自动驾驶结合数字 CIM 的文章

The digital twin (DT) can virtualize the entire life cycle of the system and is very suitable for use in autonomous driving tests. A framework based on DT to test connected autonomous driving in a limited environment is proposed, in which, the maneuver of DT is used to realize the real connected autonomous driving test in virtual complex road scenes. LTE/G5 used for data communication procedure between self-driving vehicle sensors and roadside environment. However, DT is not indistinguishable from many perceptions, including its basis, development, industrial practices, cyber-physical plotting, and basic elements. In order to highlight the applications and challenges, this paper surveys and analyzes the digital twins from multiple views.

… Autonomous vehicles have attracted attention as a result of … by the upgrading of autonomous driving (AD) technology. … and evaluation method based on a digital twin (DT) is presented to …

Collaborative driving can significantly reduce the computation offloading from autonomous vehicles (AVs) to edge computing devices (ECDs) and the computation cost of each AV. However, the frequent information exchanges between AVs for determining the members in each collaborative group will consume a lot of time and resources. In addition, since AVs have different computing capabilities and costs, the collaboration types of the AVs in each group and the distribution of the AVs in different collaborative groups directly affect the performance of the cooperative driving. Therefore, how to develop an efficient collaborative autonomous driving scheme to minimize the cost for completing the driving process becomes a new challenge. To this end, we regard collaboration as a service and propose a digital twins (DT)-based scheme to facilitate the collaborative and distributed autonomous driving. Specifically, we first design the DT for each AV and develop a DT-enabled architecture to help AVs make the collaborative driving decisions in the virtual networks. With this architecture, an auction game-based collaborative driving mechanism (AG-CDM) is then designed to decide the head DT and the tail DT of each group. After that, by considering the computation cost and the transmission cost of each group, a coalition game-based distributed driving mechanism (CG-DDM) is developed to decide the optimal group distribution for minimizing the driving cost of each DT. Simulation results show that the proposed scheme can converge to a Nash stable collaborative and distributed structure and can minimize the autonomous driving cost of each AV.

In this article, we share our real-world experiences of digital twin, a practical autonomous driving system development paradigm, which generates an integral, comprehensive, precise, and reliable representation of the physical environment to minimize the need for physical testing.

Prototyping and validating hardware–software components, sub-systems and systems within the intelligent transportation system-of-systems framework requires a modular yet flexible and open-access ecosystem. This work presents our attempt to develop such a comprehensive research and education ecosystem, called AutoDRIVE, for synergistically prototyping, simulating and deploying cyber-physical solutions pertaining to autonomous driving as well as smart city management. AutoDRIVE features both software as well as hardware-in-the-loop testing interfaces with openly accessible scaled vehicle and infrastructure components. The ecosystem is compatible with a variety of development frameworks, and supports both single- and multi-agent paradigms through local as well as distributed computing. Most critically, AutoDRIVE is intended to be modularly expandable to explore emergent technologies, and this work highlights various complementary features and capabilities of the proposed ecosystem by demonstrating four such deployment use-cases: (i) autonomous parking using probabilistic robotics approach for mapping, localization, path-planning and control; (ii) behavioral cloning using computer vision and deep imitation learning; (iii) intersection traversal using vehicle-to-vehicle communication and deep reinforcement learning; and (iv) smart city management using vehicle-to-infrastructure communication and internet-of-things.

Burdened by persistent traffic congestion, urban transportation is in a pressing need of more effective traffic guidance schemes. Existing traffic guidance approaches fall short in optimizing benefits, primarily due to their exclusive reliance on current road conditions for decision making and the prevalence of driving egoism within traditional patterns. Autonomous vehicles (AVs), liberated from human control and enhanced by the Internet of Vehicles and edge computing, provide new possibilities for traffic guidance. Nevertheless, it is tough to precisely determine the pertinent information to convey and establish an effective cooperative guidance mechanism in the face of the substantial number of AVs. This article proposes a social value orientation (SVO)-based cooperation mechanism for AVs, through which the driving routes are jointly determined by individual driving demands, local road network conditions, and global benefits. We design a digital twin-based Edge-to-Cloud traffic guidance architecture, leveraging real-time AV decisions and micro-driving characteristics for forthcoming road condition estimation. The hierarchical Edge-to-Cloud structure efficiently mitigates communication and computation overheads in traffic guidance by distributing tasks across different regions. Finally, an innovative method based on inverse reinforcement learning is proposed to address the challenge of adapting guidance policies in response to varying traffic densities and distributions. The simulation results show a 59.1% improvement in the travel achievement ratio under heavy road traffic load, with no significant change in the detour ratio. It indicates an enhanced system driving efficiency, while still safeguarding the individual benefits of AVs.

