Intelligent Connected Vehicle Research Articles

Tourism Recommendation Algorithm Based on the Mobile Intelligent Connected Vehicle Service Platform

As to the problems in current tourism recommendation, this paper proposes a tourism recommendation algorithm based on the mobile ICV service platform. Firstly, the ICV service system for the Point of Interest (POI) searching and route recommendation is designed. Secondly, the recommendation service model is set up from two aspects, namely the tourism POI clustering algorithm and the tourism POI searching and route recommendation algorithm. In the aspect of symmetrical-based matching features, the clustered POIs are matched with the tourists’ interests, and the POIs in the neighborhood of the ICV dynamic locations are searched. Then, a POI recommendation algorithm based on the tourists’ interests is constructed, and the POIs that best match the symmetrical interests of the tourists within the dynamic buffer zones of ICV are confirmed. Based on the recommended POIs, the ICV guidance route algorithm is constructed. The experiment verifies the advantages of the proposed algorithm on the aspect of the POI matching tourists’ interests, algorithm stability, traveling time cost, traveling distance cost and computational complexity. As to the iterative sum and the iterative sum average of the POI matching function values, the proposed algorithm has a performance improvement of at least 20.2% and a stability improvement of at least 20.5% compared to the randomly selected POIs in matching tourists’ interests. As to the cost of the guidance routes, the proposed algorithm reduces the average cost by 19.6% compared to the other suboptimal routes. Compared with the control group algorithms, the proposed algorithm is superior in terms of route cost, with an average cost reduction of 13.8% for the output routes compared to the control group. Also, the proposed algorithm is superior in terms of route cost compared to the control group recommendation algorithms, with an average cost reduction of 11.2%.

Distributed finite-time attack detection and estimation of intelligent connected vehicle platoon

ABSTRACT This paper presents a distributed finite-time observer design approach for the problem of intelligent connected vehicles (ICVs) containing both attack signals and unknown disturbances. Firstly, an analysis is conducted on the two types of network attacks that ICVs may encounter, followed by the construction of a dynamic vehicle model and its transformation into a state space equation. Secondly, an intermediate variable is introduced to design a distributed attack estimation observer, which both ensures accurate estimation of the vehicle safety state and attack signal within a limited time and overcomes the constraints of traditional observers. Additionally, sufficient conditions for the finite-time boundedness of the observer error system with H ∞ performance are provided in the form of linear matrix inequalities. Furthermore, an adaptive threshold attack detection method is designed. Finally, the simulation results validate the efficiency of the design methods.

Modeling risk potential fields for mandatory lane changes in intelligent connected vehicle environment

In the environment of Intelligent Connected Vehicles (ICVs), the ICV system is capable of gathering, processing, and transmitting the majority of traffic information to each vehicle, thereby empowering drivers to make more informed and secure decisions. Nevertheless, ICVs are still incapable of completely avoiding traffic conflicts and accidents. Consequently, developing a risk assessment model for ICVs is crucial for their deployment. To investigate the dynamic patterns of lane-changing vehicle risks within ICV environments, this study developed a quantitative relationship model based on the Artificial Potential Field (APF) theory. The model correlates the Total Potential Field (TPF) of mandatory lane-changing (MLC) vehicle with the longitudinal/transverse gravitational potential field (LGPF/TGPF) and the longitudinal/transverse repulsion potential field (LRPF/TRPF). Subsequently, three sets of Human Machine Interface (HMI) systems were developed to delineate distinct ICV environments: baseline, warning group, and guidance group. The baseline furnishes fundamental data, the warning group supplements preceding vehicle icons and real-time headway information, whereas the guidance group additionally augments speed and voice guidance functionalities. Following this, a simulated driving experiment involving 43 participants was carried out to simulate MLC scenarios in highway merging sections. Using enumeration and gradient descent methods, the quantitative relationship between different types of risk fields was determined. The findings indicated that the ICV environment significantly impacts the mean values of LGPF, TGPF, LRPF (Leading Vehicle, LV), and TRPF, while insignificantly affecting the mean value of LRPF (Following Vehicle, FV). Nevertheless, a notable discrepancy in the mean value of LRPF (FV) was observed between the warning and guidance groups. Comparative results with lane positions demonstrate that the proposed TPF accurately depicts vehicle lane-changing behaviors. Comparative results with classical risk indicators indicate that TPF more accurately depicts the actual driving risks and variation trends faced by vehicles in different MLC processes. The research findings will be pivotal in improving real-time control and guidance strategies for vehicles in highway merging sections under ICV environments.

