Pub Date : 2024-03-01DOI: 10.1109/TIV.2024.3394801
{"title":"Share Your Preprint Research with the World!","authors":"","doi":"10.1109/TIV.2024.3394801","DOIUrl":"https://doi.org/10.1109/TIV.2024.3394801","url":null,"abstract":"","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 3","pages":"4556-4556"},"PeriodicalIF":8.2,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10517877","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140820322","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}
Pub Date : 2024-03-01DOI: 10.1109/TIV.2024.3384550
Xumeng Wang;Xiao Xue;Ran Yan;Xingxia Wang;Yining Di;Wei Chen;Fei-Yue Wang
Scenario simulation plays an integral role in the development, application, and management of intelligent vehicles. However, planning agents and customizing scenarios for complex systems are laborious, making it challenging to implement high-performance simulations. The striking progress made by Sora, a large-scale text-to-video model, suggests a research opportunity for high-performance simulation through dynamic visualizations. This paper reports the prospective effects of Sora on the scenario simulation of intelligent vehicles. Specifically, we review the achievements of Sora, picture the perspectives of artificiofactual experiments on intelligent vehicles based on the performance of Sora-type techniques, and discuss how far are we now.
情景模拟在智能汽车的开发、应用和管理中发挥着不可或缺的作用。然而，为复杂系统规划代理和定制情景非常费力，因此实现高性能仿真具有挑战性。大规模文本到视频模型 Sora 取得的显著进展为通过动态可视化实现高性能仿真提供了研究机会。本文报告了 Sora 对智能汽车场景仿真的前瞻性影响。具体而言，我们回顾了 Sora 所取得的成就，描绘了基于 Sora 类技术性能的智能车辆人工智能实验的前景，并讨论了目前的进展情况。
{"title":"Sora for Intelligent Vehicles: A Step From Constraint-Based Simulation to Artificiofactual Experiments Through Dynamic Visualization","authors":"Xumeng Wang;Xiao Xue;Ran Yan;Xingxia Wang;Yining Di;Wei Chen;Fei-Yue Wang","doi":"10.1109/TIV.2024.3384550","DOIUrl":"https://doi.org/10.1109/TIV.2024.3384550","url":null,"abstract":"Scenario simulation plays an integral role in the development, application, and management of intelligent vehicles. However, planning agents and customizing scenarios for complex systems are laborious, making it challenging to implement high-performance simulations. The striking progress made by Sora, a large-scale text-to-video model, suggests a research opportunity for high-performance simulation through dynamic visualizations. This paper reports the prospective effects of Sora on the scenario simulation of intelligent vehicles. Specifically, we review the achievements of Sora, picture the perspectives of artificiofactual experiments on intelligent vehicles based on the performance of Sora-type techniques, and discuss how far are we now.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 3","pages":"4249-4253"},"PeriodicalIF":8.2,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140820330","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 : 2024-02-28DOI: 10.1109/TIV.2024.3370913
Wenqin Zhong;Keqiang Li;Jia Shi;Jie Yu;Yugong Luo
Connected and Autonomous Vehicle (CAV) has attracted much attention as it provides promising solutions to improve traffic performance in many scenarios, especially unsignalized intersections. However, as for unsignalized intersections where both CAV and Human-driven Vehicle (HDV) exist, to reduce the impact of HDV uncertain behaviors, existing related research tend to simplify the scenario or HDV behavior characteristics at unsignalized intersections, or ensuring passing safety without collaborative decision-making on traffic efficiency optimizations. To address these problems, this paper presents a method of reservation-prioritization-based mixed-traffic cooperative control at unsignalized intersections. Firstly, reservation rights of CAVs are prioritized by solving minimization problems, to optimize the reservation order of CAV on the behalf of traffic efficiency. Secondly, a reservation and speed planning mechanism considering HDV behaviors is designed, which develops and re-decides CAV's reservation result based on HDV free-driving behavior, and plans speed for CAVs based on their reservation results by solving constrained nonlinear programming problems. The proposed method is evaluated under different traffic volumes and CAV penetration rates on SUMO platform. Results show that the proposed reservation-prioritization-based method gains higher intersection throughput and averaged velocity under all scenarios, including a maximum throughput improvement rate of 17.77% and a maximum averaged velocity improvement rate of 66.37% compared with the comparative methods.