With the continuous development of digital twin (DT) technology at this stage, research and applications surrounding DTs have gradually become a hot spot. The application of DT technology in the field of autonomous driving tests is being studied. In this paper, we intend to build a highly open DT autonomous driving test platform, combining it with functional units such as simulation test tools, communication equipment, real test vehicles, etc., to form a rich test verification environment. This model supports various types of autonomous driving solutions and algorithm verification tests and has the ability to carry out real vehicle testing and verification in complex virtual scenes under the condition of limited resources. Finally, this article provides a brand-new test method for autonomous driving vehicles.

In the era of technological transformation, mobility and transportation systems are becoming more intelligent and greener. Thanks to powerful technologies and tools, electric and autonomous vehicles are spreading worldwide, substituting internal combustion engine vehicles and revolutionizing the way to drive. In this context, this paper is an extended version of the paper “Digital Twin in Intelligent Transportation Systems: a Review published in 2020 7th International Conference on Control, Decision and Information Technologies (CoDIT)”. The aim of this paper is to provide a comprehensive review of the literature from the last five years on the use of digital twin (DT) technology for Intelligent Transportation Systems (ITSs), focusing on electric and autonomous vehicles. In particular, with respect to the previous work, the focus has been expanded to include DT integration with other cutting-edge technologies, such as the Internet of Things (IoT), Big Data, artificial intelligence (AI), machine learning (ML), and 5G for ITS. Moreover, this paper presents a broad perspective on challenges in EV applications, including tracking, monitoring, battery and charge management, connectivity, security, and privacy. In addition, this paper discusses how DT can be used to effectively address the current issues in electric vehicle services, such as tracking, monitoring, battery and charge management, connectivity, security, and privacy.

… Digital Twin (DT) of an autonomously driving truck for a distribution center. While autonomous driving … can be the first environment where the autonomous driving of trucks is possible. A …

As autonomous driving technology matures, the focus shifts to enhancing the safety and reliability of these systems. Simulation testing is a critical method for efficiently and rapidly validating the performance of autonomous vehicles (AVs). A robust AV system requires extensive testing across a wide range of scenarios and iterative improvements. However, current simulation systems have limitations in supporting diverse scenarios, often relying on expert-designed situations. To address these challenges, we introduce DTTF-Sim, a novel simulation system based on Digital Twin technology for traffic flow. DTTF-Sim aims to accurately replicate real-world traffic flow conditions, offering continuous long-term simulation capabilities for AV testing. The system can simulate detailed dynamic traffic scenarios with a focus on interactions between multiple vehicles and between AVs and background traffic vehicles, modeling the strategic decision-making processes that occur in these encounters. This paper outlines the architecture and functionalities of DTTF-Sim, highlighting its ability to overcome the shortcomings of existing simulation platforms. We demonstrate the effectiveness of DTTF-Sim through case studies and experimental results, showing its potential to significantly advance the development and testing of autonomous driving technologies.

Digital twin maps the physical plant to a real-time digital representation and facilities product design and decision-making processes. In this paper, we propose a novel digital twin-enabled reinforcement learning approach and apply it to an autonomous driving scenario. To further improve the data efficiency of reinforcement learning, which often requires a large amount of agent-environment interactions during the training process, we propose a digital-twin environment model that can predict the transition dynamics of the physical driving scene. Moreover, we propose a rollout prediction-compatible reinforcement learning framework, which is able to further improve the training efficiency. The proposed framework is validated in an autonomous driving task with a focus on lateral motion control. The simulation results illustrate that our method could significantly speed up the learning process and the resulting driving policy could achieve better performance, compared to the conventional reinforcement learning approach, which demonstrates the feasibility and effectiveness of the proposed digital-twin-enabled reinforcement learning method.