Strategic analysis of intelligent connected vehicle industry competitiveness: a comprehensive evaluation system integrating rough set theory and projection pursuit

As a carrier of multi-industrial technology integration and the key to industrial competition, the intelligent connected vehicle (ICV) has been taken seriously around the world. However, as a fast-growing emerging industry, its development process varies greatly from place to place. Hence, the merits and demerits are analyzed for the development of the ICV industry in different cities scientifically and to clarify the development of different links in each city, this paper suggests an extensive assessment framework integrating rough set theory and projection pursuit-based computation to systematically assess and thoroughly evaluate the level of competitiveness of the ICV industry. First, through big data text analysis technology, we constructed a "5 + 24" two-tier evaluation index system composed of 24 level-II evaluation indexes as well as five level-I evaluation indexes and selected 19 typical cities as input data for the comprehensive evaluation system. Further, the Adaptive Random Forest based Crossover Tactical Unit (ARF-CTU) algorithm is proposed for evaluating the performance of the industrial vehicle industry. However, the ARF algorithm is employed to estimate the lowering of overfitting issues and handling of high dimensional data. Moreover, the continuously varying conditions are analyzed by CTU. Then, we constructed a comprehensive evaluation system in the rough set theory and projection pursuit: (I) Quoting the rough set non-decision-making algorithm for attribute reduction, that is, under the premise of unchanged classification ability, derive a new evaluation system, and calculate the index weight and score based on the new system. (II) Based on the projection pursuit technology, the index score is mapped by a genetic algorithm to a linear structure, and a one-dimensional projection vector is an output.

Recent Progress on Digital Twins in Intelligent Connected Vehicles: A Review

As an important enabling technology in the era of Industry 4.0, the intelligent connected vehicle (ICV) facilitates robust data interaction with the outside through sensors and communication technologies, ultimately making scientific decisions based on environmental perception information. However, due to constraints such as limited communication bandwidth and computing resources, the influx of data simultaneously impedes the sustainable optimisation of the vehicle decision making process at the same time. As a novel technology that effectively connects physical and virtual space, the special ability of the digital twin (DT) is to identify characteristics within a certain lifecycle, thereby garnering widespread attention across various industries. The purpose of this paper is to review the contribution of digital twins in the application field of intelligent vehicles and explore its potential for development. First, the key technologies of ICV provide a basis for the embedding of digital twins. Then, by analysing the development process and technical composition of digital twins, readers can better understand the concept of digital twins. Finally, the application of DTs in ICV is reviewed from the perspective of vehicles, traffic facilities, and occupants. Future challenges and opportunities in this direction are described at the same time.

Standard Essential Patents Data Study of the Impact of Artificial Intelligence on Intelligent Connected Vehicle Technological Advancement

Standard Essential Patents Data Study of the Impact of Artificial Intelligence on Intelligent Connected Vehicle Technological Advancement

Multisource‐multitarget cooperative positioning using probability hypothesis density filter in internet of vehicles

