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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=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJkYXRlaW4iIHZhbHVlPSJhbGwiIC8+PHBhcmFtIG5hbWU9InN1YmplY3Rsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJ0ZXJtc2xvZ2ljIiB2YWx1ZT0ib3IiIC8+PHBhcmFtIG5hbWU9ImxvY2F0aW9uIiB2YWx1ZT0iMCIgLz48L3BhcmFtcz48ZmlsdGVycz48ZmlsdGVyIGZpZWxkPSJpbmRleHRlcm1zIiB2YWx1ZT0iJnF1b3Q7VmVoaWNsZSB0byB2ZWhpY2xlIGNvbW11bmljYXRpb25zJnF1b3Q7IiBvcmlnaW5hbF92YWx1ZT0iJnF1b3Q7VmVoaWNsZSB0byB2ZWhpY2xlIGNvbW11bmljYXRpb25zJnF1b3Q7IiAvPjwvZmlsdGVycz48cmFuZ2VzIC8+PHNvcnRzPjxzb3J0IGZpZWxkPSJwdWJsaXNoZWQiIG9yZGVyPSJkZXNjIiAvPjwvc29ydHM+PHBlcnNpc3RzPjxwZXJzaXN0IG5hbWU9InJhbmdldHlwZSIgdmFsdWU9InB1Ymxpc2hlZGRhdGUiIC8+PC9wZXJzaXN0cz48L3NlYXJjaD4=" rel="self" type="application/rss+xml" />
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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>
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      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Dead Zone Mitigation in Vehicular Platoon via Solar Panel as a Communication Receiver</title>
      <link>https://trid.trb.org/View/2672822</link>
      <description><![CDATA[Intelligent transportation systems (ITS) are designed to enhance road safety and traffic efficiency. Connected autonomous vehicles are a critical component of ITS, and platooning among vehicles can enhance close coordination and improve driving efficiency. This requires high-capacity links, which can be facilitated by light communication. However, due to the small area of optical photodiodes, the light communication link can suffer misalignment, leading to reduced reliability during maneuvering on-road curvatures. Solar panels present a potential solution having a larger receiver area and the ability to convert the optical signal to its electrical equivalent. However, solar panel 3-dB bandwidth for communication is limited to only a few tens of kHz range, resulting in low data throughput. In this work, we develop an analog circuit to enhance the 3-dB bandwidth of solar panel receivers, effectively mitigating misalignment issues by maintaining connectivity even at high orientation. Experimental results demonstrate that the solar receiver can maintain communication despite extreme misalignment of the order of ±50°. Furthermore, the results indicate that the solar panel can still provide connectivity even when 90% of its area is in blockage. Specifically, on the transmitter side, we have employed near-infrared light-emitting diodes (LEDs) to mitigate the glare effect caused by white LEDs. Also, IR LEDs cause less attenuation loss in outdoor environments, thus consuming less power than white LEDs to transmit information. Analytical expressions for designed analog circuitry and the impact of using solar panels in the dead zone are derived, and the comparison results with conventional photodiode is presented. The findings indicate that solar panels, when used as optical receivers, meet the communication requirements for the connected vehicles and effectively address issues such as loss of communication links.]]></description>
      <pubDate>Thu, 09 Jul 2026 13:32:10 GMT</pubDate>
      <guid>https://trid.trb.org/View/2672822</guid>
    </item>
    <item>
      <title>Robust string-stable platoon control via observer-based distributed MPC under Markovian switching V2V networks</title>
      <link>https://trid.trb.org/View/2684471</link>
      <description><![CDATA[Switching communication topologies may cause instability in vehicle platoons, as vehicle information can be lost during the dynamic switching process. This highlights the need to design a controller capable of maintaining the stability of vehicle platoons under dynamically changing topologies. However, it remains a significant challenge to capture the dynamic characteristics of switching topologies and obtain sufficient vehicle information for controller design while ensuring stability. In this study, an observer-based distributed model predictive control (DMPC) framework is developed for vehicle platoons under directed Markovian switching communication topologies. The directed switching communication topology is modeled using a continuous-time Markov chain to characterize its stochastic switching behavior. To estimate the leader vehicle information required for control, a fully distributed adaptive observer is designed, whose estimation performance is robust to randomly switching topologies. A sufficient condition is further derived to guarantee the mean-square stability of the observer error dynamics. Based on the estimated information, a terminal update law is constructed to ensure mean-square consensus, and a string stability constraint is formulated to explicitly enforce predecessor-follower string stability within the DMPC framework. Recursive feasibility and closed-loop stability properties of the resulting control scheme are established. Numerical simulation results demonstrate that the proposed method enhances tracking performance, accelerates convergence, and reduces control effort, while maintaining predecessor-follower string stability. With acceleration fluctuations and packet loss, the proposed method reduces the maximum position error by 45%, and improves stability by 39%, demonstrating significant improvements in tracking performance and system stability. Furthermore, the effectiveness of the proposed framework is validated using real-world driving data.]]></description>
