In recent years, open-pit mining has seen significant advancement, the cooperative operation of various specialized machinery substantially enhancing the efficiency of mineral extraction. However, the harsh environment and complex conditions in open-pit mines present substantial challenges for the implementation of autonomous transportation systems. This research introduces a novel paradigm that integrates Scenario Engineering (SE) with autonomous transportation systems to significantly improve the trustworthiness, robustness, and efficiency in open-pit mines by incorporating the four key components of SE, including Scenario Feature Extractor, Intelligence and Index, Calibration and Certification, and Verification and Validation. This paradigm has been validated in two famous open-pit mines, the experiment results demonstrate marked improvements in robustness, trustworthiness, and efficiency. By enhancing the capacity, scalability, and diversity of autonomous transportation, this paradigm fosters the integration of SE and parallel driving and finally propels the achievement of the ‘6S’ objectives.
近年来，露天采矿业取得了长足的进步，各种专用机械的协同作业大大提高了矿物开采的效率。然而，露天矿环境恶劣、条件复杂，给自主运输系统的实施带来了巨大挑战。本研究提出了一种将情景工程（SE）与自主运输系统相结合的新范例，通过结合情景工程的四个关键组成部分，包括情景特征提取器、智能与索引、校准与认证以及验证与确认，显著提高露天矿的可信度、稳健性和效率。这一范例已在两个著名的露天矿中得到验证，实验结果表明其在稳健性、可信度和效率方面都有明显改善。通过提高自主运输的能力、可扩展性和多样性，该范例促进了 SE 与并行驾驶的整合，并最终推动了 "6S "目标的实现。
{"title":"Scenario Engineering for Autonomous Transportation: A New Stage in Open-Pit Mines","authors":"Siyu Teng;Xuan Li;Yuchen Li;Lingxi Li;Zhe Xuanyuan;Yunfeng Ai;Long Chen","doi":"10.1109/TIV.2024.3373495","DOIUrl":"https://doi.org/10.1109/TIV.2024.3373495","url":null,"abstract":"In recent years, open-pit mining has seen significant advancement, the cooperative operation of various specialized machinery substantially enhancing the efficiency of mineral extraction. However, the harsh environment and complex conditions in open-pit mines present substantial challenges for the implementation of autonomous transportation systems. This research introduces a novel paradigm that integrates Scenario Engineering (SE) with autonomous transportation systems to significantly improve the trustworthiness, robustness, and efficiency in open-pit mines by incorporating the four key components of SE, including Scenario Feature Extractor, Intelligence and Index, Calibration and Certification, and Verification and Validation. This paradigm has been validated in two famous open-pit mines, the experiment results demonstrate marked improvements in robustness, trustworthiness, and efficiency. By enhancing the capacity, scalability, and diversity of autonomous transportation, this paradigm fosters the integration of SE and parallel driving and finally propels the achievement of the ‘6S’ objectives.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 3","pages":"4394-4404"},"PeriodicalIF":8.2,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140820331","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}
Pub Date : 2024-03-08DOI: 10.1109/TIV.2024.3398215
Güner Tatar;Salih Bayar;İhsan Çiçek
This study introduces a new method to enhance ADAS's safety and error prevention capabilities in intelligent vehicles. We address the significant computational and memory demands required for real-time video processing by leveraging BDD100 K, KITTI, CityScape, and Waymo datasets. Our proposed hardware-software co-design integrates an MPSoC-FPGA accelerator for real-time multi-learning models. Our experimental results exhibit that, despite an increase in ADAS tasks and model parameters compared to the state-of-the-art studies, our model achieves 24,715 GOP performance with 4% lower power consumption (6.920 W) and 18.86% less logic resource consumption. The model processes highway scenes at 22.45 FPS and attains 50.06% mAP for object detection, 57.05% mIoU for segmentation, 43.76% mIoU for lane detection, 81.63% IoU for drivable area segmentation, and 9.78% SILog error for depth estimation. These findings confirm the system's effectiveness, reliability, and adaptability for ADAS applications and represent a significant advancement in intelligent vehicle technology, with the potential for further improvements in accuracy and memory efficiency.
