Journal of Intelligent and Connected Vehicles最新文献_第2页

Use of Virtual Reality for Automated Driving Simulation 虚拟现实技术在自动驾驶仿真中的应用

Journal of Intelligent and Connected Vehicles Pub Date : 2025-03-01 DOI: 10.26599/JICV.2024.9210048

Tanjida Tahmina;Mark Fuchs;Chao Shi

引用次数: 0

Trajectory Prediction of Human-Driven Vehicles on the Basis of Risk Field Theory and Interaction Multiple Models 基于风险场理论和交互多重模型的人驾驶车辆轨迹预测

Journal of Intelligent and Connected Vehicles Pub Date : 2025-03-01 DOI: 10.26599/JICV.2024.9210052

Zhaojie Wang;Guangquan Lu;Jinghua Wang;Haitian Tan;Renjing Tang

引用次数: 0

Decision Making and Control of Autonomous Vehicles Under the Condition of Front Vehicle Sideslip 前车侧滑情况下自动驾驶汽车的决策与控制

Journal of Intelligent and Connected Vehicles Pub Date : 2024-12-01 DOI: 10.26599/JICV.2023.9210044

Jian Chen;Yunfeng Xiang;Yugong Luo;Keqiang Li;Xiaomin Lian

{"title":"Decision Making and Control of Autonomous Vehicles Under the Condition of Front Vehicle Sideslip","authors":"Jian Chen;Yunfeng Xiang;Yugong Luo;Keqiang Li;Xiaomin Lian","doi":"10.26599/JICV.2023.9210044","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210044","url":null,"abstract":"The behaviors of front vehicles are important factors that can influence the driving safety of autonomous vehicles on highways. This situation poses a serious threat to the security of autonomous vehicles, especially when front vehicle sideslip occurs. To address this problem, a decision-making approach can be used to promote the emergency obstacle avoidance capability of autonomous vehicles. First, the front sideslip vehicle trajectory was predicted by the kinematic models Constant Acceleration (CA), Constant Turn Rate and Velocity (CTRV), and Constant Turn Rate and Acceleration (CTRA) based on the front vehicle sideslip identification results. The CTRA prediction approach is chosen by comparing the prediction errors of the three models. To enhance the obstacle avoidance ability of autonomous vehicles, a novel trajectory planning method based on a driving characteristic vector is proposed. Model prediction control (MPC) is used to track the planned trajectory. Finally, the cosimulation platform of Simulink and Carsim was built. The simulation results show that autonomous vehicles can avoid collisions with front sideslip vehicles through the proposed approach, and the proposed trajectory planning approach has better obstacle avoidance ability than does the traditional approach.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"7 4","pages":"248-257"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10823098","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancements and Prospects in Multisensor Fusion for Autonomous Driving 自动驾驶中多传感器融合的研究进展与展望

Journal of Intelligent and Connected Vehicles Pub Date : 2024-12-01 DOI: 10.26599/JICV.2023.9210042

Chen Tu;Liang Wang;Jaehyuck Lim;Inhi Kim

引用次数: 0

Improving the Representation of Traffic States: A Novel Method for Link Selection of Urban Road Networks 改进交通状态表征：城市道路网络链路选择的一种新方法

Journal of Intelligent and Connected Vehicles Pub Date : 2024-12-01 DOI: 10.26599/JICV.2023.9210047

Syed Muzammil Abbas Rizvi;Bernhard Friedrich

{"title":"Improving the Representation of Traffic States: A Novel Method for Link Selection of Urban Road Networks","authors":"Syed Muzammil Abbas Rizvi;Bernhard Friedrich","doi":"10.26599/JICV.2023.9210047","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210047","url":null,"abstract":"The macroscopic fundamental diagram (MFD) represents the aggregated traffic states of a road network. However, the uniqueness of an empirically estimated MFD cannot be guaranteed due to the problem of link selection. Instationarity and varying flow patterns make it difficult to select link flows that are representative of the traffic state in the whole network. This study developed a new method for selecting links equipped with loop detectors that represent a particular traffic state of a road network. The method utilizes a metric of heterogeneity characterizing the role of a network link over the time of day. The dispersion metric indicates the heterogeneity in traffic states and the dynamic role of each time interval. It ranks links based on the heterogeneity-weighted saturation level, with the highest-rank links representing the most homogeneous subset of sample links. This study compared classical and proposed dynamic weights using loop detector data from Zurich and London and a simulated network. Sample links were selected based on different saturation levels, and the saturation level was associated with the heterogeneity level to identify the links creating heterogeneity in the road network.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"7 4","pages":"266-278"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10823100","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Extracting Networkwide Road Segment Location, Direction, and Turning Movement Rules From Global Positioning System Vehicle Trajectory Data for Macrosimulation 从全球定位系统车辆轨迹数据中提取网络范围内道路段位置、方向和转向运动规则用于宏观仿真