      <pubDate>Tue, 30 Jun 2026 17:02:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2684471</guid>
    </item>
    <item>
      <title>Estimating the Impact of Vehicle Breakdown on Traffic Performances: A V2V Simulation Study of UK Motorways</title>
      <link>https://trid.trb.org/View/2670966</link>
      <description><![CDATA[Road traffic congestion has adverse effects on commuter safety and transport network efficiency, apart from its environmental consequences. To address this issue, Traffic Incident Detection (TID) models have been developed, leveraging advanced connectivity technologies. However, ensuring the alignment and effective operation of these technologies within existing systems and contexts is critical. This research aims to create an incident detection algorithm supported by Vehicle-to-Vehicle (V2V) technologies, alerting road users approaching incident zones. The algorithm’s effectiveness was assessed through metrics like vehicle delays, travel time, and macroscopic fundamental diagrams (MFDs). Real-time traffic conditions were simulated using VISSIM, employing data from Inductive Loop Detectors (ILDs) and ground truth data from an instrumented vehicle on a UK motorway section. Results reveal varying impacts on delays and overall traffic based on V2V adoption rates. The presence of Connected Vehicles (CVs) ensures efficient traffic flow. These insights benefit network operators, enabling prompt identification and communication of traffic incidents to drivers, roadside infrastructure, and traffic control centers, ultimately aiming to mitigate traffic and safety impacts.]]></description>
      <pubDate>Tue, 30 Jun 2026 09:45:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2670966</guid>
    </item>
    <item>
      <title>Vehicle Dynamics and Cooperative Driving Enhanced by Multi-Agent V2v Communication</title>
      <link>https://trid.trb.org/View/2676036</link>
      <description><![CDATA[Advances in wireless communication and sensor technologies have enabled vehicle-to-vehicle (V2V) systems that enhance road safety and traffic efficiency. The objective of this study is to develop and evaluate a multi-agent V2V communication framework that enables cooperative driving, allowing autonomous vehicles to make real-time, informed decisions in complex traffic scenarios. The proposed system is implemented using the JADE multi-agent platform and incorporates reinforcement learning and cooperative decision-making strategies. Each vehicle is represented by a Generic Car Agent (GCA) with integrated sub-agents responsible for driver modeling, information integration, knowledge management, and active interface functions. Remote Car Agents (RCA) and Traffic Control Agents (TCA) facilitate communication across vehicles and traffic networks, enabling coordinated maneuvers such as lane changes and platooning. The framework is evaluated using real-world traffic data collected from urban and highway roads in Jordan, across five challenging driving scenarios. Simulation results show improved traffic flow, reduced collision risk, and enhanced fuel efficiency. The system is cost-effective, leveraging existing onboard sensors and standard wireless technologies, demonstrating practical potential for scalable deployment in intelligent transportation systems.]]></description>
      <pubDate>Wed, 17 Jun 2026 12:23:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2676036</guid>
    </item>
    <item>
      <title>An Energy-Saving Coupling-Decoupling Optimization Strategy for Electric Modular Buses Considering Vehicle to Vehicle Charging</title>
      <link>https://trid.trb.org/View/2706244</link>
      <description><![CDATA[As an emerging innovative mode of public transportation, electric modular buses (EMBs) offer a novel solution to the problems of existing public transportation systems, due to the coupling-decoupling processes. In this paper, we study the energy consumption characteristics of EMBs by joining vehicle-to-vehicle (V2V) charging and reduction in aerodynamic drag due to coupling. For the pursuit of energy economy, ride comfort, and operational efficiency, we constructed an optimization scheme based on the simulated annealing (SA) algorithm to facilitate the coupling-decoupling process. The simulation results show that EMBs can meet 82.5 % of service requests compared with 61.8 % for the benchmark group, and V2V presents a significant contribution to energy efficiency, especially at low battery state of charge (SOC). Additionally, sensitivity analysis is conducted to study the impact of initial SOC, operation interval, and route type. The results provide insights for optimizing EMBs’ operations and emphasize the potential role of EMBs in supporting low-carbon and sustainable urban mobility systems.]]></description>