{"title":"Real-Time Multi-Learning Deep Neural Network on an MPSoC-FPGA for Intelligent Vehicles: Harnessing Hardware Acceleration With Pipeline","authors":"Güner Tatar;Salih Bayar;İhsan Çiçek","doi":"10.1109/TIV.2024.3398215","DOIUrl":"https://doi.org/10.1109/TIV.2024.3398215","url":null,"abstract":"This study introduces a new method to enhance ADAS's safety and error prevention capabilities in intelligent vehicles. We address the significant computational and memory demands required for real-time video processing by leveraging BDD100 K, KITTI, CityScape, and Waymo datasets. Our proposed hardware-software co-design integrates an MPSoC-FPGA accelerator for real-time multi-learning models. Our experimental results exhibit that, despite an increase in ADAS tasks and model parameters compared to the state-of-the-art studies, our model achieves 24,715 GOP performance with 4% lower power consumption (6.920 W) and 18.86% less logic resource consumption. The model processes highway scenes at 22.45 FPS and attains 50.06% mAP for object detection, 57.05% mIoU for segmentation, 43.76% mIoU for lane detection, 81.63% IoU for drivable area segmentation, and 9.78% SILog error for depth estimation. These findings confirm the system's effectiveness, reliability, and adaptability for ADAS applications and represent a significant advancement in intelligent vehicle technology, with the potential for further improvements in accuracy and memory efficiency.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 6","pages":"5021-5032"},"PeriodicalIF":14.0,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141965419","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}
Pub Date : 2024-03-06DOI: 10.1109/TIV.2024.3374044
Yueyuan Li;Wei Yuan;Songan Zhang;Weihao Yan;Qiyuan Shen;Chunxiang Wang;Ming Yang
Simulators play a crucial role in autonomous driving, offering significant time, cost, and labor savings. Over the past few years, the number of simulators for autonomous driving has grown substantially. However, there is a growing concern about the validity of algorithms developed and evaluated in simulators, indicating a need for a thorough analysis of the development status of the simulators. To address existing gaps in research, this paper undertakes a comprehensive review of the history of simulators, proposes a utility-based taxonomy, and investigates the critical issues within open-source simulators. Analysis of the past thirty years' development trajectory reveals a trend characterized by an increase in open-source simulators and an expansion of their functionality scope. The categorization of simulators based on feature functionalities delineates five primary classes: traffic flow, sensory data, driving policy, vehicle dynamics, and comprehensive simulators. Furthermore, the paper identifies critical unresolved issues in open-source simulators, including concerns regarding the fidelity of sensory data, representation of traffic scenarios, and accuracy in vehicle dynamics simulation, all of which have the potential to undermine experimental confidence. Additionally, challenges in data format inconsistency, labor-intensive map construction processes, sluggish step updating, and insufficient support for Hardware-In-the-Loop testing are discussed as hindrances to experimental efficiency. In light of these findings, the survey furnishes task-oriented recommendations to aid in the selection of simulators, taking into account factors such as accessibility, maintenance status, and quality, while highlighting the inherent limitations of existing open-source simulators in validating algorithms and facilitating real-world experimentation.