Journal of Intelligent and Connected Vehicles Pub Date : 2024-12-01 DOI: 10.26599/JICV.2023.9210046

Adham Badran;Ahmed El-Geneidy;Luis Miranda-Moreno

{"title":"Extracting Networkwide Road Segment Location, Direction, and Turning Movement Rules From Global Positioning System Vehicle Trajectory Data for Macrosimulation","authors":"Adham Badran;Ahmed El-Geneidy;Luis Miranda-Moreno","doi":"10.26599/JICV.2023.9210046","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210046","url":null,"abstract":"The emergence of road users' global positioning system (GPS) trajectory data is attracting increasing research interest in knowledge discovery to improve transport planning-related methods and tools. In fact, the widespread use of GPS-enabled smartphones and the mobile internet has increased the availability and size of such data. With the increase in GPS data coverage and availability, some research has expanded its use to estimate state-wide vehicle-miles travelled, to classify driving maneuvers for road safety assessment, or to estimate environmental performance indicators, such as vehicular fuel consumption and pollution emissions. In computer science, research has used GPS data to infer road network maps. Although the inferred maps provide a correct topology and connectivity, they lack the essential details to be used for transport modeling. Therefore, this work proposes a method to extract network-wide road direction and turning movement rules. In addition, building a road network model under the widely used macroscopic transport modeling software serves as a proof of concept. A sensitivity analysis was carried out to determine the output quality and recommend future improvements. Road segment geometry and directionality were extracted accurately (case study accuracy of 95\u0000<sup>%</sup>\u0000); however, turning movement rules can be extracted more accurately using a larger GPS vehicle trajectory sample (case study accuracy of 68%).","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"7 4","pages":"258-265"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10823099","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Application of the Traffic Fundamental Diagram to Assess Detector Performance 交通基本图在评估检测器性能中的应用

Journal of Intelligent and Connected Vehicles Pub Date : 2024-12-01 DOI: 10.26599/JICV.2023.9210050

Katherine Riffle;Edward J. Smaglik;Steven Procaccio;Steven R. Gehrke;Brendan J. Russo;David Hurwitz

引用次数: 0

Coordinated Optimization of Signal Timing for Intersections with Dynamic Shared Through- and Right-Turn Lanes 动态共享直行和右转车道交叉口信号配时的协调优化

Journal of Intelligent and Connected Vehicles Pub Date : 2024-09-26 DOI: 10.26599/JICV.2023.9210038

Zhe Zheng;Jian Yuan;Kun An;Nan Zheng;Wanjing Ma

{"title":"Coordinated Optimization of Signal Timing for Intersections with Dynamic Shared Through- and Right-Turn Lanes","authors":"Zhe Zheng;Jian Yuan;Kun An;Nan Zheng;Wanjing Ma","doi":"10.26599/JICV.2023.9210038","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210038","url":null,"abstract":"Through and right-turn shared lanes are widely designed to increase the capacity of through traffic, but they can also cause delays for right-turn vehicles. This study presents a dynamic control method for a shared lane that prioritizes right-turn vehicles at the beginning of the cycle and subsequently allows through traffic to queue in the shared lane for saturated discharge. The traffic wave model is employed to reveal the dynamics of the traffic flow under this control and to derive the relationships among major traffic parameters. Constrained by the major relationship, a linear programming approach to minimize the total queue length is developed to determine the proper values of control parameters, including the shared area length, subordinate signal time lag, and shared or exclusive duration. A sensitivity analysis of the control parameters for different arrival rates and flow ratios is performed. Comparisons are conducted among the dynamic shared lane, the fixed exclusive lane, and the fixed shared lane. The results show that the dynamic control method results in a lower delay for both through and total traffic.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"7 3","pages":"219-228"},"PeriodicalIF":0.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10695162","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Spectrum Quantification-Based Safety Efficiency Evaluation of Autonomous Vehicle Under Random Cut-in Scenarios 基于频谱量化的随机切入场景下自动驾驶汽车安全效率评估

