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International Conference on Artificial Intelligence and Autonomous transportation, AIAT 2024

作者： Ji, Meichen Song, Yan Hu, Song Liu, Yuqing Wang, Jing Research Institute of Highway Ministry of Transport Beijing100088 China State Key Lab of Intelligent Transportation System Beijing100088 China

ISBN: (纸本)9789819639601

Carbon emissions monitoring of road operating vehicles is usually based on the statistical calculation methods at large spatiotemporal scales, and a set of multi-level spatiotemporal monitoring method that combines macro and micro dimensions has not been explored. Moreover, the dynamic monitoring technology means of traffic carbon emission are lacking and the monitoring accuracy is low, which cannot meet the needs of daily monitoring management and carbon emission trading of operating enterprises and traffic management departments. Therefore, taking carbon emissions of road operating vehicles as the target, the influencing factors and characteristics of carbon emissions of road operating vehicles were analyzed based on the multi-source data of road operating vehicles collected from Portable Emission Measurement system (PEMS) and bench testing. A carbon emission factor data set was constructed based on typical operating vehicle types and working conditions, then a multi-level spatiotemporal monitoring model for carbon emissions of operating vehicles was established at different spatiotemporal scales. Finally, we select typical light SUV vehicles and heavy truck cement vehicles, which can achieve precise measurement of carbon emissions and holographic monitoring of carbon emissions for single vehicle and regional vehicles as well. The research is conduced to assist in the monitoring and managing carbon emissions of road operating vehicles. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Carbon Emission Emission Monitoring intelligent transportation Operational Vehicles Spatiotemporal Monitoring Model

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A Two-Stage Extended Kalman Filter-Based Approach Against FDI Cyber-Attack in intelligent and Connected Vehicles

A Two-Stage Extended Kalman Filter-Based Approach Against FD...

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11th International Conference on Vehicle Technology and intelligent Transport systems, VEHITS 2025

作者： Sun, Bin Yang, Shichun Wang, Yu Lu, Jiayi Cao, Yaoguang School of Transportation Science and Engineering Beihang University Beijing China State Key Lab of Intelligent Transportation System Beihang University Beijing China

ISBN: (纸本)9789897587450

With the widespread integration of artificial intelligence and telecommunication technologies in vehicles, the challenge of cybersecurity in intelligent and Connected Vehicles (ICVs) has gained significant attention. A typical and high-risk cyber-attack technique involves False Data Injection (FDI) into sensors through the network, resulting in deviations in subsequent planning and control algorithm outcomes. Existing approaches suffer from limited robustness, being suitable only for simple models or requiring extensive data for the training model, which limits their practicality. Therefore, this paper proposes a method based on a Two-stage Extended Kalman Filter (TSEKF), which not only detects cyber-attacks but also restores the vehicle’s true motion state, thereby enhancing the robustness of vehicle ego state perception. The experimental results demonstrate that the proposed method exhibits strong performance across various motion scenarios, offering an effective solution for the safe operation of ICVs. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Cyber attacks

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Real-Time LiDAR Point Cloud Semantic Segmentation for Unstructured Road Autonomous Driving

Real-Time LiDAR Point Cloud Semantic Segmentation for Unstru...

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International Conference on Artificial Intelligence and Autonomous transportation, AIAT 2024

作者： Li, Huazhi Yu, Guizhen Wang, Zhangyu Chen, Yang Zhao, Fei School of Transportation Science and Engineering Beihang University Beijing100191 China State Key Lab of Intelligent Transportation System Beijing China School of Vehicle and Mobility Tsinghua University Beijing100084 China

ISBN: (纸本)9789819639724

Lidar-based semantic segmentation is critical for autonomous driving, but existing methods struggle with real-time performance in unstructured environments like open-pit mining areas. This paper proposes a real-time semantic segmentation network leveraging an attention mechanism to improve accuracy and speed. key innovations include a global contextual information extraction module, bilinear upsampling layers to reduce channel redundancy, and coordinate attention-based upsampling to alleviate structural redundancy. This paper tested the proposed method on an unstructured road open-pit mining dataset, achieving an average IoU of 85.6% and an inference speed of 5.90ms, which is 4.3% and 14.6% higher than the baseline (SalsaNext) in terms of accuracy and speed, respectively. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Semantic Segmentation

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Recognition of Typical Highway Driving Scenarios for intelligent Connected Vehicles Based on Long Short-Term Memory Network

Recognition of Typical Highway Driving Scenarios for Intelli...