AbstractAccurate positioning of intelligent connected vehicle (ICV) is a key element for the development of cooperative intelligent transportation system. In vehicular networks, lots of state‐related measurements, especially the mutual measurements between ICVs, are shared. It is an advisable strategy to fuse these measurements for a more robust positioning. In this context, an innovative framework, referred to as multisource‐multitarget cooperative positioning (MMCP) is presented. In MMCP, ICVs are local information source, that upload both the states of ICVs estimated by on‐board sensors and the relative vectors between surrounding objects and vehicles to a fusion centre. In the fusion centre, ICVs are selected as the global targets, and the relative vectors are converted into global measurements. Then, the MMCP is modelled into a multi‐target tracking problem with specific targets. This paper proposes a low complexity Gaussian mixture probability hypothesis density (GM‐PHD‐LC) filter to match and fuse the global measurements to further improve the estimation of ICVs. The evaluation results show that our GM‐PHD‐LC can provide 10 Hz positioning services in urban area, and significantly improve the positioning accuracy compared to the standalone global navigation satellite system.

Complying with ISO 26262 and ISO/SAE 21434: A Safety and Security Co-Analysis Method for Intelligent Connected Vehicle.

A cyber-physical system (CPS) integrates communication and automation technologies into the operational processes of physical systems. Nowadays, as a complex CPS, an intelligent connected vehicle (ICV) may be exposed to accidental functional failures and malicious attacks. Therefore, ensuring the ICV's safety and security is crucial. Traditional safety/security analysis methods, such as failure mode and effect analysis and attack tree analysis, cannot provide a comprehensive analysis for the interactions between the system components of the ICV. In this work, we merge system-theoretic process analysis (STPA) with the concept phase of ISO 26262 and ISO/SAE 21434. We focus on the interactions between components while analyzing the safety and security of ICVs to reduce redundant efforts and inconsistencies in determining safety and security requirements. To conquer STPA's abstraction in describing causal scenarios, we improved the physical component diagram of STPA-SafeSec by adding interface elements. In addition, we proposed the loss scenario tree to describe specific scenarios that lead to unsafe/unsecure control actions. After hazard/threat analysis, a unified risk assessment process is proposed to ensure consistency in assessment criteria and to streamline the process. A case study is implemented on the autonomous emergency braking system to demonstrate the validation of the proposed method.

Kansei engineering for the intelligent connected vehicle functions: An online and offline data mining approach

The big data era enables automakers to mine users’ affective (Kansei) requirements for the car design. However, existing literature mostly applies text mining with users’ online comments, possibly leading to biased results since users without online comments were not considered. To fill in this gap, this paper proposes to jointly analyse users’ online commenting and offline usage big data, and develops a novel framework to efficiently fuse these two datasets for the Kansei engineering of the intelligent connected vehicle (ICV) functions. A behaviour-enhanced large language model is proposed to process users’ online comments; then, users’ Kansei requirements are further jointly analysed with their offline in-cabin behaviour data, by the proposed NLP-MDCEV (natural language process — multiple discrete-continuous extreme value) model, to understand user’s complex discrete and continuous choice decisions in the smart cockpit. In addition, the proposed framework aims to solve the problem of design tasks prioritization, where not all the Kansei requirements can be met if design resources are limited. The proposed framework is applied in the studied new energy vehicle company, with more than nine-months’ online comments and six-months’ offline usage data, where results suggest its merits of economic, efficient, and effective.

Navigation Route Planning for Tourism Intelligent Connected Vehicle Based on the Symmetrical Spatial Clustering and Improved Fruit Fly Optimization Algorithm