{"title":"Reservation-Prioritization-Based Mixed-Traffic Cooperative Control at Unsignalized Intersections","authors":"Wenqin Zhong;Keqiang Li;Jia Shi;Jie Yu;Yugong Luo","doi":"10.1109/TIV.2024.3370913","DOIUrl":"https://doi.org/10.1109/TIV.2024.3370913","url":null,"abstract":"Connected and Autonomous Vehicle (CAV) has attracted much attention as it provides promising solutions to improve traffic performance in many scenarios, especially unsignalized intersections. However, as for unsignalized intersections where both CAV and Human-driven Vehicle (HDV) exist, to reduce the impact of HDV uncertain behaviors, existing related research tend to simplify the scenario or HDV behavior characteristics at unsignalized intersections, or ensuring passing safety without collaborative decision-making on traffic efficiency optimizations. To address these problems, this paper presents a method of reservation-prioritization-based mixed-traffic cooperative control at unsignalized intersections. Firstly, reservation rights of CAVs are prioritized by solving minimization problems, to optimize the reservation order of CAV on the behalf of traffic efficiency. Secondly, a reservation and speed planning mechanism considering HDV behaviors is designed, which develops and re-decides CAV's reservation result based on HDV free-driving behavior, and plans speed for CAVs based on their reservation results by solving constrained nonlinear programming problems. The proposed method is evaluated under different traffic volumes and CAV penetration rates on SUMO platform. Results show that the proposed reservation-prioritization-based method gains higher intersection throughput and averaged velocity under all scenarios, including a maximum throughput improvement rate of 17.77% and a maximum averaged velocity improvement rate of 66.37% compared with the comparative methods.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 5","pages":"4917-4930"},"PeriodicalIF":14.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141964747","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 : 2024-02-26DOI: 10.1109/TIV.2024.3367815
Ehsan Hashemi;Amir Khajepour
A novel integrated stabilization and path tracking control framework, which includes the combined-slip effect, wheel dynamics, and tire force capacities, is developed for autonomous ground vehicles. The loss of cornering forces caused by increased longitudinal slip are considered in the prediction model of the developed receding horizon controls. Robustness to uncertainties in the road surface friction is addressed by an adaptive constraint scheme on the side handling-limit boundaries in order to provide a reliable stable performance. The integrated framework with constraint adaptation resolves possible conflicts of the multi-actuated system for lateral stabilization, while trajectory tracking on various surface conditions. The performance of the proposed approach, in terms of accuracy and computational efficiency, is evaluated by using hardware-in-the-loop real-time experiments and a high-fidelity CarSim model in various pure- and combined-slip maneuvers, under different road friction conditions. The real-time experiments confirm effectiveness and reliable performance of the proposed approach over existing algorithms, in dealing with reduced tire capacities in harsh obstacle avoidance and cornering scenarios, while path following, as a consequence of constraint adaptation and simultaneous vehicle-wheel stabilization.
{"title":"Integrated Path-Tracking and Combined-Slip Force Controls of Autonomous Ground Vehicles With Safe Constraints Adaptation","authors":"Ehsan Hashemi;Amir Khajepour","doi":"10.1109/TIV.2024.3367815","DOIUrl":"https://doi.org/10.1109/TIV.2024.3367815","url":null,"abstract":"A novel integrated stabilization and path tracking control framework, which includes the combined-slip effect, wheel dynamics, and tire force capacities, is developed for autonomous ground vehicles. The loss of cornering forces caused by increased longitudinal slip are considered in the prediction model of the developed receding horizon controls. Robustness to uncertainties in the road surface friction is addressed by an adaptive constraint scheme on the side handling-limit boundaries in order to provide a reliable stable performance. The integrated framework with constraint adaptation resolves possible conflicts of the multi-actuated system for lateral stabilization, while trajectory tracking on various surface conditions. The performance of the proposed approach, in terms of accuracy and computational efficiency, is evaluated by using hardware-in-the-loop real-time experiments and a high-fidelity CarSim model in various pure- and combined-slip maneuvers, under different road friction conditions. The real-time experiments confirm effectiveness and reliable performance of the proposed approach over existing algorithms, in dealing with reduced tire capacities in harsh obstacle avoidance and cornering scenarios, while path following, as a consequence of constraint adaptation and simultaneous vehicle-wheel stabilization.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 3","pages":"4265-4274"},"PeriodicalIF":8.2,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140820214","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 : 2024-02-23DOI: 10.1109/TIV.2024.3369324
Wen Hu;Cong Wang;Zejian Deng;Yanding Yang;Yang Wu;Kai Cao;Bangji Zhang;Dongpu Cao
Precisely assessing driving threat of road segments could significantly enhance the driving efficiency of intelligent connected vehicles (ICV) within mixed traffic scenarios. Existing methods primarily concentrate on collision probabilities, resulting in an insufficient appraisal of asymmetrical hazard levels attributed to the various interactions. Meanwhile, the uncertainty and communication delay have great influence on ICV, and it is an issue that must be addressed when designing decision-making and planning model. Thus, this study proposes and formulates a new driving aggressiveness model after analyzing asymmetric interactions behaviors among vehicles with different types. Subsequently, aims to verify the capability of generating asymmetric interaction, the driving aggressiveness model is applied on lane-change decision-making and planning. Concretely, the aggressiveness-sensitive lanes-selection model is designed based on game theory, and the uncertainty-aware trajectory planning is developed by utilizing stochastic model predictive control (MPC) and the asymmetric driving aggressiveness. Finally, two naturalistic driving scenarios are utilized to verify the performance of the decision-making and planning model. The outcomes of simulations illustrate that the driving aggressiveness model introduces a novel perspective to assess the asymmetric driving threat. Meanwhile, the uncertainty-aware decision making and planning model can reduce the influence on interactive vehicles, and it has superior adaptability for dynamic and connected traffic environments.
