Journal of Intelligent and Connected Vehicles最新文献

Smart Prediction-Planning Algorithm for Connected and Autonomous Vehicle Based on Social Value Orientation

Journal of Intelligent and Connected Vehicles Pub Date : 2025-03-01 DOI: 10.26599/JICV.2024.9210053

Donglei Rong;Yuefeng Wu;Wenjun Du;Chengcheng Yang;Sheng Jin;Min Xu;Fujian Wang

{"title":"Smart Prediction-Planning Algorithm for Connected and Autonomous Vehicle Based on Social Value Orientation","authors":"Donglei Rong;Yuefeng Wu;Wenjun Du;Chengcheng Yang;Sheng Jin;Min Xu;Fujian Wang","doi":"10.26599/JICV.2024.9210053","DOIUrl":"https://doi.org/10.26599/JICV.2024.9210053","url":null,"abstract":"To improve the adaptability of Connected and Automated Vehicles (CAVs) in mixed traffic, this study proposes a prediction model training indicator that comprehensively considers drivers' Social Value Orientation (SVO) and planning goals. Active Influence Factor (AIF) is used as the goal to predict the future safety loss and consistency loss of CAVs. Second, an objective function based on SVO is constructed to understand the driver's characteristics to evaluate the safety, comfort, efficiency, and consistency of candidate trajectories. The results showed that integrating SVO and consistency functions can help ensure that CAVs drive under a more stable risk potential energy field. The prediction planning model that considers SVO can improve the reliability of the CAV output trajectory to a certain extent. The prediction planning under the AIF has better accuracy and stability of the output trajectory; however, it still has strong adaptability and superiority under different sensitivity parameters. The minimum and maximum standard deviations of our model are 0.78 and 0.78 m, respectively, whereas the minimum and maximum standard deviations of the comparative model reach 2.07 and 4.56 m, respectively. The minimum standard deviation of the other comparative model reaches 1.35 m, and the maximum standard deviation reaches 4.45 m.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"8 1","pages":"1-17"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10960599","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808988","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

Safe and Efficient DRL Driving Policies Using Fuzzy Logic for Urban Lane Changing Scenarios

Journal of Intelligent and Connected Vehicles Pub Date : 2025-03-01 DOI: 10.26599/JICV.2024.9210054

Ling Han;Xiangyu Ma;Yiren Wang;Lei He;Yipeng Li;Lele Zhang;Qiang Yi

{"title":"Safe and Efficient DRL Driving Policies Using Fuzzy Logic for Urban Lane Changing Scenarios","authors":"Ling Han;Xiangyu Ma;Yiren Wang;Lei He;Yipeng Li;Lele Zhang;Qiang Yi","doi":"10.26599/JICV.2024.9210054","DOIUrl":"https://doi.org/10.26599/JICV.2024.9210054","url":null,"abstract":"Lane changing is common in driving. Thus, the possibility of traffic accidents occurring during lane changes is high given the complexity of this process. One of the primary objectives of intelligent driving is to increase a vehicle's behavior, making it more similar to that of a real driver. This study proposes a decision-making framework based on deep reinforcement learning (DRL) in a lane-changing scenario, which seeks to find a driving strategy that simultaneously considers the expected lane-changing risks and gains. First, a fuzzy logic lane-changing controller is designed. It outputs the corresponding safety and lane-change gain weights by inputting relevant driving parameters. Second, the obtained weights are brought into the constructed reward function of DRL. The model parameters are designed and trained on the basis of lane-changing behavior. Finally, we conducted experiments in a simulator to evaluate the performance of our developed algorithm in urban scenarios. To visualize and validate the estimated driving intentions, lane-changing strategies were tested under four scenarios. The results show that the average improvement in travel efficiency in the four scenarios is 19%. In addition, the average accident rate in the four scenarios increased by only 4%. We combine fuzzy logic and DRL reward functions to personify the lane-changing behavior of intelligent driving. Compared with conservative strategies that prioritize only safety, this method can considerably improve the number of lane changes and travel efficiency for autonomous vehicles (AVs) on the premise of ensuring safety. The approach provides an effective and explainable method designed for facilitating intelligent driving lane-changing behavior.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"8 1","pages":"1-13"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10960600","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808987","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