With the new industrial revolution of digital transformation, more intelligence and autonomous systems can be adopted in the manufacturing transportation processes. Safety and security of autonomous vehicles (AVs) have obvious advantages of reducing accidents and maintaining a cautious environment for drivers and pedestrians. Therefore, the transformation to data-driven vehicles is associated with the concept of digital twin, especially within the context of AV design. This also raises the need to adopt new safety designs to increase the resiliency and security of the whole AV system. To enable secure autonomous systems for smart manufacturing transportation in an end-to-end fashion, this article presents the main challenges and solutions considering safety and security functions. This article aims to identify a standard framework for vehicular digital twins that facilitate the data collection, data processing, and analytics phases. To demonstrate the effectiveness of the proposed approach, a case study for a vehicle follower model is analyzed when radar sensor measurements are manipulated in an attempt to cause a collision. Perceptive findings of this article can pave the way for future research aspects related to employing digital twins in the AV industry.

… possible solutions based on virtualisation through usage of digital twins. … driving states provided by the Digital Twin, enables operational efficiency and automated feedback to drivers. …

Digital twin (DT) technology is being applied increasingly in the Internet of Vehicles environment, but it still faces many challenges in terms of efficiency and security. In the field of DT-based autonomous driving, many previous works have been done to study the efficient migration methods of DT models. But these works consider the migration process as a blackbox. We study the efficient migration method of the DT model between the edge computing nodes inside the blackbox. We propose three different migration strategies depending on the source of the initial data and the source of the updated data, and evaluate the efficiency of these strategies in terms of migration time in different network environments using the autonomous driving simulation platform CARLA. We then derive methods for selecting migration strategies under different network conditions. During the migration process, there may be external attacks on participating elements or networks. We analyze the security problems that may arise during the migration process and propose corresponding defense methods against such cyberattacks.

Digital Twin (DT) technology, as a promising technology, can achieve the vehicular contexts mapping of the virtual world and physical world in a collaborative autonomous driving (CAD) system. DT technology is developed on the basis of C-V2X, 6G, Mobile Edge Computing (MEC), Machine Learning (ML) and other technologies, which can enable the creation of robust and reliable digital twin-based collaborative autonomous driving architectures, providing a platform for testing, validating, and refining autonomous driving systems in a highly efficient and safe manner. However, the future large-scale CAD system needs greater real-time processing and resource collaboration capability for autonomous vehicles (AVs). Especially considering the mobility of AVs, it puts higher demands on the management of AVs. In this article, we present a digital twin (DT)-based collaborative autonomous driving (DTCAD) three-layer architecture in C-V2X to provide better resource management of AVs. In order to improve the Quality of Service (QoS) and reduce the processing latency in large-scale CAD scenarios, a scalable Deep Reinforcement Learning and Mean Field Game method (DDPG-MFG) are proposed, where the dynamic and real-time interaction between AVs is approximated as a mean-field gaming process in DT resource allocation. Especially, to improve the interaction efficiency between AVs and CAD environment, we design more efficient exploitation and exploration algorithms for AVs. The CARLA simulation demonstrates our proposed algorithm significantly reduces the task offloading latency, and improves the average rewards by 28.5%, 3.5%, and 6.8%, compared with traditional DDPG, TD3, and AC, respectively.

Digital twin cities are frequently used in vehicle and traffic simulations to render realistic on-road driving scenarios under various traffic and environmental conditions. These digital twins provide a high-fidelity replica of the physical world (e.g., buildings, roads, infrastructures, traffic) to create three-dimensional (3D) virtual-physical environments to support various emerging vehicle and transportation technologies such as connected and automated vehicles. These virtual environments provide a cost-effective digital proving ground to evaluate, validate, and test emerging technologies that include control algorithms, localization, perception, and sensors. Replicating a real-world traffic scenario in a digital twin using a traditional 3D modeling approach is a time-consuming and labor-intensive effort. This paper presents a semi-automated spatial framework to construct realistic 3D digital twin cities to support autonomous driving research using readily available geographic information system (GIS) data and 3D prefabricated (prefab) models. We start with a comprehensive review of geospatial data sources of essential digital entities required in a 3D digital twin city and present an integrated GIS-3D modeling pipeline using customized QGIS/GDAL and Blender scripting in Python. The pipeline outputs are realistic 3D digital twin cities compatible with common vehicle simulation software, such as CARLA and IPG CarMaker. The paper closes with a showcase to demonstrate the quality and usability of a digital twin city created to replicate the Shallowford Road corridor in Chattanooga in both Unity and Unreal engine-based virtual environment. The generated digital twin city can be applied to a hardware-in-the-loop simulation environment with an actual testing vehicle to facilitate autonomous driving research.