{"title":"Choose Your Simulator Wisely: A Review on Open-Source Simulators for Autonomous Driving","authors":"Yueyuan Li;Wei Yuan;Songan Zhang;Weihao Yan;Qiyuan Shen;Chunxiang Wang;Ming Yang","doi":"10.1109/TIV.2024.3374044","DOIUrl":"https://doi.org/10.1109/TIV.2024.3374044","url":null,"abstract":"Simulators play a crucial role in autonomous driving, offering significant time, cost, and labor savings. Over the past few years, the number of simulators for autonomous driving has grown substantially. However, there is a growing concern about the validity of algorithms developed and evaluated in simulators, indicating a need for a thorough analysis of the development status of the simulators. To address existing gaps in research, this paper undertakes a comprehensive review of the history of simulators, proposes a utility-based taxonomy, and investigates the critical issues within open-source simulators. Analysis of the past thirty years' development trajectory reveals a trend characterized by an increase in open-source simulators and an expansion of their functionality scope. The categorization of simulators based on feature functionalities delineates five primary classes: traffic flow, sensory data, driving policy, vehicle dynamics, and comprehensive simulators. Furthermore, the paper identifies critical unresolved issues in open-source simulators, including concerns regarding the fidelity of sensory data, representation of traffic scenarios, and accuracy in vehicle dynamics simulation, all of which have the potential to undermine experimental confidence. Additionally, challenges in data format inconsistency, labor-intensive map construction processes, sluggish step updating, and insufficient support for Hardware-In-the-Loop testing are discussed as hindrances to experimental efficiency. In light of these findings, the survey furnishes task-oriented recommendations to aid in the selection of simulators, taking into account factors such as accessibility, maintenance status, and quality, while highlighting the inherent limitations of existing open-source simulators in validating algorithms and facilitating real-world experimentation.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 5","pages":"4861-4876"},"PeriodicalIF":14.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141964748","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}
Pub Date : 2024-03-05DOI: 10.1109/TIV.2024.3372625
Haochen Liu;Zhiyu Huang;Xiaoyu Mo;Chen Lv
Decision-making for urban autonomous driving is challenging due to the stochastic nature of interactive traffic participants and the complexity of road structures. Although reinforcement learning (RL)-based decision-making schemes are promising to handle urban driving scenarios, they suffer from low sample efficiency and poor adaptability. In this paper, we propose the Scene-Rep Transformer to enhance RL decision-making capabilities through improved scene representation encoding and sequential predictive latent distillation. Specifically, a multi-stage Transformer (MST) encoder is constructed to model not only the interaction awareness between the ego vehicle and its neighbors but also intention awareness between the agents and their candidate routes. A sequential latent Transformer (SLT) with self-supervised learning objectives is employed to distill future predictive information into the latent scene representation, in order to reduce the exploration space and speed up training. The final decision-making module based on soft actor-critic (SAC) takes as input the refined latent scene representation from the Scene-Rep Transformer and generates decisions. The framework is validated in five challenging simulated urban scenarios with dense traffic, and its performance is manifested quantitatively by substantial improvements in data efficiency and performance in terms of success rate, safety, and efficiency. Qualitative results reveal that our framework is able to extract the intentions of neighbor agents, enabling better decision-making and more diversified driving behaviors.
{"title":"Augmenting Reinforcement Learning With Transformer-Based Scene Representation Learning for Decision-Making of Autonomous Driving","authors":"Haochen Liu;Zhiyu Huang;Xiaoyu Mo;Chen Lv","doi":"10.1109/TIV.2024.3372625","DOIUrl":"https://doi.org/10.1109/TIV.2024.3372625","url":null,"abstract":"Decision-making for urban autonomous driving is challenging due to the stochastic nature of interactive traffic participants and the complexity of road structures. Although reinforcement learning (RL)-based decision-making schemes are promising to handle urban driving scenarios, they suffer from low sample efficiency and poor adaptability. In this paper, we propose the Scene-Rep Transformer to enhance RL decision-making capabilities through improved scene representation encoding and sequential predictive latent distillation. Specifically, a multi-stage Transformer (MST) encoder is constructed to model not only the interaction awareness between the ego vehicle and its neighbors but also intention awareness between the agents and their candidate routes. A sequential latent Transformer (SLT) with self-supervised learning objectives is employed to distill future predictive information into the latent scene representation, in order to reduce the exploration space and speed up training. The final decision-making module based on soft actor-critic (SAC) takes as input the refined latent scene representation from the Scene-Rep Transformer and generates decisions. The framework is validated in five challenging simulated urban scenarios with dense traffic, and its performance is manifested quantitatively by substantial improvements in data efficiency and performance in terms of success rate, safety, and efficiency. Qualitative results reveal that our framework is able to extract the intentions of neighbor agents, enabling better decision-making and more diversified driving behaviors.