{"title":"Uncertainty-Aware Decision Making and Planning for ICV Based on Asymmetric Driving Aggressiveness","authors":"Wen Hu;Cong Wang;Zejian Deng;Yanding Yang;Yang Wu;Kai Cao;Bangji Zhang;Dongpu Cao","doi":"10.1109/TIV.2024.3369324","DOIUrl":"https://doi.org/10.1109/TIV.2024.3369324","url":null,"abstract":"Precisely assessing driving threat of road segments could significantly enhance the driving efficiency of intelligent connected vehicles (ICV) within mixed traffic scenarios. Existing methods primarily concentrate on collision probabilities, resulting in an insufficient appraisal of asymmetrical hazard levels attributed to the various interactions. Meanwhile, the uncertainty and communication delay have great influence on ICV, and it is an issue that must be addressed when designing decision-making and planning model. Thus, this study proposes and formulates a new driving aggressiveness model after analyzing asymmetric interactions behaviors among vehicles with different types. Subsequently, aims to verify the capability of generating asymmetric interaction, the driving aggressiveness model is applied on lane-change decision-making and planning. Concretely, the aggressiveness-sensitive lanes-selection model is designed based on game theory, and the uncertainty-aware trajectory planning is developed by utilizing stochastic model predictive control (MPC) and the asymmetric driving aggressiveness. Finally, two naturalistic driving scenarios are utilized to verify the performance of the decision-making and planning model. The outcomes of simulations illustrate that the driving aggressiveness model introduces a novel perspective to assess the asymmetric driving threat. Meanwhile, the uncertainty-aware decision making and planning model can reduce the influence on interactive vehicles, and it has superior adaptability for dynamic and connected traffic environments.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 3","pages":"4432-4444"},"PeriodicalIF":8.2,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140820326","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}
With the development of intelligent transportation technologies, the Internet of Vehicles (IoV) faces challenges such as data silos, security and privacy concerns, data quality issues, and collaboration barriers. To address the various challenges, this paper proposes an innovative integration scheme called the IoV Data Management System (IDMS). This system is built upon blockchain and parallel intelligence technologies, aiming to solve the challenges presented in the IoV domain. The proposed system uses the decentralized, immutable and traceable characteristics of blockchain, combined with the incentive mechanism and collaboration model of decentralized autonomous organization (DAO), to build a secure and trusted data sharing platform to solve data problems in IoV. This research combines parallel intelligence, blockchain and DAO technologies to provide an innovative framework for IoV information management. The proposed framework enables parallel management of connected vehicle systems, thereby improving safety, reliability and efficiency. Furthermore, it would promote the development and application of vehicle networking technology, and provide more intelligent, convenient and safe services for people's travel experience. Finally, a parking data sharing case study validates the effectiveness of the designed system and demonstrates its potential to solve IoV data management challenges.