GRU-LSTM Model Based on the SSA for Short-Term Traffic Flow Prediction

Journal of Intelligent and Connected Vehicles Pub Date : 2025-03-01 DOI: 10.26599/JICV.2024.9210051

Changxi Ma;Xiaoyu Huang;Yongpeng Zhao;Tao Wang;Bo Du

{"title":"GRU-LSTM Model Based on the SSA for Short-Term Traffic Flow Prediction","authors":"Changxi Ma;Xiaoyu Huang;Yongpeng Zhao;Tao Wang;Bo Du","doi":"10.26599/JICV.2024.9210051","DOIUrl":"https://doi.org/10.26599/JICV.2024.9210051","url":null,"abstract":"The transportation department relies on accurate traffic forecasting for effective decision-making. However, determining relevant parameters for existing traffic flow prediction models poses challenges. To address this issue, this study proposes a hybrid model, sparrow search algorithm-gated recurrent unit-long short-term memory (SSA-GRU-LSTM), which leverages the SSA to optimize the GRUs and LSTM networks. The SSA is employed to identify the optimal parameters for the GRU-LSTM model, mitigating their impact on prediction accuracy. This model integrates the predictive efficiency of the GRU, LSTM's capability in temporal data analysis, and the fast convergence and global search attributes of the SSA. Comprehensive experiments are conducted to validate the proposed method via traffic flow datasets, and the results are compared with those of baseline models. The numerical results demonstrate the superior performance of the SSA-GRU-LSTM model. Compared with the baselines, the proposed model results in reductions in the root mean square error (RMSE) of 4.632%–45.206%, the mean absolute error (MAE) of 2.608%–53.327%, the mean absolute percentage error (MAPE) of 1.324%–13.723%, and an increase in <tex>$R^{2}$</tex> of 0.5%–17.5%. Consequently, the SSA-GRU-LSTM model has high prediction accuracy and measurement stability.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"8 1","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10960593","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808858","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

Long-Term Trajectory Prediction Method Based on Highway Vehicle-Following Behavior Patterns

Journal of Intelligent and Connected Vehicles Pub Date : 2025-03-01 DOI: 10.26599/JICV.2024.9210045

Zhichao An;Yimin Wu;Fan Zhang;Dong Zhang;Bolin Gao;Suying Zhang;Guang Zhou;Aoning Jia

{"title":"Long-Term Trajectory Prediction Method Based on Highway Vehicle-Following Behavior Patterns","authors":"Zhichao An;Yimin Wu;Fan Zhang;Dong Zhang;Bolin Gao;Suying Zhang;Guang Zhou;Aoning Jia","doi":"10.26599/JICV.2024.9210045","DOIUrl":"https://doi.org/10.26599/JICV.2024.9210045","url":null,"abstract":"To address existing shortcomings such as short time domains and low interpretability, this study proposes a long-term trajectory prediction model for leading vehicles that considers the impact of traffic flow. Through an analysis of trailing trajectory data from the HighD natural driving dataset, fitting relationships for the following behavior patterns were derived. Building upon the intelligent driver model (IDM), three long-term trajectory prediction models were established: acceleration delta velocity (ADV), space delta velocity intelligent driver model (SDVIDM), and space velocity intelligent driver model (SVIDM). These models were then compared with the IDM model through simulations. The results indicate that when there is one vehicle ahead, under aggressive following conditions, the ADV model outperforms the IDM model, reducing the root mean square errors in acceleration, speed, and position by 79.61%, 91.26%, and 87.82%, respectively. In scenarios with two vehicles ahead and conservative short-distance following, the SDVIDM model exhibits reductions of 83.42%, 92.85%, and 92.25%, while the SVIDM model shows reductions of 82.31%, 92.47%, and 94.02%, respectively, compared to the IDM model.","PeriodicalId":100793,"journal":{"name":"Journal of Intelligent and Connected Vehicles","volume":"8 1","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10960598","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808859","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

Use of Virtual Reality for Automated Driving Simulation

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

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