Intelligent Connected Vehicles Research Articles

How Does Heterogeneity Affect Freeway Safety? A Simulation-Based Exploration Considering Sustainable Intelligent Connected Vehicles

Intelligent connected vehicles (ICVs) are recognized as a new sustainable transportation mode, which could be promising for reducing crashes. However, the mixed traffic consisting of manually driven vehicles and ICVs may negatively affect road safety due to individual heterogeneity. This study investigated heterogeneity effects on freeway safety-based simulation experiments. Two types of vehicle dynamic models were employed to depict dynamic behaviors of manually driven vehicles and adaptive cruise control (ACC) vehicles (a simplified version of ICVs), respectively. Real vehicle trajectories were utilized to calibrate model parameters based on genetic algorithms. Surrogate safety measures were applied to establish the relationship between vehicle behaviors and longitudinal collision risks. Simulation results indicate that the heterogeneity has negative effects on longitudinal safety. With the higher degree of heterogeneity, longitudinal collision risks are increased. Compared to traffic flow consisting of human drivers only, mixed traffic flow may be more dangerous when the market penetration rate of ACC is low, since the ACC system can be recognized as a new source of individual heterogeneity. Findings of this study show that necessary countermeasures should be developed to improve safety for mixed traffic flow from the perspective of transportation safety planning in the near future.

An Attribute-Isolated Secure Communication Architecture for Intelligent Connected Vehicles

The rapidly increasing connectedness of modern vehicles leads to new security challenges for intelligent connected vehicles (ICVs), where some potential attackers can achieve unauthorized access to gain control of the vehicle by injecting malicious information into in-vehicle electronic control units (ECUs). Therefore, in this paper, a secure attribute-isolated communication architecture for an ICV, which introduces attributes into the ECUs to achieve authorized access among the ECU nodes is proposed. First, an analysis of the functional attributes of all of the in-vehicle ECUs in an intelligent connected environment and a division of the functional attributes of the ECUs into five classifications are performed. Second, based on the above-classified attributes, a secure attribute-isolated communication architecture is demonstrated. The ECUs have different access rights, allowing only the ECUs with the same functional attributes in the internal network of the vehicle to communicate. Then, it is proven that the proposed architecture can resist forgery and eavesdropping attacks under the random oracle model. Finally, the secure attribute-isolated communication architecture is constructed in a hardware environment and evaluated with an in-vehicle network simulator (IVNS). The evaluation results show that the average memory usage with 120 ECUs and 100 messages is below 40 MB and the bus load can be reduced to 18.96% using the proposed security architecture compared to the bus load of existing architectures. Therefore, the proposed secure attribute-isolated communication architecture solves the problem of the tradeoff between the security threat of unauthorized access and the high bus load of existing in-vehicle architectures.

Toward Energy-Aware Caching for Intelligent Connected Vehicles

With the widespread application of infotainment services in intelligent connected vehicles (ICVs), network traffic has grown exponentially, bringing huge burden and energy consumption to the ICV network. Edge caching, which enables edges [e.g., vehicles or roadside units (RSUs)] with cache storages, is a promising technology to alleviate this problem. In this article, in terms of the hybrid communication mode of vehicle to vehicle (V2V) and vehicle to RSU (V2R), an energy-aware caching scheme for infotainment services is proposed. Considering the geographical distribution of vehicles and RSUs as well as the size of transmission content, the energy consumption model in the ICV network is formulated to implement the optimal selection of cache nodes. Then, the selection of the cache node in the ICV network is transformed into the optimal stopping problem and solved by the optimal stopping theory. Finally, we propose a new algorithm for optimal energy-efficiency cache node selection (OEECS). The simulation results show that the proposed OEECS can obtain higher energy saving and lower average access latency than other baseline schemes.

An active safety control method of collision avoidance for intelligent connected vehicle based on driving risk perception

As the complex driving scenarios bring about an opportunity for application of deep learning in safe driving, artificial intelligence based on deep learning has become a heatedly discussed topic in the field of advanced driving assistance system. This paper focuses on analysing vehicle active safety control of collision avoidance for intelligent connected vehicles (ICVs) in a real driving risk scenario, and driving risk perception is based on the ICV technology. In this way, trajectories of surrounding vehicles can be predicted and tracked in a real-time manner. In this paper, vehicle dynamics based state-space equations conforming to model predictive controllers are set up to primarily explore and identify a safety domain of active collision avoidance. Furthermore, the model predictive controller is also designed and calibrated, thereby implementing the active collision avoidance strategy for vehicles based on the model predictive control method. At last, functional testing is conducted for the proposed active collision avoidance control strategy in a designed complex traffic scenario. The research findings here can effectively improve automatic driving, intelligent transportation efficiency and road traffic safety.

