IEEE Transactions on Intelligent Vehicles最新文献_第6页

Building Supporting SLAM Community for IEEE TIV: From DHWs to Smart Academic Organizations 为 IEEE TIV 建立支持 SLAM 社区：从 DHW 到智能学术组织

IF 14 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-09-25 DOI: 10.1109/TIV.2024.3451250

Fei-Yue Wang

引用次数: 0

Safe Trajectory Optimization and Efficient-Offline Robust Model Predictive Control for Autonomous Vehicle Lane Change 自动驾驶汽车变道安全轨迹优化与高效离线鲁棒模型预测控制

IF 14.3 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-09-24 DOI: 10.1109/TIV.2024.3467111

Hung Duy Nguyen;Dongryul Kim;Anh Nguyen;Kyoungseok Han;Minh Nhat Vu

{"title":"Safe Trajectory Optimization and Efficient-Offline Robust Model Predictive Control for Autonomous Vehicle Lane Change","authors":"Hung Duy Nguyen;Dongryul Kim;Anh Nguyen;Kyoungseok Han;Minh Nhat Vu","doi":"10.1109/TIV.2024.3467111","DOIUrl":"https://doi.org/10.1109/TIV.2024.3467111","url":null,"abstract":"Driving autonomous vehicles through diverse road conditions at various high speeds poses a significant challenge. To address this challenge, we propose a hierarchical control strategy consisting of an optimization-based trajectory planner in the first layer and an efficient-offline robust method employing path tracking in the second layer. Considering vehicle parametric uncertainties, the proposed hierarchical structure addresses multiple scenarios with varying road surface conditions and velocities. In the first layer, using the Pontryagin maximum principle (PMP) flexibly with the time-to-collision (TTC) method, the motion planner generates a safe-optimal lane-change trajectory when interacting with forward vehicles and adjacent-lane vehicles to improve ride comfort while maintaining safe distances. In the second layer, the efficient offline robust model predictive control (RMPC) with terminal constraints is applied to a linear parameter varying (LPV) system. Utilizing linear matrix inequality (LMI) techniques, the optimization problem accommodates parametric uncertainties while robustly satisfying input and output constraints. To emphasize superior performance, we have considered comparing our proposed approach with several state-of-the-art methods. Therefore, comparative simulation results have shown that our safe-optimal trajectory generation is better than the Spatio-Temporal Corridors method regarding path smoothy. The proposed approach (i.e., efficient-offline RMPC) then outperforms the offline MPC method in terms of path-tracking performance and parametric uncertainty handling while outperforming the online RMPC method in terms of computational complexity reduction. Further, all methods are verified using a co-simulation and testing platform via a high-fidelity dynamics testing vehicle control software (i.e., CarSim).","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"10 6","pages":"3871-3885"},"PeriodicalIF":14.3,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10690241","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145256021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning Expert-Level Racing Strategies via Scheduled Cost Functions in Model Predictive Control 模型预测控制中利用计划成本函数学习专家级竞赛策略

IF 14.3 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-09-24 DOI: 10.1109/TIV.2024.3465598

Jonghak Bae;Jaehyun Lim;Bogyeong Suh;Jinwon Lee;Kunhee Ryu;Jinsung Kim;Jongeun Choi