With the increment of connected vehicles, the level of intelligence becomes more and more irregular, so the difficulties of determining the dynamic safety of self-driving in mixed-transport flow have increased significantly. To solve the problems such as reliability, human-car-road perception, decision making, and control coordination assessment in an intelligent networked environment, this article established a multi-source dynamic game model to carry out the measurement of autonomous vehicle dynamics model, control estimation, decision strategy, forward and backward safety mechanism, and planning of mixed-traffic flow route. The digital twin has real-time, synchronous evolution, and interactivity with a semi-physical environment and a hardware-in-the-loop (HIL) model to control the accuracy of dynamic safety decisions for smart connected vehicles. This all process developed by combining with vehicle-to-everything (as a physical entity) and smart simulation test technology (as a virtual entity), which understands the compound and dynamic safety decision objects such as multi-agent view, multi-source data communication, vehicle switching, V2V transmission, and V2R synchronization for connected autonomous vehicles (CAVs) in the mixed-traffic flow atmospheres.

Digital twins found their genesis in the halls of NASA and the methods of product lifecycle management. Rapidly evolving trends around the proliferation of sensors, the Internet of Things, Industry 4.0, and cyber-physical systems have spurred the growth of digital twins. This paper reviews digital twins and their use in connected and automated vehicles (CAVs). Strictly speaking, digital twins must have communication between a physical system and its model, as opposed to similar methodologies that achieve indirect communication through iteration, or that substitute different parts of a system simulation with bits of hardware or software for testing. In practice, new methodologies for testing CAVs are sufficiently complex and difficult to apply simple labels. This is seen in our review of vehicular digital twins. Several gaps and challenges are apparent for the continued advancement of digital twin applications. We note some developing areas as traffic management centers, digital maps, onboard diagnostics, and logistics. Digital twins foster model reuse and encourage the use of multiple models at different scales of resolution. The role of digital twins will continue to grow as models become more tightly integrated to the physical systems they represent. This will drive such models towards uniqueness (matching a particular vehicle or road), adaptability (evolving with changing conditions and subject to wear and tear), and interpretability (conveying useful information to a human user). A maturing connected infrastructure and the development of smart cities will cause the number of new digital twin services to explode in a myriad of unforeseen ways.

Digital twin (DT) technologies have the potential to revolutionize the online control and optimization methods for cyber-physical systems. However, the lack of the high-fidelity DT models that can accurately simulate the operation of the physical world is a key obstacle to their development. To address this issue, this paper proposes a federated DT (FedDT) architecture and modeling method to create a DT that can mimic the intrinsic dynamics and operational mechanisms of the physical world. Specifically, the FedDT architecture encompasses the internal components for specifying the assignment of functionalities and the external structure to leverage the collaboration of individual DTs. This model provides a digital representation of an online decision-making process for universal control and optimization problems, while considering DTs’ collaboration to enrich their capabilities. Then, we design a federated self-learning algorithm to complete the DT modeling. By using the modeled FedDT, we are able to make purposeful planning and decisions in the digital space that are equivalent to those in the real world, without requiring knowledge of the dynamics of entities and environments. This enables achieving predictive evolution, precise estimation, and reliable decision for online control and optimization. The proposed FedDT offers practical advantages for engineering systems that require predictive and reliable decision-making with strong lookahead capabilities. It is particularly well-suited for applications such as autonomous driving, frequency control in smart grids, and real-time process control in Industrial Internet of Things (IIoT), where accurately modeling physical system dynamics is highly challenging. In real-world deployments, engineers only need to define the observations, actions, and reward signals. The proposed method then autonomously learns the underlying system dynamics and derives informed, data-driven optimization and control strategies. Finally, we demonstrate the effectiveness of our proposed FedDT model by applying it for the autonomous driving use case.