Adaptive Task Offloading in Vehicular Edge Computing Networks: a Reinforcement Learning Based Scheme

In recent years, with the rapid development of Internet of Things (IoTs) and artificial intelligence, vehicular networks have transformed from simple interactive systems to smart integrated networks. The accompanying intelligent connected vehicles (ICVs) can communicate with each other and connect to the urban traffic information network, to support intelligent applications, i.e., autonomous driving, intelligent navigation, and in-vehicle entertainment services. These applications are usually delay-sensitive and compute-intensive, with the result that the computation resources of vehicles cannot meet the quality requirements of service for vehicles. To solve this problem, vehicular edge computing networks (VECNs) that utilize mobile edge computing offloading technology are seen as a promising paradigm. However, existing task offloading schemes lack consideration of the highly dynamic feature of vehicular networks, which makes them unable to give time-varying offloading decisions for dynamic changes in vehicular networks. Meanwhile, the current mobility model cannot truly reflect the actual road traffic situation. Toward this end, we study the task offloading problem in VECNs with the synchronized random walk model. Then, we propose a reinforcement learning-based scheme as our solution, and verify its superior performance in processing delay reduction and dynamic scene adaptability.

Distributed perception and model inference with intelligent connected vehicles in smart cities

The fast penetration of Intelligent Connected Vehicles (ICVs) has become the primary growth engine of the automotive industry in recent years. Urban vehicular network consisting of ICVs is evolving towards a distributed intelligent platform for pervasive sensing, connecting and computing in Intelligent Transportation System (ITS) and smart cities. In this paper, we propose that parked vehicles (PVs) could be exploited for environment perception and model inference. We describe the system architecture and its typical application scenarios of distributed environment perception for city roads, parking lots, as well as for commercial and residential buildings. PVs are motivated to assist in deep learning model inference for the captured image data in such applications. Regarding the diversity of PVs in deep learning capability, a differential incentive mechanism is elaborately designed based on contract theory to emulate PVsparticipation. The experiment on the dataset of German Traffic Sign Recognition Benchmark is conducted to verify the effectiveness and efficiency of the proposed approach.

Analysis of the Business Models of the Intelligent and Connected Vehicle Industry

Intelligent connected vehicles (ICV) are recognized as a great opportunity with huge social benefits by the global auto industry. Governments of various countries attach great importance to them, and traditional Original Equipment Manufacturer (OEM) and technology companies are also introducing them into consumers’ lives by virtue of various business models, thereby generating practical value. The business model plays a decisive role in determining whether a company can share the ICV market cake and its future position in the industry. More importantly, it will also determine how fast the ICVs can become reality from imagination. In this paper, the ICV industry ecosystem was sorted out, the business models of ICVs adopted by mainstream OEMs were analyzed, and five typical business models were summarized, i.e. platform model, self-transformation model, traditional Tier1 dependence model, alliance model, and outsourcing model. On this basis, the SWOT analysis method was adopted to systematically analyze the internal and external advantages and disadvantages of the five business models, and forward insights on enterprises’ developing and maintaining the competitive advantages in the ICV industry were proposed from five perspectives of technology, market timing, customer experience, brand, and data.

TrafficChain: A Blockchain-Based Secure and Privacy-Preserving Traffic Map

Intelligent Connected Vehicles (ICVs) can provide smart, safe, and efficient transportation services and have attracted intensive attention recently. Obtaining timely and accurate traffic information is one of the most important problems in transportation systems, which would allow people to select fast routes and avoid congestions, thus saving their travel time on the road. Currently, the most popular ways to obtain traffic information is to inquire navigation agents, e.g., Apple map, and Google map. However, these navigation agents are essentially centralized systems, which are vulnerable to service congestions, a single point of failure, and attacks. Furthermore, users' privacy gets compromised as the agents can know their home and work addresses and hence their identities, track them in real-time, etc. In this paper, we propose TrafficChain, a secure and privacy-preserving decentralized traffic information collection system on the blockchain, by taking advantage of fog/edge computing infrastructure. In particular, we employ a two-layer blockchain architecture in TrafficChain to improve system efficiency, design a privacy-preserving scheme to protect users' identities and travel traces, and devise LSTM based deep learning mechanisms that can defend against Byzantine attacks and Sybil attacks in our system. Furthermore, an incentive mechanism is designed to motivate users to participate in the system. Simulation results show that TrafficChain works very efficiently and is resilient to both Byzantine attacks and Sybil attacks.