      <pubDate>Tue, 02 Jun 2026 11:12:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2706244</guid>
    </item>
    <item>
      <title>Cooperative Fleet Lane Changing With Multi-Group Splitting and Merging Based on Vehicular Sensor Networks</title>
      <link>https://trid.trb.org/View/2617732</link>
      <description><![CDATA[In this paper, we propose a cooperative fleet lane changing (CFLC) framework based on vehicular sensor networks, which enables multiple groups of fleet vehicles splitting and merging. The proposed CFLC framework determines the lane-changing space in the target lane, performs real-time splitting of fleet vehicles, coordinates the speeds of fleet vehicles and non-fleet vehicles, and merges multiple groups of fleet vehicles. In CFLC, we design the dynamic programming algorithm to determine the optimal fleet lane-changing space, and split fleet vehicles into multiple groups according to the optimal lane-changing space. As there are no sufficient inter-vehicle gaps for fleet group lane changing, hybrid speed control is explored to adjust both the fleet vehicle speeds and the speeds of vehicles in the target lane to avoid unnecessary acceleration/deceleration. Moreover, efficient merging of multiple fleet groups is designed to deal with the separation of fleet vehicles for avoiding increased travel time and fuel/energy consumption. According to our review of relevant research, this is the first cooperative fleet lane changing framework that supports multi-group vehicle splitting and merging. Simulation results show that our framework outperforms existing lane-changing methods and can minimize the speed variations of fleet vehicles and non-fleet vehicles for improving traffic flow and reducing fuel/energy wastage. In particular, with a fleet of 10 vehicles at 110 km/hr, average speed variations for changing lanes are decreased by more than 14 m/s and improved by more than 45%.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:36 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617732</guid>
    </item>
    <item>
      <title>Learning-Based AoI Minimization Through UAV-Assisted Data Distribution in Vehicular Networks</title>
      <link>https://trid.trb.org/View/2658831</link>
      <description><![CDATA[Uncrewed Aerial Vehicle (UAV) is extensively employed as a mobile base station in areas with inadequate cellular infrastructure to enhance the freshness of vehicle sensors. The Age of Information (AoI) is a metric utilized to characterize the freshness of information produced by vehicle sensors. This paper investigates the use of Uncrewed Aerial Vehicles (UAVs) as mobile base stations to enhance the freshness of vehicle sensor information in areas with inadequate cellular infrastructure. We focus on minimizing the Age of Information (AoI) and UAV energy consumption in a Vehicle-to-UAV (V2U) network within the Manhattan scenario. The challenge lies in jointly optimizing UAV trajectories and vehicle data packet scheduling amidst high vehicle mobility and limited communication range. To address this issue, we employ Reinforcement Learning (RL) to formulate the problem as a Markov Decision Process (MDP), proposing a Dueling Double Deep Q-Network (D3QN) method for trajectory and scheduling optimization. We also introduce Priority Experience Replay (PER) to improve reward acquisition for the UAV, addressing the issue of sparse rewards due to the expansive space for vehicle movement. Simulation results provide empirical evidence supporting the efficacy of the proposed algorithm in comparison to baseline policies.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658831</guid>
    </item>
    <item>
      <title>Trajectory Prediction for Multiple Agents in Dynamic Environments: Factoring in Traffic States and Driving Styles</title>
      <link>https://trid.trb.org/View/2658805</link>
