EV-ACC vehicles on the overall traffic flow. The results demonstrated the superiority of the EVM in representing the unique car-following behavior of EV-ACC vehicles compared with traditional car-following models. Furthermore, traffic simulations highlighted how EV-ACC vehicles can enhance traffic stability, throughput, and efficiency compared with internal combustion engine (ICE)-ACC vehicles.]]></description>
      <pubDate>Fri, 31 Jul 2026 09:23:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2688608</guid>
    </item>
    <item>
      <title>Cooperative Adaptive Cruise Control System Based on Interacting Multiple Model Cubature Kalman Filter and Robust Model Predictive Control</title>
      <link>https://trid.trb.org/View/2730952</link>
      <description><![CDATA[With the advancement of Internet of Vehicles technology, V2V-based cooperative adaptive cruise control (CACC) is poised to address variable traffic conditions. Considering the inevitability of communication failures, this paper introduces a robust CACC framework leveraging an on-board estimator, which integrates an interacting multiple model cubature Kalman filter (IMM-CKF) and a robust model predictive control (R-MPC) strategy with terminal constraints, designed to satisfy multiple driving requirements (safety, stability and driving experience). Due to the ill-conditioning calculations during estimation, the IMM method is introduced to tackle this issue. Meanwhile, the basic vehicle following model is enhanced to incorporate control signal increments and the safety concerns are managed through state constraints. Combined with H∞ control theory and linear matrix inequality (LMI) theory, a terminal invariant set is developed to ensure the stability of the MPC under constraints, and the stability of this strategy is demonstrated using a Lyapunov function. Moreover, building on existing research, we analyze the string stability of a CACC system, a general condition ensuring string stability is identified through preliminary experiments. Subsequent numerical experiment and simulations validate the outstanding performance of the R-MPC strategy against the tube-MPC strategy.]]></description>
      <pubDate>Thu, 30 Jul 2026 10:07:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2730952</guid>
    </item>
    <item>
      <title>Formation and Investigation of Cooperative Platooning at the Early Stage of Connected and Automated Vehicles Deployment</title>
      <link>https://trid.trb.org/View/2717723</link>
      <description><![CDATA[Cooperative platooning, enabled by cooperative adaptive cruise control (CACC), is a cornerstone technology for connected automated vehicles (CAVs), offering significant improvements in safety, comfort, and traffic efficiency over traditional adaptive cruise control (ACC). This paper addresses a key challenge in the initial deployment phase of CAVs: the limited benefits of cooperative platooning due to the sparse distribution of CAVs on the road. To overcome this limitation, the authors propose an innovative control framework that enhances cooperative platooning in mixed traffic environments. Two techniques are utilized: 1) a mixed cooperative platooning strategy that integrates CACC with unconnected vehicles (CACCu), and 2) a strategic lane-change decision model designed to facilitate safe and efficient lane changes for platoon formation. Additionally, a surrounding vehicle identification system is embedded in the framework to enable CAVs to effectively identify and select potential platooning leaders. Simulation studies across various CV market penetration rates (MPRs) show that incorporating CACCu systems significantly improves safety, comfort, and traffic efficiency compared to existing systems with only CACC and ACC systems, even at CV penetration as low as 10%. The maximized platoon formation increases by up to 24%, accompanied by an 11% reduction in acceleration and a 7% decrease in fuel consumption. Furthermore, the strategic lane-change model enhances CAV performance, achieving notable improvements between 6% and 60% CV penetration, without adversely affecting overall traffic flow.]]></description>
      <pubDate>Tue, 28 Jul 2026 15:25:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2717723</guid>
    </item>
    <item>
      <title>Cooperative Adaptive Cruise Control of Connected and Autonomous Vehicles Via Hybrid Iteration</title>
      <link>https://trid.trb.org/View/2727839</link>
      <description><![CDATA[This paper proposes a novel data-driven approach, named hybrid iteration (HI), for the implementation of cooperative adaptive cruise control (CACC) in connected and autonomous vehicles (CAVs). The proposed method leverages the integration of adaptive dynamic programming (ADP), internal model principle, and distributed control technique. By collecting input-state data, an approximately optimal control policy is synthesized through the online iterative technique, HI, to optimize the performance of CACC by minimizing a predefined cost function. Note that the HI preserves the benefits of two established learning algorithms: policy iteration (PI) and value iteration (VI). The learning process initiates with the VI algorithm, followed by the application of the PI algorithm to expedite convergence. HI eliminates the necessity for an initial stabilizing control policy, which is a prerequisite in PI, and exhibits a faster convergence rate than VI. The efficiency and safety of the HI method in CACC systems have been demonstrated through platoon vehicle testing and verification in traffic congestion and cut-in scenarios.]]></description>
      <pubDate>Mon, 27 Jul 2026 11:16:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2727839</guid>
    </item>
    <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>A Reinforcement Learning-Based Attack Generator for Testing the Security of Connected and Autonomous Vehicles</title>
      <link>https://trid.trb.org/View/2672800</link>