The intelligent connected vehicle (ICV) decision-making system needs to match tourist interests and search for the route with the lowest travel cost when recommending POIs (Points of Interest) and navigation tour routes. In response to this research objective, we construct a navigation route-planning model for tourism intelligent connected vehicles based on symmetrical spatial clustering and improved fruit fly optimization algorithm. Firstly, we construct the POI feature attribute clustering algorithm based on the spatial decision forest to achieve the optimal POI recommendation. Secondly, we construct the POI spatial attribute clustering algorithm based on the SA-AGNES (Spatial Accessibility-Agglomerative Nesting) to achieve the spatial modeling between POIs and ICV clusters. On the basis of POI feature attribute and spatial attribute, we construct the POI recommendation algorithm for the ICV navigation routes based on the attribute weights. On the basis of the recommended POIs, we construct the tourism ICV navigation route-planning model based on the improved fruit fly optimization algorithm. Experiments prove that the proposed algorithm can accurately output POIs that match tourists’ interests and needs, and find out the ICV navigation route with the lowest travel cost. Compared with the commonly used map route-planning methods and traditional route-searching algorithms, the proposed algorithm can reduce the travel costs by 15.22% at most, which can also effectively reduce the energy consumption of the ICV system, and improve the efficiency of sight-seeing and traveling for tourists.

Data Validity Analysis Based on Reinforcement Learning for Mixed Types of Anomalies Coexistence in Intelligent Connected Vehicle (ICV)

Compared with traditional anomaly analysis, intelligent connected vehicle (ICV) data validity analysis is faced with a variety of data anomalies, including sensor anomalies, driving behavior anomalies, malicious tampering, and so on, which eventually leads to anomalies in the data. How to integrate the vehicle moving characteristics, driving style, and traffic flow conditions to provide an effective data detection method has become a new problem in the field of intelligent networked vehicles. Based on ICV data, a particle swarm optimization data validity detection algorithm (TE-PSO-SVM) was proposed by combining driving style and traffic flow theory to realize the effective detection of driving data. In addition, aiming at the problem of mixed types of anomalies in complex scenes, a model pool is constructed, and a model selection algorithm based on reinforcement learning (RLBMS) is proposed. Experiments on the real data set HighD show that RLBMS has a better detection effect in complex scenes of mixed types of anomalies.

Talent Flow in China's Intelligent Connected Vehicle Industry: Evidence From Online Resume Mining

Talent Flow in China's Intelligent Connected Vehicle Industry: Evidence From Online Resume Mining

Transmission-Efficient RIS-Carrying UAV’s Auxiliary Communication Systems for Intelligent Connected Vehicle Platoons at the Unsignalized Intersection in Smart Cities

Numerous interaction demands for smart cities have arisen due to the explosive growth of the Internet of Things and mobile communication. Due to its passive low energy consumption and affordable deployment cost, reconfigurable intelligent surface (RIS) is a potential key technology to develop a novel paradigm of wireless communication at unsignalized intersections. This paper studies the signal interaction between multiple vehicle platoons in different paths before entering an unsignalized intersection supported by UAV, which is usually difficult to solve without the assistance of communication. Under the limitation of calculation and transmission resources, the objective is to optimize the system’s transmission efficiency. The joint resource scheduling model of communication time and transmission power includes the coupling effects of vehicle dynamics, onboard computing, signal transmission and reflection, and energy consumption. A method based on the quasi-Newton and sequential quadratic programming (SQP) algorithm is designed to carry out optimization iteration. Finally, the simulation results show that this method is generally efficient and outperforms the benchmark methods.

Comparative analysis of intelligent connected vehicle industry in China, United States and European Union from technology lifecycle perspective

PurposeThe intelligent connected vehicle (ICV) is an important trend in automobile development, but little research has been conducted on the technological differences in the ICV industry across countries. In this regard, the authors select China, the United States (US) and the European Union (EU) as countries with developed ICV industries to reveal these differences based on the perspective of subdivision technology.Design/methodology/approach The authors use logistic regression to fit lifecycles at technology level and country level based on ICV-related patents from China, the US and the EU, then use the Revealed Technological Advantage (RTA) index, Fast-Growing Specialization Index (FGSI) and International Patent Classification (IPC) numbers to conduct comparison of national technology advantages, finally use the social network analysis to investigate the evolution of characteristics and intermediate nodes of each technology innovation network.FindingsTechnology lifecycles vary according to the subdivision technology and country. The global development of the ICV industry has reached the mature stage, and 2030 may be a watershed moment, ushering in a wave of new technology iterations. In various subdivision technologies, China and the US have more leading RTAs, and China and the EU have more leading FGSIs. Innovation networks in different countries expand with technology lifecycles, with that in China being the fastest. China's Universities, the US's enterprises and the EU's research institutes are active in cooperative innovation as intermediaries.Originality/valueThis is the first study to compare the development of the ICV industry in major countries from the perspective of subdivision technology and reveal characteristics of innovation networks in each.