{"title":"Learning Expert-Level Racing Strategies via Scheduled Cost Functions in Model Predictive Control","authors":"Jonghak Bae;Jaehyun Lim;Bogyeong Suh;Jinwon Lee;Kunhee Ryu;Jinsung Kim;Jongeun Choi","doi":"10.1109/TIV.2024.3465598","DOIUrl":"https://doi.org/10.1109/TIV.2024.3465598","url":null,"abstract":"In racing sports, driving strategies necessitate meticulous control and optimal utilization of vehicle dynamics. Model predictive control (MPC) has emerged as an effective approach for imitating expert driving strategies. Traditional MPC methods typically rely on constant cost functions, which are not optimal in dynamic environments that require track-dependent strategies. This paper introduces a novel framework that enhances the imitation of expert strategies by incorporating a scheduled cost function into the MPC. We present an inverse model predictive control (iMPC) framework, equipped with a custom MPC formulation that adeptly integrates scheduled cost functions. By employing Gaussian process (GP) regression, our framework effectively maps the connection between trajectories and their respective scheduling costs, enabling dynamic adaptation of cost functions within MPC planning. Furthermore, we present a probabilistic modeling method that combines Bayesian optimization (BO) with GP. This method is designed to create datasets that closely mimic expert-level driving behaviors, enriching the data available for training and validating our iMPC approach. We evaluate our framework by emphasizing the goodness of fit and interpretability of the reconstructed cost functions and the resulting trajectories. Compared to standard imitation learning methods, our approach stands out in its ability to accurately restore trajectories. We validate our framework using human-in-the-loop expert data and demonstrate the superiority of our methodology by comparing it with a tracking MPC.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"10 6","pages":"3827-3840"},"PeriodicalIF":14.3,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145255977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distributed Coordinated Control for QUAVs With Switching Formation Strategy 基于切换编队策略的quav分布式协调控制

IF 14.3 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-09-23 DOI: 10.1109/TIV.2024.3466152

Hongzhen Guo;Mou Chen;Shuang Shi;Zengliang Han;Mihai Lungu

引用次数: 0

Comparison of LiDAR Data Filtration Algorithms for Enhanced Situational Awareness Under Noisy Conditions 噪声条件下增强态势感知的激光雷达数据过滤算法比较

IF 14.3 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-09-23 DOI: 10.1109/TIV.2024.3466312

Makhluk Hossain Prio;Md Sohanur E Hijrat Raju;Sahil Patel;Goutam Koley

{"title":"Comparison of LiDAR Data Filtration Algorithms for Enhanced Situational Awareness Under Noisy Conditions","authors":"Makhluk Hossain Prio;Md Sohanur E Hijrat Raju;Sahil Patel;Goutam Koley","doi":"10.1109/TIV.2024.3466312","DOIUrl":"https://doi.org/10.1109/TIV.2024.3466312","url":null,"abstract":"In adverse weather conditions like fog, rain, smoke, and snow, LiDAR sensor data can become corrupted by noise, leading to missed detections or false positives. This paper presents a comparison of multiple state-of-the-art LiDAR data filtration techniques, namely Radius Outlier Removal (ROR), Dynamic Radius Outlier Removal (DROR), and Low Intensity Outlier Removal (LIOR), by evaluating their performance in addressing simulated noise, as well as realistic noise introduced by fog, smoke, and rain. The comparison is conducted by analyzing well-defined performance metrics, including, accuracy, error, precision, recall, and F-Score. Our filtration results indicate that overall performance of DROR is superior to both ROR and LIOR filtration algorithms, however, in specific short-range LiDAR imaging scenarios, the performance of LIOR can be comparable with DROR. Furthermore, this study presents a novel endeavor by establishing the relationship between performance metrics and minimum number of neighboring points (<italic>K<sub>min</sub></i>) for both ROR and DROR filtration techniques, utilizing different densities of simulated noise, as well as realistic noise introduced by foggy and smoky conditions.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"10 6","pages":"3852-3870"},"PeriodicalIF":14.3,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145255853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Safe Reinforcement Learning for Autonomous Driving by Using Disturbance-Observer-Based Control Barrier Functions 基于干扰观测器控制障碍函数的自动驾驶安全强化学习

IF 14.3 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-09-20 DOI: 10.1109/TIV.2024.3463468