      <description><![CDATA[Predicting the trajectories of multiple agents in dynamic driving scenarios such as intersections and roundabouts remains challenging due to dense agent interactions, varying speeds, and complex environmental constraints. While previous studies have proposed interaction-aware models, they often neglect the combined effects of traffic states and individual driving behaviors. This study presents a personalized motion prediction framework based on Vehicle-to-Vehicle (V2V) communication to support decision-making in complex environments. An unsupervised method is proposed to recognize driving styles by analyzing features derived from V2V communication, which are then encoded and incorporated into the prediction models, with feature importance analyzed via a random forest algorithm. Traffic states are modeled using relative spatial relationships and captured through spatiotemporal encoding. To explore different prediction paradigms, three architectures, GAN-based, CVAE-based, and Transformer-based, are designed and comparatively evaluated, each incorporating driving style and traffic state information as categorical encodings. Among these, the Transformer-based model demonstrates superior performance across multiple prediction horizons and metrics due to its effective integration of behavioral and contextual features in both encoder and decoder. The proposed framework is evaluated on two public interactive driving datasets, rounD and INTERACTION, where it achieves state-of-the-art accuracy. Results show that incorporating personalized driving styles and traffic context significantly enhances trajectory prediction, making it more suitable for real-time decision support in connected and autonomous vehicles.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658805</guid>
    </item>
    <item>
      <title>Joint Communication and Control Optimization of a Multi-Vehicle Platooning System</title>
      <link>https://trid.trb.org/View/2658786</link>
      <description><![CDATA[In the context of vehicle-road-cloud integration, multi-vehicle platooning systems have become an important approach for improving road traffic efficiency, driver comfort, driving safety, energy consumption, and mitigating traffic congestion. However, under high-speed mobility, Vehicle-to-Vehicle (V2V) communication within multi-vehicle platoons is susceptible to delays caused by interference and the inherent uncertainties of wireless communication channels. These delays present considerable challenges to achieving effective multi-vehicle cooperative control. To overcome the limitations of existing research, this paper proposes a joint communication and control optimization strategy for multi-vehicle platooning systems. A novel spacing error metric is introduced, which uses the real-time velocity of each vehicle to improve the platooning system responsiveness. Furthermore, we derive the Signal-to-Interference-plus-Noise Ratio (SINR) threshold to ensure the stability and reliability of the platoon. This ensures safe distances and synchronized speeds among all vehicles, even when communication delays occur. Finally, the proposed joint optimization strategy is validated through performance comparisons, demonstrating its effectiveness and superior performance.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658786</guid>
    </item>
    <item>
      <title>Safety-Critical Multi-Agent MCTS for Mixed Traffic Coordination at Unsignalized Intersections</title>
      <link>https://trid.trb.org/View/2658784</link>
      <description><![CDATA[Decision making at unsignalized intersections presents significant challenges for autonomous vehicles (AVs), particularly in mixed traffic scenarios where both AVs and human-driven vehicles (HDVs) must safely coordinate their movements. This paper proposes a safety-critical multi-agent Monte Carlo tree search (MCTS) framework that integrates deterministic and probabilistic predictions to enable cooperative decision making in complex intersection scenarios. The framework incorporates three main innovations: 1) a safety assessment mechanism that systematically handles AV-to-AV (V2V), AV-to-HDV (V2H), and Vehicle-to-Road (V2R) interactions using dynamic safety thresholds and spatiotemporal risk metrics, 2) an adaptive HDV behavior awareness by combining the Intelligent Driver Model (IDM) with probabilistic distributions, and 3) a multi-objective reward function optimization approach that balances safety, efficiency, and cooperation. Extensive simulations demonstrate our framework’s efficacy and superior capability in ensuring safe and efficient intersection navigation across the fully-autonomous scenario (100% AVs) and challenging mixed traffic scenario (50% AVs +50% HDVs). Compared to benchmarks, our method reduces trajectory deviations by up to 37.56% in the fully-autonomous scenario and 62.43% in the mixed traffic scenario, while maintaining significantly lower Post-Encroachment Time (PET) violations (0% and 2.8%, respectively).]]></description>
      <pubDate>Thu, 28 May 2026 17:09:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658784</guid>
    </item>
    <item>
      <title>Cooperative Merging via Online Speed Replanning: A Model-Free Approach With Vehicle-to-Vehicle Communication Packet Drop Compensation</title>
      <link>https://trid.trb.org/View/2658777</link>