Knowledge Mapping with CiteSpace, VOSviewer, and SciMAT on Intelligent Connected Vehicles: Road Safety Issue

The rapid development of the Intelligent connected vehicle (ICV) industry has stimulated technological innovation in energy and communication while also highlighting the need for effective policies and road safety measures. Understanding and addressing road safety issues in the context of ICVs can contribute to ICV development and safe driving. This paper employs a knowledge mapping approach to scientifically and intuitively demonstrate research on the road safety issues of ICV over the last decade. By utilizing bibliometric tools such as CiteSpace, VOSviewer, and SciMAT, a total of 3661 original articles from the Web of Science are examined to explore three aspects. Firstly, the study investigates the collaborative relationships among authors and institutions within the industry. Secondly, it summarizes major research topics by analyzing and clustering keywords. Lastly, the paper identifies research hotspots and predicts future research directions. The findings reveal a dynamic field characterized by close collaboration among diverse institutions, with China and the United States emerging as the most active countries and mathematics and computer science journals becoming mainstream. According to three bibliometric tools, the research topics primarily revolve around three areas: Vehicular ad hoc Networks (VANET), intelligent transportation systems (ITS), and network security. Machine learning and V2X communication are predicted to be essential research topics in the next stage. Research on traffic accidents still has potential as the number of ICVs increases.

Time-Dependent Vehicle Speed Variation Based Global Path Planning for Intelligent Connected Vehicles

Article Time-Dependent Vehicle Speed Variation Based Global Path Planning for Intelligent Connected Vehicles Sihao Chen 1,2, Zhenfeng Wang 1,2, Zhengbai Liu 3, Xianyi Yang 1,2, and Heng Wang 1,2,* 1 College of Mechanical and Electrical Engineering, Henan Agricultural University, Zhengzhou 450002, China 2 Henan Provincial Cold Chain Information and Equipment Laboratory for Logistics of Agricultural Products, Zhengzhou 450002, China 3 College of Innovation and Entrepreneurship, Southern University of Science and Technology, Shenzhen 518055, China * Correspondence: dawn.wangh@henau.edu.cn Received: 8 May 2023 Accepted: 29 May 2023 Published: 21 June 2023 Abstract: When an intelligent connected vehicle (ICV) autonomously completes an intelligent driving assignment, the decision planning layer needs to plan an optimal path from the starting location to the target location for the vehicle, which is referred to as global path planning (GPP) for the ICV. For the GPP of ICVs undertaking long-distance and multi-location driving assignments, a fixed open travelling salesman problem (TSP) was constructed in conjunction with travel time analysis. To better address this issue, a genetic annealing algorithm (GAA) was proposed, and corresponding simulations were conducted using genetic algorithm, ant colony algorithm, and GAA respectively. Based on the optimization processes and results, the GAA outperformed the traditional genetic algorithm and ant colony algorithm in tackling this issue. Therefore, the method proposed in this paper can be applied to the global path planning platform for intelligent networked vehicles.