      <description><![CDATA[Connected and autonomous vehicles (CAVs) are employed to enhance the safety and efficiency of transportation systems. However, their connectivity makes them vulnerable to various types of attacks, such as false data injection (FDI) attacks, which can lead to unsafe scenarios. Therefore, there is a need to develop a framework to test and verify the security of CAVs under such attacks. Currently, researchers test the security of CAVs by generating manual and random attacks, but there is no automatic attack generation with proof of convergence. In this paper, for the first time, we look at testing and verification as a feedback control system problem with unknown dynamics. We introduce a novel centralized multi-agent attack generator designed to evaluate the safety and security of CAVs under FDI attacks. Our approach begins with the development of a verification signal to systematically assess the safety and performance of CAVs. We then design a reinforcement learning-based FDI attack generator, capable of estimating the unknown nonlinear relationship between the injected FDI attack and the verification signal, ultimately generating scenarios that compromise safety. A Lyapunov-based stability analysis is conducted to ensure the asymptotic convergence of the verification signal error. We employ the proposed reinforcement learning (RL)-based FDI attack generator on a cooperative adaptive cruise control (CACC) system as a case study, demonstrating its capability to create challenging and unsafe scenarios.]]></description>
      <pubDate>Wed, 24 Jun 2026 11:31:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2672800</guid>
    </item>
    <item>
      <title>Perceptions of and Experiences with Advanced Driver Assistance Systems Among New and Used Vehicle Purchasers, Renters, and Borrowers</title>
      <link>https://trid.trb.org/View/2709195</link>
      <description><![CDATA[Advanced Driver Assistance Systems (ADAS) comprise several different vehicle technologies that work independently and in concert to warn drivers of potential safety hazards, take action to prevent or mitigate a collision, or to provide continuous driving support. It is important for drivers of ADAS-equipped vehicles to understand these technologies and use them appropriately. Although used vehicles account for the majority of passenger vehicle sales in the United States, and people sometimes drive vehicles that they do not personally own, most research about user experiences with ADAS has focused explicitly or implicitly on owners of new vehicles. The current study sought to investigate understanding of and experiences with ADAS among individuals who drive ADAS-equipped vehicles of which they were not the original owner, including owners of used vehicles as well as renters and borrowers of vehicles equipped with ADAS. A web-based survey was administered to a convenience sample of drivers recruited online from a commercial crowdsourcing platform. The questionnaire investigated drivers’ general understanding of ADAS; experiences of purchasing, renting, or borrowing vehicles; and awareness and use of ADAS in vehicles that they had driven recently. Questions about ADAS focused mainly on two specific technologies: Adaptive Cruise Control (ACC) and Lane Centering Assistance (LCA). A total of 3,466 respondents completed the survey.]]></description>
      <pubDate>Thu, 11 Jun 2026 13:20:14 GMT</pubDate>
      <guid>https://trid.trb.org/View/2709195</guid>
    </item>
    <item>
      <title>Enhancing Traditional Adaptive Cruise Control through Map-Based Feature Integration: A Performance Evaluation Study on Stationary Vehicle Response at High Speeds</title>
      <link>https://trid.trb.org/View/2691846</link>
      <description><![CDATA[Adaptive Cruise Control (ACC) has become a widely adopted driver-assist technology, designed primarily to regulate a vehicle’s longitudinal movement while maintaining a safe following distance from the preceding vehicle. A key performance criterion is the system’s ability to detect and respond to both moving and stationary target vehicles within the ego vehicle’s path. While manufacturers typically validate ACC performance within specific speed ranges, responding to stationary objects remains particularly challenging due to limited sensor range, difficulty in detecting distant stationary targets, and constrained deceleration capabilities. Beyond certified operating limits, overall system reliability may degrade. Nonetheless, increasing industry and regulatory expectations are driving the need to extend ACC functionality across wider and more clearly defined speed domains. Modern ACC systems are further evolving to recognize and respond to various road features, including traffic lights, STOP signs, intersections, curved road segments, and roundabouts—an expanding set of scenarios enabled by multi-sensor fusion and map integration using standard definition (SD) and high definition (HD) maps. Regulatory frameworks are increasingly addressing these map-based functionalities. This paper investigates the interaction between map-based functionalities and traditional ACC behavior, specifically examining how map integration enhances ACC responsiveness to critical scenarios. Due to the wide variety of possible cases, this study focuses on stationary vehicle encounters, recognized as the most challenging and safety-critical scenario, particularly at higher speeds. Simulation studies are conducted to evaluate the impact of map-based augmentation on ACC performance, with results demonstrating performance improvements. For instance, at 50 mph on straight roads, the ego vehicle safely stopped ~4 meters from the target stationary vehicle using map-based anticipatory braking, compared to less than 1 meter with traditional ACC. These findings highlight the extended operational capability and safety benefits offered by the proposed approach, even beyond conventional speed limits.]]></description>
      <pubDate>Wed, 03 Jun 2026 09:07:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2691846</guid>
    </item>
    <item>
      <title>Human-Machine Shared Control Approach for the Takeover of Cooperative Adaptive Cruise Control</title>
      <link>https://trid.trb.org/View/2617777</link>