Optimization of Energy Consumption in the MEC-Assisted Multi-User FD-SWIPT System

That alleviating the heavy computing task, improving spectral efficiency and prolonging battery lifetime have been the key design challenges in Internet of Things (IoT) and intelligent connected vehicles (ICV). This paper studies the optimization of communication, computation and energy resource to minimize the energy consumption in the mobile terminal, where some superior technologies are included, such as Full-Duplex (FD), Simultaneous Wireless Information and Power Transfer (SWIPT), Mobile-Edge Computing (MEC) and Multi-input Multi-output (MIMO). In this model, the MEC-assisted Base station (BS) works in FD mode, then it can transmit and receive signals in the same frequency and time. Moreover, the mobile devices offload some computation tasks to the BS and complete local computations at the same time. Besides, the mobile device harvests the energy from the BS to support its energy consumption. And, our target is to minimize the energy consumption of mobile devices. Since the problem is non-convex, we propose an iterative solving algorithm including a multi-step optimization. First, we obtain the closed-form solution of the CPU frequency. And then, we transform the remain problem into a convex one to solve it by the interior point algorithm. Finally, we obtain the approximate solution by multiple iterations. Simulation results show that the proposed algorithm is superior to the compared schemes.

Edge Computing in Industrial Internet of Things: Architecture, Advances and Challenges

The Industrial Internet of Things (IIoT) is a crucial research field spawned by the Internet of Things (IoT). IIoT links all types of industrial equipment through the network; establishes data acquisition, exchange, and analysis systems; and optimizes processes and services, so as to reduce cost and enhance productivity. The introduction of edge computing in IIoT can significantly reduce the decision-making latency, save bandwidth resources, and to some extent, protect privacy. This paper outlines the research progress concerning edge computing in IIoT. First, the concepts of IIoT and edge computing are discussed, and subsequently, the research progress of edge computing is discussed and summarized in detail. Next, the future architecture from the perspective of edge computing in IIoT is proposed, and its technical progress in routing, task scheduling, data storage and analytics, security, and standardization is analyzed. Furthermore, we discuss the opportunities and challenges of edge computing in IIoT in terms of 5G-based edge communication, load balancing and data offloading, edge intelligence, as well as data sharing security. Finally, we introduce some typical application scenarios of edge computing in IIoT, such as prognostics and health management (PHM), smart grids, manufacturing coordination, intelligent connected vehicles (ICV), and smart logistics.

Federated Learning in Vehicular Edge Computing: A Selective Model Aggregation Approach

Federated learning is a newly emerged distributed machine learning paradigm, where the clients are allowed to individually train local deep neural network (DNN) models with local data and then jointly aggregate a global DNN model at the central server. Vehicular edge computing (VEC) aims at exploiting the computation and communication resources at the edge of vehicular networks. Federated learning in VEC is promising to meet the ever-increasing demands of artificial intelligence (AI) applications in intelligent connected vehicles (ICV). Considering image classification as a typical AI application in VEC, the diversity of image quality and computation capability in vehicular clients potentially affects the accuracy and efficiency of federated learning. Accordingly, we propose a selective model aggregation approach, where “fine” local DNN models are selected and sent to the central server by evaluating the local image quality and computation capability. Regarding the implementation of model selection, the central server is not aware of the image quality and computation capability in the vehicular clients, whose privacy is protected under such a federated learning framework. To overcome this information asymmetry, we employ two-dimension contract theory as a distributed framework to facilitate the interactions between the central server and vehicular clients. The formulated problem is then transformed into a tractable problem through successively relaxing and simplifying the constraints, and eventually solved by a greedy algorithm. Using two datasets, i.e., MNIST and BelgiumTSC, our selective model aggregation approach is demonstrated to outperform the original federated averaging (FedAvg) approach in terms of accuracy and efficiency. Meanwhile, our approach also achieves higher utility at the central server compared with the baseline approaches.