The autonomous driving map model defines the content structure and logical relationships of the map, serving as a foundation for scenario definition in autonomous driving. Addressing the current issues of inconsistency in map models, the challenges of global updates, and the autonomy conflict faced in the “vehicle-road-cloud” collaborative autonomous driving, this paper proposes a digital twin map model tailored for a hierarchical dedicated road network. Initially, the concept of the digital twin map model is elaborated in terms of its scope of use, representation of static and dynamic environments, local and global capabilities, and dispatch and control mechanisms. Subsequently, the paper provides a detailed description of the definitions and functions of each component of the map model, including static and dynamic maps defining scenario information data, and cloud service maps for orderly dispatch and control of scenario resources. Finally, the paper discusses the data organization method of the map model, which is “coordinate partitioning and content layering.” The digital twin map model not only offers high flexibility and ease of management but also simplifies the map updating and maintenance process.

Connected and Autonomous Vehicles (CAVs) will be provided with multiple sensing and connectivity options as well as embedded computing and decision-making capabilities. The resulting technological landscape paves the way for the deployment of a plethora of innovative applications involving different stakeholders, such as insurance companies, car repairs, car manufacturers and public authorities. In such a context it is crucial to collect data in an efficient manner, not to burden the vehicle itself and the network infrastructure, while also providing an interoperable data sharing among all the involved players. The Digital Twin (DT) concept can play a key role to properly retrieve, store and share data as well as to exploit them to monitor, predict and improve the vehicle safety and driving experience. This work proposes a comprehensive framework which encompasses the presence of an edge-based DT interacting with the vehicle and the remote applications. It leverages properly specified interfaces and semantic models for different types of data provided by on-board sensing and learning capabilities. A Proof-of-Concept (PoC) has been developed to assess the practicality of the proposal and its performance in terms of communication and computation footprint under a variety of settings.

The scenario-based tests have been an important means of improving the safety and reliability of autonomous vehicles (AVs). Moreover, ensuring the fidelity of generated scenarios is a safeguard for scenario-based tests. This article presents a new approach to generate twin scenarios for the virtual testing of AVs using meta scenarios. By extracting key parameter clusters from real-world scenarios and redefining and reassembling them, a large number of twin scenarios are generated. This approach addresses the need for diverse scenarios in AV virtual testing and enhances efficiency and hazard detection in the Safety of the Intended Functionality (SOTIF) assessment. The validity of twin scenarios, their temporal logic, and social attributes are verified using the UPPAAL tool, ensuring reliability and fidelity in virtual testing with AV digital twins. This innovative method offers a valuable solution to address challenges in AV virtual testing and improve the overall assessment process.

The automated system replaces the driver, which makes autonomous vehicle to improve safety and convenience, so the market of autonomous vehicle is huge. However, the real-world application of autonomous vehicles faces many challenges due to the immaturity of automated systems. As a consequence, simulation verification plays an irreplaceable role in the application of autonomous vehicle (AV). Car-following is the most common driving scenario in mixed traffic flows, so it is essential to develop an appropriate and effective simulation method for AV. Combined with the existing AV simulation methods and digital twin (DT) technology, this paper proposes a DT-assisted method for AV simulation in a car-following scenario. The method makes the physical vehicle interact with the DT vehicle, and the DT vehicle can dynamically regulate the physical entities through real-time simulation data; the simulation verification can be displayed in the DT scenario to ensure the security of the simulation. Meanwhile, a DT-assisted simulation framework of AV is proposed, the framework includes physical entity components, DT components, and data processing and evaluation components. Besides, a DT-assisted simulation platform is developed base on Unity engine. Finally, the DT-assisted simulation of AV in the car-following scenario is implemented in field experiment. The experimental results show that the proposed method can be effectively conducted AV simulation in car-following, and the average of communication latency is 52.3 ms, which is smaller than the update frequency 15 Hz (66.6 ms) between DT-assisted platform and AV. The DT-assisted simulation method of AV proposed in this paper is applied in the car-following scenario, which effectively solves the challenges of car-following scenario simulation through virtual-real interaction.