      <description><![CDATA[Cooperative Adaptive Cruise Control (CACC) often requires human takeover for tasks such as exiting a freeway. Direct human takeover can pose significant risks, especially given the close-following strategy employed by CACC, which might cause drivers to feel unsafe and execute hard braking, potentially leading to collisions. This research aims to develop a CACC takeover controller that ensures a smooth transition from automated to human control. The proposed CACC takeover maneuver employs an indirect human-machine shared control approach, modeled as a Stackelberg competition where the machine acts as the leader and the human as the follower. The machine guides the human to respond in a manner that aligns with the machine’s expectations, aiding in maintaining following stability. Additionally, the human reaction function is integrated into the machine’s predictive control system, moving beyond a simple “prediction-planning” pipeline to enhance planning optimality. The controller has been verified to 1) enable a smooth takeover maneuver of CACC; 2) ensure string stability in the condition that the platoon has less than 6 CAVs and human control authority is less than 40%; 3) enhance both perceived and actual safety through machine interventions; and 4) reduce the impact on upstream traffic by up to 60%.]]></description>
      <pubDate>Mon, 01 Jun 2026 09:10:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617777</guid>
    </item>
    <item>
      <title>Challenges and Algorithms for Connected and Autonomous Vehicle Safety and Mobility in Smart Cities</title>
      <link>https://trid.trb.org/View/2579091</link>
      <description><![CDATA[Connected vehicles can enhance safety by enabling real-time communication between vehicles and infrastructure, alerting drivers to potential hazards, and improving situational awareness. This technology also benefits vulnerable road users and reduces the economic burden of traffic incidents. In this chapter, we take a look at various challenges facing connected autonomous vehicles and proposed algorithms to address those challenges and enhance vehicle safety and mobility in smart cities. Specifically, cooperative adaptive cruise control subject to temporary communication or target detection loss; connected lane-change advance warning systems for improving traffic flow; probabilistic trajectory forecasting for vulnerable road users, especially pedestrians; and, finally, approaches to collision avoidance and mitigation for autonomous vehicles.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:37 GMT</pubDate>
      <guid>https://trid.trb.org/View/2579091</guid>
    </item>
    <item>
      <title>RACER: Rational Artificial Intelligence Car-Following-Model Enhanced by Reality</title>
      <link>https://trid.trb.org/View/2658940</link>
      <description><![CDATA[This paper introduces RACER, the Rational Artificial Intelligence Car-following model Enhanced by Reality, a cutting-edge deep learning car-following model, that satisfies partial derivative constraints, designed to predict Adaptive Cruise Control (ACC) driving behavior while staying theoretically feasible. Unlike conventional models, RACER effectively integrates Rational Driving Constraints (RDCs), crucial tenets of actual driving, resulting in strikingly accurate and realistic predictions. Against established models like the Optimal Velocity Relative Velocity (OVRV), a car-following Neural Network (NN), and a car-following Physics-Informed Neural Network (PINN), RACER excels across key metrics, such as acceleration, velocity, and spacing. Notably, it displays a perfect adherence to the RDCs, registering zero violations, in stark contrast to other models. This study highlights the immense value of incorporating physical constraints within AI models, especially for augmenting safety measures in transportation. It also paves the way for future research to test these models against human driving data, with the potential to guide safer and more rational driving behavior. The versatility of the proposed model, including its potential to incorporate additional derivative constraints and broader architectural applications, enhances its appeal and broadens its impact within the scientific community.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658940</guid>
    </item>
    <item>
      <title>Pollution Reduction and Carbon Reduction in Mixed Traffic Flow Environments under the Influence of Lci for Energy Consumption Analysis</title>
      <link>https://trid.trb.org/View/2666432</link>
      <description><![CDATA[The accelerated progression of urbanisation has emerged as a pivotal factor in the escalating issue of global warming, with automobile exhaust emissions assuming a central role in this context. This phenomenon underscores the growing prominence of energy consumption and environmental pollution as critical concerns. To promote the healthy development of green and sustainable transportation systems, this study analyses the impact of lane change intention (LCI) on reducing pollution and carbon in mixed traffic flow. The study proposes a traffic energy consumption model to analyse the impact of LCI on vehicle energy consumption in a mixed traffic flow environment. The model combines the collaborative adaptive cruise control (CACC) strategy to explore energy consumption in a multidimensional mixed traffic environment. The results showed that vehicles with LCI in mixed traffic flow had an average energy consumption increase of 7.65% compared to vehicles driving normally. When the vehicle adopted CACC, it could effectively alleviate the increase in energy consumption caused by LCI. Vehicles with the LCI and applying CACC only increased their energy consumption by an average of 4.84%, a decrease of 2.81% compared to those without cruise control. In summary, the research on pollution reduction and carbon reduction in mixed traffic flow environments under the influence of LCI for energy consumption analysis provides support and reference for the sustainable development of green transportation.]]></description>
      <pubDate>Tue, 26 May 2026 09:41:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2666432</guid>
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