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 3","pages":"4405-4421"},"PeriodicalIF":8.2,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140820328","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}
Pub Date : 2024-03-05DOI: 10.1109/TIV.2024.3373012
Hafiz Muhammad Yasir Naeem;Aamer Iqbal Bhatti;Yasir Awais Butt;Qadeer Ahmed;Xiaoshan Bai
Adopting energy-efficient driving practices can harness the full benefits of EVs. This work uses a multi-objective optimization strategy to perform eco-driving to reduce the energy consumption of EVs and to prolong the health of batteries. The problem jointly considers constraints of conflicting nature; such as traffic signals, preceding vehicles, limitations on speed and acceleration, checks on input torque and its rate of change and bounds on battery's SoC and charging/discharging rates. This research also explores how adhering strictly to one constraint may compromise other constraints. A comprehensive control strategy using MPC is adopted to formulate eco-driving as nonlinear programming and to achieve a realistic and optimal solution. The proposed strategy has successfully achieved eco-driving along with satisfying all the conflicting constraints in uncertain environmental conditions. Furthermore, results are compared with PMP to validate the optimal solution. SoH analysis indicates that the inclusion of battery-related constraints improves the battery's health. Finally, Lyapunov stability analysis is conducted to check the systems' stability with parametric uncertainty.
{"title":"Energy Efficient Solution for Connected Electric Vehicle and Battery Health Management Using Eco-Driving Under Uncertain Environmental Conditions","authors":"Hafiz Muhammad Yasir Naeem;Aamer Iqbal Bhatti;Yasir Awais Butt;Qadeer Ahmed;Xiaoshan Bai","doi":"10.1109/TIV.2024.3373012","DOIUrl":"https://doi.org/10.1109/TIV.2024.3373012","url":null,"abstract":"Adopting energy-efficient driving practices can harness the full benefits of EVs. This work uses a multi-objective optimization strategy to perform eco-driving to reduce the energy consumption of EVs and to prolong the health of batteries. The problem jointly considers constraints of conflicting nature; such as traffic signals, preceding vehicles, limitations on speed and acceleration, checks on input torque and its rate of change and bounds on battery's SoC and charging/discharging rates. This research also explores how adhering strictly to one constraint may compromise other constraints. A comprehensive control strategy using MPC is adopted to formulate eco-driving as nonlinear programming and to achieve a realistic and optimal solution. The proposed strategy has successfully achieved eco-driving along with satisfying all the conflicting constraints in uncertain environmental conditions. Furthermore, results are compared with PMP to validate the optimal solution. SoH analysis indicates that the inclusion of battery-related constraints improves the battery's health. Finally, Lyapunov stability analysis is conducted to check the systems' stability with parametric uncertainty.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 4","pages":"4621-4631"},"PeriodicalIF":8.2,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141315189","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}
Pub Date : 2024-03-04DOI: 10.1109/TIV.2024.3372522
Jingwei Lu;Lefei Li;Fei-Yue Wang
A novel event-triggered control (ETC) method, called deep event-triggered parallel control (deep-ETPC), is presented to achieve path tracking for comfortable autonomous driving (CAD) using parallel control and deep deterministic policy gradient (DDPG). Based on parallel control, the developed deep-ETPC method constructs a dynamic control policy by introducing variation rates of controls. By employing variation rates of controls, the developed deep-ETPC method is capable of indicating communication loss and comfortable driving indices in the reward, and then enables reinforcement learning (RL) agents to learn comfortable ETC driving policies directly. Moreover, the communication loss, which reflects ETC, is integrated into the reward, so there is no need to additionally design/train triggering conditions, which can be considered a type of multi-tasking learning. Furthermore, an ETPC-oriented DDPG algorithm is developed to achieve the developed deep-ETPC method, making DDPG applicable to ETC. Empirical results, including tracking a simple straight line trajectory and a complicated sinusoidal trajectory, demonstrate the effectiveness of the developed deep-ETPC method.