Blockchain‐enhanced trust management for mobile edge computing‐enabled intelligent vehicular collaboration in the 6G era

AbstractThe rapid development and increasing popularity of mobile devices have facilitated the development of computation‐intensive applications and services, but their constrained compute, storage and battery capabilities have also created the requirements for optimized resource provision and service orchestration. Emerging mobile edge computing (MEC) and next‐generation networks (6G) can meet these requirements of mobile devices by migrating complex computing tasks and large volumes of data to edge servers with acceptable latency, thus effectively enhancing the quality of experience (QoE) of mobile users. Intelligent Vehicular Collaboration, as an intelligent mobile application or service in intelligent connected vehicle (ICV) applications, enables precise and effective driving decisions based on a large amount of real‐time traffic information statistics and intelligent analysis of vehicle status to improve the user driving experience and the efficiency of intelligent transportation systems(ITS). However, the openness and dynamics of wireless communications and the high frequency of information interaction of MEC‐enabled intelligent vehicular collaboration services in the 6G era make them highly vulnerable to cyber attacks. How to achieve dynamically trustworthy, secure, and intelligent vehicular collaboration services in the presence of malicious node access and cyber attacks in the 6G MEC‐enabled vehicular network is a significant challenge. In this paper, we propose a novel blockchain‐enabled trust management mechanism to address this challenge. First, we design a spatiotemporal‐correlated message credibility assessment method and a dynamic mutual trust evaluation method to obtain accurate ratings for messages and mobile nodes respectively. Based on the above methods, we propose an improved blockchain‐based intelligent trust management mechanism that enables transparent, tamper‐evident, and trustworthy secure intelligent vehicular collaboration services while maintaining stable and efficient system performance. Simulation results show that our suggested solutions can efficiently evaluate the trust level of ICVs and achieve efficient and secure intelligent vehicular collaboration with acceptable communication and computation overheads.

Evolutionary game analysis on incremental component suppliers and auto manufacturers' intelligent connected vehicle production decisions

AbstractThis paper considers a two‐population evolutionary game between incremental component suppliers and new energy vehicle (NEV) manufacturers to explore their intelligent connected vehicle (ICV) production decisions in the long term. The government subsidy policy is taken into consideration in the extended model. Through analysis, we derive all the possible evolutionary results and obtain while the static subsidy affects the thresholds compared with non‐government subsidy situation, it has no effect on the overall evolutionary direction. However, the dynamic subsidy can remove the periodic behavior of the ICV supply chain participants. This paper provides some suggestions to enhance the ICV industry development.

Spatial-Temporal Risk Field for Intelligent Connected Vehicle in Dynamic Traffic and Application in Trajectory Planning

The intelligent connected vehicle (ICV) is an organic combination of connectivity and intellectuality, making it possible to exchange information and drive autonomously. How to assess and describe the traffic risk for the ICV is a fundamental and crucial problem. Unlike the existing studies that only obtain instantaneous and quasi-static risk fields due to the separation between space and time, this study proposes a novel Spatial-Temporal Risk Field (STRF) that represents the dynamic driving risk from the perspective of time-space coupling for ICVs in dynamic traffic is proposed. In order to generate the field for the different traffic elements, the influential traffic elements are divided into concrete elements and abstract elements according to their features and influencing mechanisms. And then, A biased sweeping method (BSM) is developed for concrete elements, and a modeling method based on Gaussian distribution is developed for abstract elements. The results of the STRF for a traffic scenario are visualized and analyzed in two forms: complete form and slice form. The results show that the STRF can precisely describe the risk distribution of each traffic element in a specific and characteristic shape according to the spatial-temporal situation. Additionally, this study also provides an application example of the STRF in trajectory planning to demonstrate the applicability and availability of the STRF. The STRF-based trajectory planning method greatly benefits from the STRF and shows the potential ability of flexible and personalized trajectory planning. The STRF proposed in this paper can express the dynamics and continuity of the spatial-temporal risk and describe the instantaneous risk by extracting the spatial risk distribution at a certain moment. The STRF can be used in risk assessment, decision making, trajectory planning, driving behavior modeling, and automatic testing.

Sanitizable Cross-System Authorization for Secure Communication in Intelligent Connected Vehicle

Sanitizable Cross-System Authorization for Secure Communication in Intelligent Connected Vehicle