Backoff-Based Coded Random Access for Intelligent Connected Vehicles

The rapid growth in the scale of the intelligent connected vehicles (ICVs) brings about the need for novel random access schemes that realize collision avoidance and reliable communication. The coded random access (CRA) scheme utilizes successive interference cancellation (SIC) across slots to enable the extraction of multiple users' packets from the collision slots and proves to be an efficient solution to collision resolution. In this paper, we propose a random backoff-based CRA scheme to achieve a tradeoff between the access success probability and access delay for CRA. Specifically, we partition the traditional CRA frame into subframes, so that the user access delay is reduced to the same scale of the subframe length. In addition, the backoff operation among subframes is introduced so that access failures originated from the subframe length limit can be reduced with the retransmission in subsequent subframes. In particular, we consider practical Nakagami-m fading channel in the CRA performance analysis and take into account the capture effect in the iterative interference cancellation process. Simulation results show that backoff-based CRA can significantly reduce the average access delay without severe negative effect on the access success probability for moderate system load compared with traditional CRA. Comparisons with the state-of-the-art non-orthogonal random access (NORA) scheme proposed for 5G indicates that backoff-based CRA provides a feasible solution for random access of large-scale ICVs.

A Task Offloading Scheme in Vehicular Fog and Cloud Computing System

Vehicular fog and cloud computing (VFCC) system, which provides huge computing power for processing numerous computation-intensive and delay sensitive tasks, is envisioned as an enabler for intelligent connected vehicles (ICVs). Although previous works have studied the optimal offloading scheme in the VFCC system, no existing work has considered the departure of vehicles that are processing tasks, i.e., the occupied vehicles. However, vehicles leaving the system with uncompleted tasks will affect the overall performance of the system. To solve the problem, in this paper, we study the optimal offloading scheme that considers the departure of occupied vehicles. We first formulate the task offloading problem as an semi-Markov decision process (SMDP). Then we design the value iteration algorithm for the SMDP to maximize the total long-term reward of the VFCC system. Finally, the numerical results demenstrate that the proposed offloading scheme can achieve higher system reward than the greedy scheme.

Regulatory Architecture of Data Processing for Connected and Automated Driving in Europe

The beginning of the 2020s ought to reflect a steady conclusion of the vast majority of the European Union’s projects with regards to the new era of connectivity and mobility within the European Union dimension. We expect Intelligent Connected Vehicles (ICVs) to step into free circulation within the internal market. Since the operation of the ICVs depends on the number of data processing operations, data processing operations should be precisely determined and framed beforehand. ICVs data operations consist of extraordinarily large volumes and velocity of a data flow which previously existed in traditional relational database systems and could not have been processed within the desired timeframe. Even though the currently adopted database systems are ready to face the new level of data processing, a huge data stream is also faced with complex obstacles and new risks which have never been experienced beforehand.While seeking to ensure safe and secure introduction of a new level of data processing for connectivity and automation at the European Union market, the author precisely examines all potential risks and possibilities of integration into a uniform legal regulation to ensure secured ICVs data processing at all levels. The regulatory framework should document adequate security requirements and defences against ICVs attacks e.g. interference and remote-control interception.

Assessing the Socioeconomic Impacts of Intelligent Connected Vehicles in China: A Cost–Benefit Analysis

The deployment of intelligent connected vehicles (ICVs) is regarded as a significant solution to improve road safety, transportation management, and energy efficiency. This study assessed the safety, traffic, environmental, and industrial economic benefits of ICV deployment in China under different scenarios. A bottom-up model was established to deal with these impacts within a unified framework, based on the existing theories and literature of ICVs’ cost–benefit analysis, as well as China’s most recent policies and statistics. The results indicate that the total benefits may reach 13.25 to 24.02 trillion renminbi (RMB) in 2050, while a cumulative benefit–cost ratio of 1.15 to 3.06 suggests high cost-effectiveness. However, if the government and industry only focus on their own interests, the break-even point may be delayed by several years. Hence, an effective business model is necessary to enhance public–private cooperation in ICV implementation. Meanwhile, the savings of travel time costs and fleet labor costs play an important part in all socioeconomic impacts. Therefore, the future design of ICVs should pay more attention to the utilization of in-vehicle time and the real substitution for human drivers.

FiWi-Enhanced Vehicular Edge Computing Networks: Collaborative Task Offloading

Intelligent connected vehicles (ICVs) equipped with onboard sensors, communication, and computation units, are expected to be the promising paradigm of future vehicles. With the development of ICVs, the conflict between resource-hungry ICV applications and resource-constrained ICVs becomes prominent, and mobile edge computing provides a promising solution to address it. However, facing diverse computation tasks with different granularities and quality-of-service (QoS) requirements, it is obviously not enough to solely rely on the lightweight edge servers placed at the roadside units (RSUs). Making the most of the computational resources at the edge servers and the cloud center appears to be particularly important. In addition, considering the diversity of vehicle-to-everything (V2X) communications and their QoS requirements, the coexistence of multiple wireless communication technologies will persist for a long time, and designing a networking technology to integrate them is necessary and urgent. Toward this end, we introduce fiber-wireless (FiWi) integration to enhance vehicular edge computing networks to provide support for the coexistence of remote cloud and edge servers and propose two collaborative task offloading schemes in FiWienhanced vehicular edge computing networks, where both vehicle-to-RSU and vehicle-to-vehicle (V2V) communications are considered. Extensive numerical results corroborate the superior performance of our schemes on reducing the tasks' processing delay.