{"title":"Event-Triggered Parallel Control Using Deep Reinforcement Learning With Application to Comfortable Autonomous Driving","authors":"Jingwei Lu;Lefei Li;Fei-Yue Wang","doi":"10.1109/TIV.2024.3372522","DOIUrl":"https://doi.org/10.1109/TIV.2024.3372522","url":null,"abstract":"A novel event-triggered control (ETC) method, called deep event-triggered parallel control (deep-ETPC), is presented to achieve path tracking for comfortable autonomous driving (CAD) using parallel control and deep deterministic policy gradient (DDPG). Based on parallel control, the developed deep-ETPC method constructs a dynamic control policy by introducing variation rates of controls. By employing variation rates of controls, the developed deep-ETPC method is capable of indicating communication loss and comfortable driving indices in the reward, and then enables reinforcement learning (RL) agents to learn comfortable ETC driving policies directly. Moreover, the communication loss, which reflects ETC, is integrated into the reward, so there is no need to additionally design/train triggering conditions, which can be considered a type of multi-tasking learning. Furthermore, an ETPC-oriented DDPG algorithm is developed to achieve the developed deep-ETPC method, making DDPG applicable to ETC. Empirical results, including tracking a simple straight line trajectory and a complicated sinusoidal trajectory, demonstrate the effectiveness of the developed deep-ETPC method.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 3","pages":"4470-4479"},"PeriodicalIF":8.2,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140820420","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}
Pub Date : 2024-03-04DOI: 10.1109/TIV.2024.3372590
Yogesh Kumar;Bassam Pervez Shamsi;Sayan Basu Roy;Sujit P B
In this paper, we design and validate a kinematic controller for a quadrotor tracking a planar moving target using image-based visual servoing (IBVS). Most of the current literature on IBVS for moving targets often consider restrictive assumptions on the target dynamics that limits its generalizability for any arbitrary motion. We propose a model-free target velocity estimator augmented kinematic controller based on appropriately derived feature dynamics in a virtual image plane. We show how the inner-loop mismatch affects the kinematic controller performance through a comprehensive theoretical analysis based on the Lyapunov direct method. We prove that the system errors converge exponentially to an ultimate bound in general and asymptotically to zero for the purely translational and constant target motions and vanishing inner-loop mismatch. Extensive simulations, including model-in-the-loop and software-in-the-loop settings, along with experimental validation in an outdoor environment, confirm the utility of the proposed visual servoing technique.
{"title":"Tracking a Planar Target Using Image-Based Visual Servoing Technique","authors":"Yogesh Kumar;Bassam Pervez Shamsi;Sayan Basu Roy;Sujit P B","doi":"10.1109/TIV.2024.3372590","DOIUrl":"https://doi.org/10.1109/TIV.2024.3372590","url":null,"abstract":"In this paper, we design and validate a kinematic controller for a quadrotor tracking a planar moving target using image-based visual servoing (IBVS). Most of the current literature on IBVS for moving targets often consider restrictive assumptions on the target dynamics that limits its generalizability for any arbitrary motion. We propose a model-free target velocity estimator augmented kinematic controller based on appropriately derived feature dynamics in a virtual image plane. We show how the inner-loop mismatch affects the kinematic controller performance through a comprehensive theoretical analysis based on the Lyapunov direct method. We prove that the system errors converge exponentially to an ultimate bound in general and asymptotically to zero for the purely translational and constant target motions and vanishing inner-loop mismatch. Extensive simulations, including model-in-the-loop and software-in-the-loop settings, along with experimental validation in an outdoor environment, confirm the utility of the proposed visual servoing technique.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 3","pages":"4362-4372"},"PeriodicalIF":8.2,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140820424","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}
Pub Date : 2024-03-04DOI: 10.1109/TIV.2024.3372652
Yue Jiang;Zhongkui Li
This paper addresses cooperative target encircling of multiple autonomous surface vehicles (ASVs) with private and potentially competitive objectives. A fully distributed encircling control approach is proposed based on noncooperative games. Specifically, a fully distributed estimator with an adaptive gain is developed to estimate the target information without using global state or topology knowledge. Based on a low-frequency learning technique, a fuzzy predictor is presented to approximate the unknown vehicle kinematics induced by uncertain nonlinearities and environmental disturbances. By decoupling the cooperative target encircling into an encircling task and a spacing task, an encircling control law and a spacing control law are designed based on fully distributed Nash equilibrium seeking for achieving the private control objective of each ASV. The input-to-state stability of the closed-loop system is proven via cascade analysis. Simulation results are provided to illustrate the effectiveness of the noncooperative game-based control method for ASVs in circumnavigation missions.