      <description><![CDATA[On-ramp merging is a critical bottleneck in freeway traffic flow, contributing to congestion, accidents, and excessive fuel consumption. Although traditional ramp metering provides macroscopic control, it lacks the granularity for optimizing an individual vehicle’s trajectory. Cooperative merging, enabled by connected and automated vehicles, can potentially enhance traffic efficiency, safety, and fuel economy. However, existing research often neglects the influence of heterogeneous vehicle dynamics, unreliable vehicle-to-vehicle (V2V) communication, and real-time implementation challenges. This paper introduces novel model-free online speed planners for cooperative on-ramp merging. The planners address these limitations by being agnostic to vehicle dynamics, effectively compensating for V2V communication packet drops and incurring only a light computational burden. Comprehensive evaluation, conducted on a real-time traffic-vehicle-communication co-simulation platform integrating high-fidelity vehicle dynamics, a traffic simulator, and recorded V2V communication footprints, demonstrates the effectiveness of the proposed speed planners. Simulation results reveal that the proposed method yields accurate tracking of desired speed and inter-vehicle distance, maintaining low fuel consumption even under high packet drop ratios, and demonstrating real-time implementation efficiency.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658777</guid>
    </item>
    <item>
      <title>BCDAP-DGS: Dynamic Group Signature and Batch Cross-Domain Authentication Protocol for Intelligent Transportation</title>
      <link>https://trid.trb.org/View/2658761</link>
      <description><![CDATA[The Internet of Vehicles (IoV), as a core component of intelligent transportation systems, significantly enhances the intelligence level of traffic management by enabling efficient vehicle-to-vehicle (V2V) and vehicle-to-infrastructure information sharing. However, the highly dynamic and open nature of the IoV poses severe security challenges in cross-domain scenarios, mainly due to the lack of trust relationships between different domains, making it difficult to achieve efficient and secure cross-domain authentication(CDA). Existing CDA mechanisms in the IoT context often suffer from high computational complexity, excessive communication overhead, and poor scalability for large-scale deployments. This paper proposes a Batch CDA Protocol based on Dynamic Group Signatures (BCDAP-DGS) to address these issues. The proposed protocol incorporates non-interactive zero-knowledge (NIZK) proofs to achieve secure identity verification without requiring additional data exchange. By leveraging dynamic group signature techniques, BCDAP-DGS supports real-time updates of vehicle membership status and provides conditional anonymity. In addition, an online/offline authentication framework is designed by incorporating vehicle location information to precompute related parameters, thereby significantly improving CDA efficiency. A formal security analysis is conducted under the random oracle model, demonstrating that the proposed protocol satisfies essential security properties, including anonymity, non-frameability, unforgeability, and traceability. Experimental results and performance comparisons show that the proposed protocol outperforms existing schemes in terms of both security and efficiency, making it well-suited for large-scale and highly dynamic IoV CDA scenarios.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658761</guid>
    </item>
    <item>
      <title>Design and Optimization of Adaptive Cooperative MAC Protocol With Priority Scheduling for Train-to-Train Communications</title>
      <link>https://trid.trb.org/View/2659013</link>
      <description><![CDATA[With the advancement of urbanization, communication-based train control (CBTC) systems for urban rail transit and train-to-train (T2T) communication have garnered significant attention. T2T communication establishes mobile ad hoc networks (MANETs), similar to those in vehicular ad hoc networks (VANETs). Building upon this foundation, we propose an adaptive cooperative (ADCO) MAC protocol for T2T communication. The scheme introduces clustering and cooperative transmission mechanisms, which enhance the efficiency and reliability of safety packet transmission. Additionally, the protocol assigns different priorities to packets engaging in contention on the control channel (CCH) and enables trains to access service channels (SCHs) without contention through pre-reserved time slots. To analyze the transmission probabilities and success rates of packets with varying priorities, a Markov-based model is utilized, ultimately determining the optimal ratio of the CCH interval (CCHI) to the SCH interval (SCHI) for maximizing channel utilization. Theoretical analysis and simulation results demonstrate that the proposed MAC protocol ensures reliable transmission of safety packets while simultaneously optimizing the throughput on SCHs.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659013</guid>