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11th International Conference on Vehicle Technology and intelligent Transport systems, VEHITS 2025

作者： Feng, Xinjie Yang, Shichun Peng, Zhaoxia Chen, Yuyi Sun, Bin Lu, Jiayi Wang, Rui Cao, Yaoguang School of Transportation Science and Engineering Beihang University Beijing China State Key Lab of Intelligent Transportation System Beihang University Beijing China Hangzhou International Innovation Institute Beihang University Hangzhou China

ISBN: (纸本)9789897587450

In the complex traffic environment where intelligent connected vehicles (ICVs) and traditional vehicles coexist, accurately identifying the driving scenarios of a vehicle helps ICVs make safer and more efficient decisions, while also enabling performance evaluation across different scenarios to further optimize system capabilities. This paper presents a typical highway driving scenarios recognition model with extensive scenario coverage and high generalizability. The model first categorizes the constituent elements of driving scenarios and extracts the core elements of typical highway scenarios. Then, based on a long short-term memory (LSTM) network architecture, it extracts features from the ego vehicle and surrounding vehicles to identify the typical driving scenarios in which the ego vehicle is located. The model was tested and validated on the HighD dataset, achieving an overall accuracy of 96.74% for four typical highway scenarios: Lane-change, Car-following, Alongside vehicle cut-in, and Preceding vehicle cut-out. Compared to baseline models, the proposed model demonstrated superior performance. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Advanced traffic management systems

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Recognize then Resolve: A Hybrid Framework for Understanding Interaction and Cooperative Conflict Resolution in Mixed Traffic

arXiv

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arXiv 2025年

作者： Fang, Shiyu Zhou, Donghao Cui, Yiming Xu, ChengKai Hang, Peng Sun, Jian College of Transportation Tongji University Shanghai201804 China State Key Lab of Intelligent Transportation System Beijing100088 China

A lack of understanding of interactions and the inability to effectively resolve conflicts continue to impede the progress of Connected Autonomous Vehicles (CAVs) in their interactions with Human-Driven Vehicles (HDVs). To address this challenge, we propose the Recognize then Resolve (RtR) framework. First, a Bilateral Intention Progression Graph (BIPG) is constructed based on CAV-HDV interaction data to model the evolution of interactions and identify potential HDV intentions. Three typical interaction breakdown scenarios are then categorized, and key moments are defined for triggering cooperative conflict resolution. On this basis, a constrained Monte Carlo Tree Search (MCTS) algorithm is introduced to determine the optimal passage order while accommodating HDV intentions. Experimental results demonstrate that the proposed RtR framework outperforms other cooperative approaches in terms of safety and efficiency across various penetration rates, achieving results close to consistent cooperation while significantly reducing computational resources. Our code and data are available at: https://***/FanGShiYuu/RtR-Recognize-then-Resolve/. Copyright © 2025, The Authors. All rights reserved.

关键词： Autonomous vehicles

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A Simulation Model for Mechanism Analysis of Interoperation in Road Autonomous transportation system

A Simulation Model for Mechanism Analysis of Interoperation ...

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International Conference on Artificial Intelligence and Autonomous transportation, AIAT 2024

作者： Liu, Yanyue Zhang, Yipeng Li, Zhenhua ITS Center Research Institute of Highway Ministry of Transport Beijing100088 China State Key Laboratory of Intelligent Transportation System Research Institute of Highway Ministry of Transport Beijing100088 China

ISBN: (纸本)9789819639649

This paper proposed a simulation model for analyzing interoperation process of road autonomous transportation system. First, an interoperation simulation model built with complex network theory is proposed. In the model, vehicles and infrastructures are modeled as nodes of graph. Then, a network evolution model based on random graph theory is proposed. The network evolution model aims to create interoperation topology between vehicles and infrastructures. An autonomous level transition algorithm is subsequently proposed. In the algorithm, interoperation and information sharing is considered as key components of influencing autonomous level, where age of Information (AoI) is the pivot indicator of validity of the shared information. The paper conducts a case study in highway scenario for validating effectiveness of the model. In the case study, the proposed model is further utilized to analyze the pathway for upgrading autonomous level via interoperation. The result of case study showcases the proposed model can characterize relationships of interoperation and autonomous level. The proposed model offering a theoretical basis for foundation of interoperation link in road autonomous transportation system. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Autonomous vehicles

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LLM-Enhanced Reinforcement Learning for Traffic Control at On-Ramp Merging Areas

LLM-Enhanced Reinforcement Learning for Traffic Control at O...

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International Conference on Artificial Intelligence and Autonomous transportation, AIAT 2024

作者： Yin, Qihao Xiong, Zhengang Liu, Yanyue ITS Center Research Institute of Highway Ministry of Transport Beijing100088 China State Key Laboratory of Intelligent Transportation System Research Institute of Highway Ministry of Transport Beijing100088 China

ISBN: (纸本)9789819639649

The on-ramp merging area plays an important role in improving the overall safety and efficiency of the traffic. At present, the widely used ramp merging control methods are mostly based on reinforcement learning (RL), but these methods may make erroneous decisions due to bad generalization ability, potentially jeopardizing traffic efficiency and safety under unseen scenarios. Noticing the great success of large language models (LLMs) on other domain, this paper proposes a novel method based on LLM-enhanced RL agent for traffic control at on-ramp merging areas. Specifically, the proposed method utilizes RL agents to make initial actions based on surrounding environment, after that, an LLM model refines these decisions by the designed prompt and chain of thoughts. Experiment results show that the proposed method outperforms the other methods for on-ramp merging, with a promotion of about 14% on average speed of involved vehicles, and the promotion becomes 22.1% under severe dense traffic conditions. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Deep reinforcement learning

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An Engineering Evaluation Methodology of Heterogeneous Network for Highway Infrastructure Monitoring Data Transmission Based on Graph Theory

An Engineering Evaluation Methodology of Heterogeneous Netwo...