Zhengyu Hou;Wenjun Liu;Alois Knoll

{"title":"Safe Reinforcement Learning for Autonomous Driving by Using Disturbance-Observer-Based Control Barrier Functions","authors":"Zhengyu Hou;Wenjun Liu;Alois Knoll","doi":"10.1109/TIV.2024.3463468","DOIUrl":"https://doi.org/10.1109/TIV.2024.3463468","url":null,"abstract":"Recently, reinforcement learning (RL) has been increasingly used in autonomous driving (AD) navigation control systems. However, most RL-based AD navigation control systems remain in the simulation stage. Its practical application is limited due to growing safety concerns. The safety of these algorithms remains uncertain when confronted with real-world disturbances and vehicle model uncertainties. To enhance the safety of RL, we propose a disturbance observer (DOB) based safe soft actor-critic (SAC) algorithm that combines the SAC algorithm with a safety constraints filter composed of DOB and control barrier function (CBF). When the SAC agent's action output is unsafe, the safety constraints filter will alter it. We employ a DOB to accurately estimate the difference between the nominal model of the vehicle and the actual model, i.e., the lumped disturbances. Then, a more accurate vehicle model can be obtained. To ensure the safety of DOB-SAC under complex and dynamically changing environmental conditions, a further predictive safety constraint is defined based on model predictive control (MPC) ideas. The safe action will be rendered using safety-critical optimal control according to the DOB compensated vehicle model, CBF, and the predictive safety constraints. We discuss the SAC architecture and training details, and investigate the effectiveness of CBF in modeling safety constraints. Joint simulations are conducted in scenarios with static obstacles and intersection scenes with dynamic obstacles.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"10 6","pages":"3782-3791"},"PeriodicalIF":14.3,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145256029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Autonomous Mining Transportation Systems: Integrating 4D mmWave Radar for Enhanced Detection of Obstructed Static Objects 自主采矿运输系统：集成4D毫米波雷达以增强对受阻静态物体的检测

IF 14.3 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-09-19 DOI: 10.1109/TIV.2024.3463968

Jianjian Yang;Tianmu Gui;Yibo Tong;Yuyuan Zhang;Qiankun Huang;Guanghui Zhao

{"title":"Autonomous Mining Transportation Systems: Integrating 4D mmWave Radar for Enhanced Detection of Obstructed Static Objects","authors":"Jianjian Yang;Tianmu Gui;Yibo Tong;Yuyuan Zhang;Qiankun Huang;Guanghui Zhao","doi":"10.1109/TIV.2024.3463968","DOIUrl":"https://doi.org/10.1109/TIV.2024.3463968","url":null,"abstract":"‘‘Mining 5.0,” in response to “Industry 5.0,” requires autonomous haulage systems to operate fully autonomously in open-pit mines. Current autonomous mining transportation systems rely on wireless transmission for edge dumping operations, which is inefficient and poses risks of potential communication loss and cybersecurity issues. Sensors such as LiDAR, cameras, and 3D mmWave radar do not support autonomous haulage to complete automation of edge dumping, as they struggle to detect obscured static obstacles and operate in harsh mining environments. We propose an innovative approach to address this challenge: integrating 4D mmWave radar into autonomous haulage systems. We have collected the world's first 4D mmWave radar dataset in the open pit mine to evaluate this approach, including four haulage operating scenarios under various lighting conditions. To quantify the precision of the 4D mmWave radar, we induce three points cloud comparison methods, a static object tracking algorithm, and point cloud image comparison to assess the system's ability to detect obscured static obstacles. Based on our findings, we conclude that using 4D mmWave radar enhances the autonomous haulage system's ability to detect obscured static obstacles in open pit mines, particularly at dumping sites.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"10 6","pages":"3792-3802"},"PeriodicalIF":14.3,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145255994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Search Area Prediction Algorithm for Tracking Missing Target in Complex Terrain Environments Using UAVs 无人机在复杂地形环境下跟踪失踪目标的搜索区域预测算法

IF 14.3 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-09-19 DOI: 10.1109/TIV.2024.3464609