    </item>
    <item>
      <title>Swarm of MASSs Cyber-Security for Anti-Hijack System Based on Blockchain and Chaotic-Steganography Using VRF-PBFT and Encoder-Decoder Deep Neural Networks</title>
      <link>https://trid.trb.org/View/2658973</link>
      <description><![CDATA[It is crucial to guarantee the remote operation safety of the Maritime Autonomous Surface Ships (MASS). In this paper, a cyber-security framework of the anti-hijack system in the swarm of MASSs was proposed for important information hiding based on blockchain and chaotic-steganography to generate the watermark camouflage image and ensure the security and concealment of important information transmitted in the ship Ad hoc network. The swarm of MASSs cyber-security problem was divided into two parts: source security and transmission security. Three modules are utilized to address the problem. First, the cognitive mechanism for multi-ship interaction based on VRF-PBFT was constructed. Subsequently, MASS transmit the key information of the plain-text which were respectively subjected to the Henon-Logistic chaotic mapping function to produce strong pseudo-random sequence information. Finally, the encrypted sequence information was combined with the original image as the input data for the encoder deep neural networks, and the watermark camouflage image was generated by U-net with the down-sampling and up-sampling structures to extract the image features that are adaptable to the original image. At the ship base, the watermark camouflage image with the original image were merged to achieve the chaotic-steganography which is inserted into the video stream for hidden transmission. At the shore base, the decoder neural networks utilized the ST-transfer CNN architecture to retrieve encrypted hidden information within images. Finally, six self-made MASS remote operation systems of Tianjin University were applied to validate the method. Histogram, PSNR, MSE and SSIM analysis of images before and after encryption demonstrates the method’s statistical robustness, ensuring the secret and secure transmission of critical information perceived by MASSs in ship Ad hoc network.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658973</guid>
    </item>
    <item>
      <title>Platoon Communication Power Control Under V2V Data Uncertainty: A Robust DRL Approach</title>
      <link>https://trid.trb.org/View/2658959</link>
      <description><![CDATA[Connected and autonomous vehicle (CAV) platoon control has been considered as a promising technology to improve the safety and efficiency of intelligent transportation systems. In the platoon control decision-making process, vehicle-to-vehicle (V2V) communications are required to perform information exchange between vehicles. Existing studies commonly assume ideal V2V communication conditions, where the vehicles can receive completely accurate V2V data. However, in realistic scenarios, the V2V communication is subject to channel fading or communication attacks, which makes the vehicles to receive inaccurate or malicious V2V data, causing ‘V2V data uncertainty’. To tackle this problem, we investigate the CAV platoon control problem under the V2V data uncertainty. Firstly, we formulate a stability optimization problem under the V2V data uncertainty, based on an improved car-following model that incorporates multiple predecessors following-based V2V communication topology. Secondly, by employing linear stability theory in conjunction with the improved car-following model, we derive the stability constraint for the optimization problem. Then, by solving this optimization problem at every time step, an optimal control input is obtained to maintain system stability, thereby indirectly controlling the CAV platoon. To learn the optimal control strategy, we propose a robust deep reinforcement learning (DRL) approach. This approach demonstrates that the difference between the value function under optimal state perturbation and that under unperturbed conditions is bounded by a tunable upper limit. Since data uncertainty is intuitively reflected in the state, adjusting this upper bound enables the robust DRL approach to counteract the effects of V2V data uncertainty. Finally, simulation results indicate that the proposed approach significantly outperforms baseline approaches and the average allocation method in terms of platoon state control, stability, and comfort. Moreover, it is markedly superior to the baseline approaches regarding convergence and overall model performance.]]></description>
      <pubDate>Thu, 28 May 2026 17:09:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2658959</guid>
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