本文论述了多个自主水面飞行器（ASV）合作包围目标的问题，这些飞行器具有私人目标和潜在竞争目标。本文提出了一种基于非合作博弈的全分布式包围控制方法。具体来说，开发了一种具有自适应增益的全分布式估计器，以在不使用全局状态或拓扑知识的情况下估计目标信息。在低频学习技术的基础上，提出了一种模糊预测器，用于近似由不确定非线性和环境干扰引起的未知车辆运动学。通过将合作目标包围解耦为包围任务和间隔任务，设计了基于全分布纳什均衡寻求的包围控制法则和间隔控制法则，以实现每个 ASV 的私有控制目标。通过级联分析证明了闭环系统的输入到状态稳定性。仿真结果说明了基于非合作博弈的控制方法在 ASV 环绕飞行任务中的有效性。
{"title":"Fully Distributed Target Encircling Control of Autonomous Surface Vehicles Based on Noncooperative Games","authors":"Yue Jiang;Zhongkui Li","doi":"10.1109/TIV.2024.3372652","DOIUrl":"https://doi.org/10.1109/TIV.2024.3372652","url":null,"abstract":"This paper addresses cooperative target encircling of multiple autonomous surface vehicles (ASVs) with private and potentially competitive objectives. A fully distributed encircling control approach is proposed based on noncooperative games. Specifically, a fully distributed estimator with an adaptive gain is developed to estimate the target information without using global state or topology knowledge. Based on a low-frequency learning technique, a fuzzy predictor is presented to approximate the unknown vehicle kinematics induced by uncertain nonlinearities and environmental disturbances. By decoupling the cooperative target encircling into an encircling task and a spacing task, an encircling control law and a spacing control law are designed based on fully distributed Nash equilibrium seeking for achieving the private control objective of each ASV. The input-to-state stability of the closed-loop system is proven via cascade analysis. Simulation results are provided to illustrate the effectiveness of the noncooperative game-based control method for ASVs in circumnavigation missions.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 4","pages":"4769-4779"},"PeriodicalIF":8.2,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141315207","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}
Pub Date : 2024-03-01DOI: 10.1109/TIV.2024.3384837
Fei-Yue Wang
From Jan. 1 to Mar. 23, we have received 1735 submissions. The current average number of submissions per day (SPD) rate is 21.12 [1], [2]. However, such a vibrated SPD also indicates a tremendous workload for our editorial board. Therefore, I would like to expand our team to include at least 240 members. If you have suitable candidates for potential associate editors, please send a Short BioSketch and a Resume to feiyue@ieee.org and danielyenew22@gmail.com.