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International Conference on Artificial Intelligence and Autonomous transportation, AIAT 2024

作者： Li, Zhenhua Zhang, Yipeng Chen, Yi School of Electrical and Control Engineering North China University of Technology Beijing100144 China Research Institute of Highway Ministry of Transport Ministry of Transport Beijing100081 China State Key Lab of Intelligent Transportation System Ministry of Transport Beijing100081 China

ISBN: (纸本)9789819639564

A large-scale highway infrastructure monitoring system requires a complex and heterogeneous data transmission network to transmit different types and large amounts of sensor data. In order to meet the requirement of rapid modeling and performance evaluation of highway infrastructure monitoring data transmission networks in engineering construction projects, this paper proposes a graph modeling method for heterogeneous transmission networks of highway infrastructure monitoring data using relevant concepts and methods of graph theory. By combining network models with engineering implementation experience, an exploratory engineering evaluation method for evaluating network carrying capacity, end-to-end latency, and end-to-end reliability has been proposed. The above methods can be used to achieve rapid modeling of networks and rapid evaluation of key performance parameters. The evaluation conclusions can serve as the basis for optimizing and implementing the engineering design of highway infrastructure monitoring systems. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Network theory (graphs)

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FusionSis: Analysis and Evaluation Framework for Fusion Safety state of Connected and Automated Vehicles Under Cyber Attacks 1st

FusionSis: Analysis and Evaluation Framework for Fusion Sa...

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1st International Symposium on intelligent Technology for Future transportation, ITFT 2024

作者： Zheng, Bowen Yang, Shichun Gong, Weifeng Guang, Haoran Shi, Yi Chen, Mingjie Cao, Yaoguang School of Transportation Science and Engineering Beihang University Beijing100191 China State Key Lab of Intelligent Transportation System Beihang University Beijing100191 China

ISBN: (纸本)9783031841477

As connected and autonomous vehicles (CAVs) become more integrated into complex networks and systems, which introduce both safety and security concerns. To address these challenges, it is crucial to develop a fusion safety approach that combines cybersecurity, functional safety, and the safety of the intended functionality. One of the key difficulties in this approach is the diversity of cyber attacks and their potential impact on CAVs. However, evaluating and analyzing the fusion safety in practical environments remains challenging. To address this, we present a simulation framework designed to assess CAV resilience against cyber attacks while maintaining safety operation. The framework can simulates vehicle’s status both in physical and cyber field. A diverse cyber attacks with various driving scenarios are supported. Based on framework’s current capability, we propose eight fusion safety evaluation metrics to measure CAV performance under attack conditions. These metrics encompass aspects of functional safety, data security and driving comfortableness. Three demonstration scenarios show the effectiveness of the proposed framework in assessing the fusion safety state of CAVs, contributing to the development of more secure and resilient fusion safety applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cyber attacks

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Interact, Instruct to Improve: A LLM-Driven Parallel Actor-Reasoner Framework for Enhancing Autonomous Vehicle Interactions

arXiv

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arXiv 2025年

作者： Fang, Shiyu Liu, Jiaqi Xu, Chengkai Lv, Chen Hang, Peng Sun, Jian College of Transportation Tongji University Shanghai201804 China State Key Lab of Intelligent Transportation System Beijing100088 China Nanyang Technological University 639798 Singapore

Autonomous Vehicles (AVs) have entered the commercialization stage, but their limited ability to interact and express intentions still poses challenges in interactions with Human-driven Vehicles (HVs). Recent advances in large language models (LLMs) enable bidirectional human-machine communication, but the conflict between slow inference speed and the need for real-time decision-making challenges practical deployment. To address these issues, this paper introduces a parallel Actor-Reasoner framework designed to enable explicit bidirectional AV-HV interactions across multiple scenarios. First, by facilitating interactions between the LLM-driven Reasoner and heterogeneous simulated HVs during training, an interaction memory database, referred to as the Actor, is established. Then, by introducing the memory partition module and the two-layer memory retrieval module, the Actor’s ability to handle heterogeneous HVs is significantly enhanced. Ablation studies and comparisons with other decision-making methods demonstrate that the proposed Actor-Reasoner framework significantly improves safety and efficiency. Finally, with the combination of the external Human-Machine Interface (eHMI) information derived from Reasoner’s reasoning and the feasible action solutions retrieved from the Actor, the effectiveness of the proposed Actor-Reasoner is confirmed in multi-scenario field interactions. Our code is available at https://***/FanGShiYuu/Actor-Reasoner. Copyright © 2025, The Authors. All rights reserved.

关键词： Magnetic levitation vehicles

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