随着智能交通技术的发展，车联网（IoV）面临着数据孤岛、安全和隐私问题、数据质量问题以及协作障碍等挑战。为应对各种挑战，本文提出了一种创新的集成方案，即 IoV 数据管理系统（IDMS）。该系统基于区块链和并行智能技术，旨在解决物联网领域所面临的挑战。该系统利用区块链去中心化、不可篡改、可追溯的特点，结合去中心化自治组织（DAO）的激励机制和协作模式，构建安全可信的数据共享平台，解决物联网领域的数据问题。这项研究结合了并行智能、区块链和 DAO 技术，为物联网信息管理提供了一个创新框架。拟议框架可实现对联网车辆系统的并行管理，从而提高安全性、可靠性和效率。此外，它还将促进车联网技术的发展和应用，为人们的出行体验提供更加智能、便捷和安全的服务。最后，停车数据共享案例研究验证了所设计系统的有效性，并展示了其解决物联网数据管理难题的潜力。
{"title":"Parallel Management of IoV Information Enabled by Blockchain and Decentralized Autonomous Organizations","authors":"Shuangshuang Han;Yongqiang Bai;Tianrui Zhang;Yueyun Chen;Chintha Tellambura","doi":"10.1109/TIV.2024.3368510","DOIUrl":"https://doi.org/10.1109/TIV.2024.3368510","url":null,"abstract":"With the development of intelligent transportation technologies, the Internet of Vehicles (IoV) faces challenges such as data silos, security and privacy concerns, data quality issues, and collaboration barriers. To address the various challenges, this paper proposes an innovative integration scheme called the IoV Data Management System (IDMS). This system is built upon blockchain and parallel intelligence technologies, aiming to solve the challenges presented in the IoV domain. The proposed system uses the decentralized, immutable and traceable characteristics of blockchain, combined with the incentive mechanism and collaboration model of decentralized autonomous organization (DAO), to build a secure and trusted data sharing platform to solve data problems in IoV. This research combines parallel intelligence, blockchain and DAO technologies to provide an innovative framework for IoV information management. The proposed framework enables parallel management of connected vehicle systems, thereby improving safety, reliability and efficiency. Furthermore, it would promote the development and application of vehicle networking technology, and provide more intelligent, convenient and safe services for people's travel experience. Finally, a parking data sharing case study validates the effectiveness of the designed system and demonstrates its potential to solve IoV data management challenges.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 4","pages":"4759-4768"},"PeriodicalIF":8.2,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141315149","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 : 2024-02-21DOI: 10.1109/TIV.2024.3368109
Kui Wang;Zongdian Li;Kazuma Nonomura;Tao Yu;Kei Sakaguchi;Omar Hashash;Walid Saad
Digital twins (DTs) have driven major advancements across various industrial domains over the past two decades. With the rapid advancements in autonomous driving and vehicle-to-everything (V2X) technologies, integrating DTs into vehicular platforms is anticipated to further revolutionize smart mobility systems. In this paper, a new smart mobility DT (SMDT) platform is proposed for the control of connected and automated vehicles (CAVs) over next-generation wireless networks. In particular, the proposed platform enables cloud services to leverage the abilities of DTs to promote the autonomous driving experience. To enhance traffic efficiency and road safety measures, a novel navigation system that exploits available DT information is designed. The SMDT platform and navigation system are implemented with state-of-the-art products, e.g., CAVs and roadside units (RSUs), and emerging technologies, e.g., cloud and cellular V2X (C-V2X). In addition, proof-of-concept (PoC) experiments are conducted to validate system performance. The performance of SMDT is evaluated from two standpoints: (i) the rewards of the proposed navigation system on traffic efficiency and safety and, (ii) the latency and reliability of the SMDT platform. Our experimental results using SUMO-based large-scale traffic simulations show that the proposed SMDT can reduce the average travel time and the blocking probability due to unexpected traffic incidents. Furthermore, the results record a peak overall latency for DT modeling and route planning services to be 155.15 ms and 810.59 ms, respectively, which validates that our proposed design aligns with the 3GPP requirements for emerging V2X use cases and fulfills the targets of the proposed design.