Haojie Zhu;Mou Chen;Zengliang Han;Tongle Zhou

{"title":"Search Area Prediction Algorithm for Tracking Missing Target in Complex Terrain Environments Using UAVs","authors":"Haojie Zhu;Mou Chen;Zengliang Han;Tongle Zhou","doi":"10.1109/TIV.2024.3464609","DOIUrl":"https://doi.org/10.1109/TIV.2024.3464609","url":null,"abstract":"This paper presents a novel dynamic-target search area prediction (SAP) algorithm to address the challenge of tracking missing vehicles lost in complex terrain environments. The algorithm aims to improve the effectiveness of prediction after target loss. First, a target motion prediction model based on physical rules is established. This model can accurately predict the potential movement patterns of the dynamic targets. Subsequently, the rule-based model is used as expert demonstrations in the Maximum Entropy Deep Inverse Reinforcement Learning (MEDIRL) algorithm. And the real trajectory data of the target will be used to fine-tune the network structure model obtained by Soft Actor-Critic (SAC) algorithm. This step can quickly fit the driving intentions and preferences of the current target. Furthermore, the algorithm predicts the distribution of target positions and generates a probability-based heatmap, reflecting the likelihood of target presence across the terrain. To determine the area containing the highest probability of target, a spatial sliding adaptive window (SSAW) method is employed. This approach can dynamically adjust the area based on the heatmap, focusing on the most probable region for target presence. Simulation results demonstrate that the proposed algorithm effectively predicts target areas with a higher probability of containing the targets. These predictions provide valuable reference for target tracking in mountainous terrain environments.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"10 6","pages":"3814-3826"},"PeriodicalIF":14.3,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145255967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automated Driving Control in Mixed Traffic Flow Using V2V Communication 基于V2V通信的混合交通流自动驾驶控制

IF 14.3 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-09-18 DOI: 10.1109/TIV.2024.3463170

Euntak Lee;Bongsoo Son;Wongil Kim

{"title":"Automated Driving Control in Mixed Traffic Flow Using V2V Communication","authors":"Euntak Lee;Bongsoo Son;Wongil Kim","doi":"10.1109/TIV.2024.3463170","DOIUrl":"https://doi.org/10.1109/TIV.2024.3463170","url":null,"abstract":"Automated vehicles (AVs) are expected to transform the future of intelligent transportation systems. To enhance the feasibility of integrating AVs with human-driven vehicles (HDVs), AV technology needs to accurately assess driving situations and operate within human expectations. Car-following (CF) and lane-changing (LC) are key models for improving AV driving control. However, most CF and LC models were developed separately and tested on a few vehicle samples, without considering their impacts on traffic flow. Therefore, this study develops an automated driving control model that simultaneously generates CF and LC decisions. The model utilizes vehicle-to-vehicle (V2V) technology that incorporates thirty-four features from the subject vehicle and six surrounding vehicles. A hybrid deep learning model is proposed using LSTM with two parallel structures for each maneuver. By applying the NGSIM dataset, the proposed model outperformed all other models with the accuracy of matching ratios of 70.5%, 96.1%, and 98.4% at acceleration, speed, and position levels, respectively. Traffic flow simulations were conducted, validating that vehicles perform driving behaviors within human expectations and the simulations are reliable for describing the actual traffic flow. For comfort and safe AV driving, jerk, or change rate of acceleration, is restricted within a certain range. With the AV rate increasing, traffic congestion was mitigated with speed increasing from 47.5 to 74.9 km/h and density decreasing from 36.8 to 25.4 veh/km. However, traffic safety remained unstable unless the rate reached 100%. This study contributes to the development of V2V-based AV driving control in mixed traffic scenarios.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"10 6","pages":"3768-3781"},"PeriodicalIF":14.3,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145255857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Safe Reinforcement Learning-Based Visual Servoing Control for Quadrotors Tracking Unknown Ground Vehicles 基于安全强化学习的四旋翼跟踪未知地面车辆视觉伺服控制

IF 14.3 1区工程技术

IEEE Transactions on Intelligent Vehicles Pub Date : 2024-09-18 DOI: 10.1109/TIV.2024.3464094

Xinning Yi;Hao Liu;Yueying Wang;Haibin Duan;Kimon P. Valavanis

引用次数: 0