Analysis on current situation of China’s intelligent connected vehicle road test regulations

This paper first elaborated the technological development status of the intelligent connected vehicles (ICVs) and sorted out the “intelligent connected vehicle road test management regulations” jointly formulated by the Ministry of Industry and Information Technology (MIIT), the Ministry of Public Security and the Ministry of Transport, and related regulations for major cities in China. The domestic is compare with foreign ICVs road test status and policy. The differences between the national regulation and municipal regulations for the test subjects, test drivers and test vehicles are analysed and enumerated. By comparing the domestic and international road test regulations, it is concluded that the safety is the prerequisite of vehicle road test. It is compulsory to conduct the closed field testing before conducting public road tests.

Joint Beamforming Design for Energy Efficient Wireless Communications in Heterogeneous Intelligent Connected Vehicles Networks

The wireless communications is studied for the intelligent connected vehicles (ICV) networks by supporting heterogeneous access with direct access of one vehicle yet the dual-hop access of another vehicle. The dual-hop access is enhanced by one dedicated vehicle as relaying vehicle with multiple antennas. The goal in this paper is to joint design of both receive beamforming and transmit beamforming at the relaying multi-antenna vehicle to suppress the interference between the two wireless receive vehicles yet match the dual-hop wireless channel as much as possible. The energy efficiency is optimized as the objective function under the constraint of the limited power over every beamforming vector. Due to the difficulty of the optimization, the principle of the signal to leakage plus noise ratio (SLNR) is introduced to the studied ICV networks and thus to obtain the analytical expression of the beamforming vectors. With the derived closed form over all the beamforming vectors, an iterative algorithm is developed for jointly optimizing all the beamforming vectors to maximize the energy efficiency of the whole system, which is guaranteed to be convergent to one local optimum. Numerical simulations show the good performances of the proposed method in ICV networks.

Deep Learning-Based Channel Prediction for Edge Computing Networks Toward Intelligent Connected Vehicles

With the development of intelligent connected vehicles (ICVs), there emerge many new services and applications which involve intensive computation. To support the intensive computation in vehicle-to-everything (V2X) communication system, the framework of edge computing networks has been proposed, which exploits the computation ability of edge nodes at the cost of wireless transmission. Hence, it is of vital importance to predict the wireless channel parameters, which can help schedule the system resource management and optimize the system performance in advance. To fulfil this challenge, this paper proposes a novel prediction model based on long short-term memory (LSTM) network, which is powerful in capturing valuable information in the sequence and hence is good at analyzing the spatio-temporal correlation in the channel parameters. To validate the proposed model, we conduct extensive simulations to show that the proposed model is quite effective in the channel prediction. In particular, the proposed model can outperform the conventional ones substantially.

Intelligent and connected vehicles: Current status and future perspectives

Intelligent connected vehicles (ICVs) are believed to change people’s life in the near future by making the transportation safer, cleaner and more comfortable. Although many prototypes of ICVs have been developed to prove the concept of autonomous driving and the feasibility of improving traffic efficiency, there still exists a significant gap before achieving mass production of high-level ICVs. The objective of this study is to present an overview of both the state of the art and future perspectives of key technologies that are needed for future ICVs. It is a challenging task to review all related works and predict their future perspectives, especially for such a complex and interdisciplinary area of research. This article is organized to overview the ICV key technologies by answering three questions: what are the milestones in the history of ICVs; what are the electronic components needed for building an ICV platform; and what are the essential algorithms to enable intelligent driving? To answer the first question, the article has reviewed the history and the development milestones of ICVs. For the second question, the recent technology advances in electrical/electronic architecture, sensors, and actuators are presented. For the third question, the article focuses on the algorithms in decision making, as the perception and control algorithm are covered in the development of sensors and actuators. To achieve correct decision-making, there exist two different approaches: the principle-based approach and data-driven approach. The advantages and limitations of both approaches are explained and analyzed. Currently automotive engineers are concerned more with the vehicle platform technology, whereas the academic researchers prefer to focus on theoretical algorithms. However, only by incorporating elements from both worlds can we accelerate the production of high-level ICVs.