{"title":"Editorial Intelligent Vehicles for Sustainability Industry: Call for Nomination and Participation","authors":"Fei-Yue Wang","doi":"10.1109/TIV.2024.3384837","DOIUrl":"https://doi.org/10.1109/TIV.2024.3384837","url":null,"abstract":"From Jan. 1 to Mar. 23, we have received 1735 submissions. The current average number of submissions per day (SPD) rate is \u0000<bold>21.12</b>\u0000 [1], [2]. However, such a vibrated SPD also indicates a tremendous workload for our editorial board. Therefore, I would like to expand our team to include at least 240 members. If you have suitable candidates for potential associate editors, please send a Short BioSketch and a Resume to feiyue@ieee.org and danielyenew22@gmail.com.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 3","pages":"4233-4239"},"PeriodicalIF":8.2,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140820406","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}
Cooperative perception can significantly improve the perception performance of autonomous vehicles beyond the limited perception ability of individual vehicles by exchanging information with neighbor agents through V2X communication. However, most existing work assume ideal communication among agents, ignoring the significant and common interruption issues caused by imperfect V2X communication, where cooperation agents can not receive cooperative messages successfully and thus fail to achieve cooperative perception, leading to safety risks. To fully reap the benefits of cooperative perception in practice, we propose V2X communication INterruption-aware COoperative Perception (V2X-INCOP), a cooperative perception system robust to communication interruption for V2X communication-aided autonomous driving, which leverages historical cooperation information to recover missing information due to the interruptions and alleviate the impact of the interruption issue. To achieve comprehensive recovery, we design a communication-adaptive multi-scale spatial-temporal prediction model to extract multi-scale spatial-temporal features based on V2X communication conditions and capture the most significant information for the prediction of the missing information. To further improve recovery performance, we adopt a knowledge distillation framework to give explicit and direct supervision to the prediction model and a curriculum learning strategy to stabilize the training of the model. Experiments on three public cooperative perception datasets demonstrate that the proposed method is effective in alleviating the impacts of communication interruption on cooperative perception. V2X-INCOP outperforms state-of-the-art cooperative perception methods and has a cooperative perception gain up to 14.06%, 13.9%, and 12.07% over individual perception on average of different packet drop rates on OPV2V, V2X-Sim, and Dair-V2X datasets, respectively.
{"title":"Interruption-Aware Cooperative Perception for V2X Communication-Aided Autonomous Driving","authors":"Shunli Ren;Zixing Lei;Zi Wang;Mehrdad Dianati;Yafei Wang;Siheng Chen;Wenjun Zhang","doi":"10.1109/TIV.2024.3371974","DOIUrl":"https://doi.org/10.1109/TIV.2024.3371974","url":null,"abstract":"Cooperative perception can significantly improve the perception performance of autonomous vehicles beyond the limited perception ability of individual vehicles by exchanging information with neighbor agents through V2X communication. However, most existing work assume ideal communication among agents, ignoring the significant and common \u0000<italic>interruption issues</i>\u0000 caused by imperfect V2X communication, where cooperation agents can not receive cooperative messages successfully and thus fail to achieve cooperative perception, leading to safety risks. To fully reap the benefits of cooperative perception in practice, we propose V2X communication INterruption-aware COoperative Perception (V2X-INCOP), a cooperative perception system robust to communication interruption for V2X communication-aided autonomous driving, which leverages historical cooperation information to recover missing information due to the interruptions and alleviate the impact of the interruption issue. To achieve comprehensive recovery, we design a communication-adaptive multi-scale spatial-temporal prediction model to extract multi-scale spatial-temporal features based on V2X communication conditions and capture the most significant information for the prediction of the missing information. To further improve recovery performance, we adopt a knowledge distillation framework to give explicit and direct supervision to the prediction model and a curriculum learning strategy to stabilize the training of the model. Experiments on three public cooperative perception datasets demonstrate that the proposed method is effective in alleviating the impacts of communication interruption on cooperative perception. V2X-INCOP outperforms state-of-the-art cooperative perception methods and has a cooperative perception gain up to 14.06%, 13.9%, and 12.07% over individual perception on average of different packet drop rates on OPV2V, V2X-Sim, and Dair-V2X datasets, respectively.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 4","pages":"4698-4714"},"PeriodicalIF":8.2,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141315148","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}
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