The widespread adoption of autonomous vehicles has generated considerable interest in their autonomous operation, with path planning emerging as a critical aspect. However, existing road infrastructure confronts challenges due to prolonged use and insufficient maintenance. Previous research on autonomous vehicle navigation has focused on determining the trajectory with the shortest distance, while neglecting road construction information, leading to potential time and energy inefficiencies in real‐world scenarios involving infrastructure development. To address this issue, a digital twin‐embedded multi‐objective autonomous vehicle navigation is proposed under the condition of infrastructure construction. The authors propose an image processing algorithm that leverages captured images of the road construction environment to enable road extraction and modelling of the autonomous vehicle workspace. Additionally, a wavelet neural network is developed to predict real‐time traffic flow, considering its inherent characteristics. Moreover, a multi‐objective brainstorm optimisation (BSO)‐based method for path planning is introduced, which optimises total time‐cost and energy consumption objective functions. To ensure optimal trajectory planning during infrastructure construction, the algorithm incorporates a real‐time updated digital twin throughout autonomous vehicle operations. The effectiveness and robustness of the proposed model are validated through simulation and comparative studies conducted in diverse scenarios involving road construction. The results highlight the improved performance and reliability of the autonomous vehicle system when equipped with the authors’ approach, demonstrating its potential for enhancing efficiency and minimising disruptions caused by road infrastructure development.

A digital twin is a mapping of real world objects in virtual space. For the study of traffic safety issues, digital twins have great potential to facilitate more accurate and detailed risk analysis. In this study, a digital twin method for highway driving safety analysis is proposed, which consists of three parts: Extracting vehicle motion information in the real world, constructing vehicle motion scenes in the virtual world, and analyzing vehicle driving risks. Firstly, aerial video of vehicle motion is captured by drone, while the microscopic vehicle trajectories are extracted from the video using machine vision algorithms. Secondly, the digital twin of vehicles and roads is constructed, while the motion behavior of vehicles is mapped in a digital space based on virtual physical simulation technology. Finally, according to the stability and trajectory deviation, the driving risks of the vehicle are evaluated, including sideslip, rollover, and guardrail collision. Through a case study, the effectiveness of the proposed digital twin method in driving risk assessment is verified, and one vehicle is found to have a higher driving risk.

Focusing on the low efficiency of the current active safety control method with intelligent networked vehicles, which cannot warn potential dangers in advance, a digital twin vehicle stability monitoring system based on side slip angle is proposed. By analyzing the practical significance of digital twin technology in automobile field, the framework of vehicle stability monitoring system is proposed, which includes vehicle physical system, virtual vehicle model system, vehicle twin data platform and comprehensive monitoring system. Firstly, a virtual vehicle model is established, and its accuracy and real-time performance are verified by real vehicle test. The PSOLSTM (Particle Swarm Optimization Long and Short-Term Memory) algorithm relied on the improved LSTM (Long and Short-Term Memory) is designed in the cause of construct automobile side slip angle state estimator model, which has better accuracy and followability. Secondly, a simulation experiment platform and a real vehicle test platform are built based on the comprehensive monitoring system to verify the accuracy and real-time performance of the designed vehicle side slip angle state estimator model. The experiments show that the maximum estimation error of automobile side slip angle is only 0.634deg under four different working conditions. Finally, a digital twin vehicle stability monitoring platform based on side slip angle is designed. The new intelligent vehicle active safety control mode of “data-driven, virtual-real combination, accurate estimation, autonomous decision-making, shared autonomy” is realized under the drive of digital twin.

… vehicle digital twin and digital twin empowered control optimization. In this paper, a vehicle test is done first based … In this study, the physical entity is the vehicle itself, and a database is …

This survey provides a comprehensive analysis of digital twin (DT) technology as a transformative tool for advancing connected and autonomous vehicles (CAVs) and intelligent transportation systems (ITSs), focusing on advancements in vehicle safety, traffic management, and autonomous driving capabilities. The paper begins by discussing the foundational concepts and enabling technologies behind DT systems, setting the stage for their application in transportation networks. We review DT applications in vehicle safety, highlighting their role in real-time monitoring, predictive maintenance, and risk mitigation. Next, we explore the role of DT technology in optimizing traffic flow, enhancing traffic management, and enabling adaptive responses to dynamic conditions. The paper then examines the integration of DTs in intelligent and autonomous vehicles, emphasizing advancements in simulation, testing, and the development of autonomous driving functionalities. Finally, we outline future research opportunities and challenges for DT applications, providing a roadmap for their continued evolution in CAVs and ITS.