{"title":"Smart Mobility Digital Twin Based Automated Vehicle Navigation System: A Proof of Concept","authors":"Kui Wang;Zongdian Li;Kazuma Nonomura;Tao Yu;Kei Sakaguchi;Omar Hashash;Walid Saad","doi":"10.1109/TIV.2024.3368109","DOIUrl":"10.1109/TIV.2024.3368109","url":null,"abstract":"Digital twins (DTs) have driven major advancements across various industrial domains over the past two decades. With the rapid advancements in autonomous driving and vehicle-to-everything (V2X) technologies, integrating DTs into vehicular platforms is anticipated to further revolutionize smart mobility systems. In this paper, a new smart mobility DT (SMDT) platform is proposed for the control of connected and automated vehicles (CAVs) over next-generation wireless networks. In particular, the proposed platform enables cloud services to leverage the abilities of DTs to promote the autonomous driving experience. To enhance traffic efficiency and road safety measures, a novel navigation system that exploits available DT information is designed. The SMDT platform and navigation system are implemented with state-of-the-art products, e.g., CAVs and roadside units (RSUs), and emerging technologies, e.g., cloud and cellular V2X (C-V2X). In addition, proof-of-concept (PoC) experiments are conducted to validate system performance. The performance of SMDT is evaluated from two standpoints: (i) the rewards of the proposed navigation system on traffic efficiency and safety and, (ii) the latency and reliability of the SMDT platform. Our experimental results using SUMO-based large-scale traffic simulations show that the proposed SMDT can reduce the average travel time and the blocking probability due to unexpected traffic incidents. Furthermore, the results record a peak overall latency for DT modeling and route planning services to be 155.15 ms and 810.59 ms, respectively, which validates that our proposed design aligns with the 3GPP requirements for emerging V2X use cases and fulfills the targets of the proposed design.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 3","pages":"4348-4361"},"PeriodicalIF":8.2,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10443037","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140447458","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}
In-wheel-motor-driven electric vehicles (IWM-EVs) provide more potential to enhance vehicle stability performance. However, traditional stability control relies on the current status fed back by sensors for stability judgment and control, only taking effect after the vehicle has already become unstable. In response to this issue, this paper proposes a pre-stability control strategy based on a hybrid dynamic state prediction method to predict dangerous driving conditions and intervene in vehicle stability control in advance. First, a driver-vehicle model is established to characterize the driver's driving intention and obtain the vehicle's ideal motion responses. Then, the methodology for implementing vehicle pre-stability control is introduced, which mainly includes sideslip angle estimation utilizing the extended Kalman filter, a hybrid dynamic state prediction approach based on vehicle model and data trends, and a vehicle pre-stability judgment method. Subsequently, a vehicle hierarchical controller is designed to achieve pre-stability control. The upper-level controller focuses on calculating the required additional yaw moment, and the lower-level controller aims to optimize torque distribution among the four wheels. Finally, the proposed pre-stability control strategy is validated by the hardware-in-the-loop test bench. The results show that the proposed control strategy can intervene in dangerous driving conditions in advance, and its mean errors of the yaw rate and sideslip angle are reduced by over 17.1% and 23.5%, respectively, compared with the traditional method, which significantly enhances vehicle stability and driving safety.
{"title":"Pre-Stability Control for In-Wheel-Motor-Driven Electric Vehicles With Dynamic State Prediction","authors":"Mengjie Tian;Qixiang Zhang;Duanyang Tian;Liqiang Jin;Jianhua Li;Feng Xiao","doi":"10.1109/TIV.2024.3368207","DOIUrl":"https://doi.org/10.1109/TIV.2024.3368207","url":null,"abstract":"In-wheel-motor-driven electric vehicles (IWM-EVs) provide more potential to enhance vehicle stability performance. However, traditional stability control relies on the current status fed back by sensors for stability judgment and control, only taking effect after the vehicle has already become unstable. In response to this issue, this paper proposes a pre-stability control strategy based on a hybrid dynamic state prediction method to predict dangerous driving conditions and intervene in vehicle stability control in advance. First, a driver-vehicle model is established to characterize the driver's driving intention and obtain the vehicle's ideal motion responses. Then, the methodology for implementing vehicle pre-stability control is introduced, which mainly includes sideslip angle estimation utilizing the extended Kalman filter, a hybrid dynamic state prediction approach based on vehicle model and data trends, and a vehicle pre-stability judgment method. Subsequently, a vehicle hierarchical controller is designed to achieve pre-stability control. The upper-level controller focuses on calculating the required additional yaw moment, and the lower-level controller aims to optimize torque distribution among the four wheels. Finally, the proposed pre-stability control strategy is validated by the hardware-in-the-loop test bench. The results show that the proposed control strategy can intervene in dangerous driving conditions in advance, and its mean errors of the yaw rate and sideslip angle are reduced by over 17.1% and 23.5%, respectively, compared with the traditional method, which significantly enhances vehicle stability and driving safety.","PeriodicalId":36532,"journal":{"name":"IEEE Transactions on Intelligent Vehicles","volume":"9 3","pages":"4541-4554"},"PeriodicalIF":8.2,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140820276","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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