Journal of Intelligent and Connected Vehicles Pub Date : 2024-09-26 DOI: 10.26599/JICV.2023.9210035

Jiang Chen;Weiwei Zhang;Miao Liu;Xiaolan Wang;Jun Gong;Jun Li;Boqi Li;Jiejie Xu

{"title":"Spectrum Quantification-Based Safety Efficiency Evaluation of Autonomous Vehicle Under Random Cut-in Scenarios","authors":"Jiang Chen;Weiwei Zhang;Miao Liu;Xiaolan Wang;Jun Gong;Jun Li;Boqi Li;Jiejie Xu","doi":"10.26599/JICV.2023.9210035","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210035","url":null,"abstract":"Continuous-scale trusted safety efficiency evaluation is crucial for the agile development and robust validation of autonomous vehicle intelligence. While the UN R157 Regulation evaluates automated lane-keeping system (ALKS) performance baselines through safe collision plots (SCPs) in various scenario clusters, quantifying the specific ALKS safety efficiency remains challenging. We propose a spectrum quantification approach to evaluate the safety efficiency of autonomous vehicles in cut-in scenarios. First, we collected speed-distance data under different cut-in scenarios and extracted essential spectral features to indicate the vehicle motion parameters during the cut-in process. Second, by utilizing Fourier analysis, a spectral analysis model was built to quantify and analyze the vehicle motion characteristics, providing insights into scenario safety. Finally, we created approximate analytical equations for the normalized disturbance frequencies in the nonlinear response scenarios of autonomous driving systems by combining the SCP with a frequency spectrum analysis model. The results showed that the normalized disturbance frequency in the cut-in scenario was approximately 0.2. When the relative longitudinal distance and speed of the vehicle are the same, if the cut-in speed of the cut-in vehicle is larger, the normalized disturbance frequency is higher, indicating that the cut-in process of the autonomous vehicle is more dangerous and may trigger a collision.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"7 3","pages":"205-218"},"PeriodicalIF":0.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10695163","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142323054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Development of Deep-Learning-Based Autonomous Agents for Low-Speed Maneuvering in Unity 在 Unity 中开发基于深度学习的低速操纵自主机器人

Journal of Intelligent and Connected Vehicles Pub Date : 2024-09-26 DOI: 10.26599/JICV.2023.9210039

Riccardo Berta;Luca Lazzaroni;Alessio Capello;Marianna Cossu;Luca Forneris;Alessandro Pighetti;Francesco Bellotti

{"title":"Development of Deep-Learning-Based Autonomous Agents for Low-Speed Maneuvering in Unity","authors":"Riccardo Berta;Luca Lazzaroni;Alessio Capello;Marianna Cossu;Luca Forneris;Alessandro Pighetti;Francesco Bellotti","doi":"10.26599/JICV.2023.9210039","DOIUrl":"https://doi.org/10.26599/JICV.2023.9210039","url":null,"abstract":"This study provides a systematic analysis of the resource-consuming training of deep reinforcement-learning (DRL) agents for simulated low-speed automated driving (AD). In Unity, this study established two case studies: garage parking and navigating an obstacle-dense area. Our analysis involves training a path-planning agent with real-time-only sensor information. This study addresses research questions insufficiently covered in the literature, exploring curriculum learning (CL), agent generalization (knowledge transfer), computation distribution (CPU vs. GPU), and mapless navigation. CL proved necessary for the garage scenario and beneficial for obstacle avoidance. It involved adjustments at different stages, including terminal conditions, environment complexity, and reward function hyperparameters, guided by their evolution in multiple training attempts. Fine-tuning the simulation tick and decision period parameters was crucial for effective training. The abstraction of high-level concepts (e.g., obstacle avoidance) necessitates training the agent in sufficiently complex environments in terms of the number of obstacles. While blogs and forums discuss training machine learning models in Unity, a lack of scientific articles on DRL agents for AD persists. However, since agent development requires considerable training time and difficult procedures, there is a growing need to support such research through scientific means. In addition to our findings, we contribute to the R&D community by providing our environment with open sources.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"7 3","pages":"229-244"},"PeriodicalIF":0.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10695161","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0