The field of autonomous vehicles (AVs) scenarios has become a more vital process to secure safety. However, the existing scenario approaches to assess AVs have three limitations: low representativeness, low diversity, and low efficiency. Since the real AV driving data cannot be accessed publicly, the scenarios based on low-dimensional data are inadequate to secure representativeness and diversity. Also, generating myriad scenarios using human resources (numerous experts’ input) is inefficient and often lacks consistency. Recognizing these issues, we present a novel approach for generating scenarios of AVs safety. Our approach emphasizes process efficiency in generating scenarios while ensuring diversity and representativeness. We devise a multi-modal image captioning model, referred to as Auto Scenario Generator (Auto-SG), which automatically generates accident scenarios using digital twin data. This model consists of inception-v3, attention mechanism, and GRU. Using SCANeR studio, we also implement various AV accident situations and extract the images for model training. We identify the optimal Auto-SG model through extensive experimentation, enabling the model to generate captions similar to real captions. To evaluate the model, we use BLEU@N and ROUGE-L metrics. Our model achieves approximately 80 to higher scores, demonstrating that the captions of AV accident scenarios are generated correctly. Among the results, we exemplify six top-accuracy accidents as to formalized functional scenarios for assessing AV safety. The scenarios present two intersections and four road-related accidents, including the behaviors of lane changing and turning right. Finally, we suggest the qualitative criterion of ’efficiency’ to evaluate these scenarios, which can consider investments of human resources and consistency of scenarios. We believe our model can help us generate scenarios more efficiently while ensuring diversity and representativeness.

… of digital twin in autonomous driving … driving simulation test platform based on digital twin. Furthermore,the necessary function of simulation test platform is to build real test scenarios, to …

Cooperative, connected and automated mobility (CCAM) across Europe requires efficient and coordinated solutions to overcome cross-borders service discontinuity. The provision of CCAM services across different countries has enormous innovative business potential. However, ensuring uninterrupted connectivity poses technical challenges that 5G technologies aim to solve. In the mark of the 5G-CARMEN project, good progress has been made to develop wireless infrastructure technologies for 5G deployment at the cross-borders. However, besides the field trials, testing the performance and limits of connected vehicles technology to enable safer real-world deployment is a real necessity, particularly, when considering fully autonomous driving vehicles. Digital Twin represents an innovative solution that provides a software replica of the 5G physical network allowing the study and optimization of real-world use cases. This paper presents a simulation-based digital twin developed to emulate connected automated and autonomous vehicles performing cooperative lane change/merge maneuvers, as defined in 5G-CARMEN, in a cross-border scenario.

Digital Twin (DT) is an emerging technology and has been introduced into intelligent driving and transportation systems to digitize and synergize connected automated vehicles. However, existing studies focus on the design of the automated vehicle, whereas the digitization of the human driver, who plays an important role in driving, is largely ignored. Furthermore, previous driver-related tasks are limited to specific scenarios and have limited applicability. Thus, a novel concept of a driver digital twin (DDT) is proposed in this study to bridge the gap between existing automated driving systems and fully digitized ones and aid in the development of a complete driving human cyber-physical system (H-CPS). This concept is essential for constructing a harmonious human-centric intelligent driving system that considers the proactivity and sensitivity of the human driver. The primary characteristics of the DDT include multimodal state fusion, personalized modeling, and time variance. Compared with the original DT, the proposed DDT emphasizes on internal personality and capability with respect to the external physiological-level state. This study systematically illustrates the DDT and outlines its key enabling aspects. The related technologies are comprehensively reviewed and discussed with a view to improving them by leveraging the DDT. In addition, the potential applications and unsettled challenges are considered. This study aims to provide fundamental theoretical support to researchers in determining the future scope of the DDT system

This study reviews the research works published in the last five years on Digital Twin (DT) technology for intelligent transportation systems, focusing on the use of DT in electromobility and autonomous vehicles. The review is carried out systematically, considering specific domains within intelligent transportation in which DT technology is applied in combination with Internet of Thing and 5G technologies. In addition, the paper discusses the current issues in electric vehicle services, such as tracking, monitoring, battery management systems, and connectivity, and how they can be addressed effectively through DT approaches.