To support various upper applications of intelligent vehicles ranging from driving assistance to automated planning and control, accurate localization, and tracking are the fundamental tasks. Given the limited versatility and efficiency of traditional single-vehicle multisensor and multivehicle multisensor localization and tracking solutions, this article presents an efficient distributed multivehicle cooperative tracking framework via multicast. Once the self-positioning data is locally fused with assistance from roadside units, each vehicle shares the local-fusion results with surrounding vehicles through multicast and observes surrounding vehicles with on-board sensing equipment. The vehicles can then jointly feed the local-fusion results, received multicast information, and observation results into a global filter to obtain accurate and robust cooperative tracking. By leveraging multicast, the communication load is reduced, which promotes the efficiency of communication resource utilization. By optimizing the data fusion procedure, the error caused by error correlation is eliminated and the sensitivity to nonideal conditions, including packet loss, interruption, time-varying cooperative vehicles, etc., is reduced, which improves the versatility of the framework in real-world applications. Furthermore, several practical issues, such as random communication delay, packet loss, communication load, and localization robustness are also involved. To verify the effect of the framework, both theoretical analyses and simulation results are presented to show the accuracy and robustness of our proposed cooperative tracking framework.

The U-Net-like coarse-to-fine network design is currently the dominant choice for dense prediction tasks. Although this design can often achieve competitive performance, it suffers from some inherent limitations, such as training error propagation from low to high resolution and the dependency on the deeper and heavier backbones. To design an effective network that performs better, we instead propose Recurrent Multiscale Feature Modulation (R-MSFM), a new lightweight network design for self-supervised monocular depth estimation. R-MSFM extracts per-pixel features, builds a multiscale feature modulation module, and performs recurrent depth refinement through a parameter-shared decoder at a fixed resolution. This network design enables our R-MSFM to maintain a more lightweight architecture and fundamentally avoid error propagation caused by the coarse-to-fine design. Furthermore, we introduce the mask geometry consistency loss to facilitate our R-MSFM for geometry consistent depth learning. This loss penalizes the inconsistency of the estimated depths between adjacent views within the nonoccluded and nonstationary regions. Experimental results demonstrate the superiority of our proposed R-MSFM both at model size and inference speed, and show state-of-the-art results on two datasets: KITTI and Make3D.

Recent years have witnessed the exciting developments for the power grid. For instance, many traditional mechanical components are being replaced by modern electronics devices that can operate intelligently; new elements, such as renewable energy resources and various large-scale energy storage, are introduced into the grid to bring a new outlook on the system operation and control; smart appliances are produced to facilitate more customized and efficient energy usage; and advanced sensors, such as the phasor measurement units (PMUs) and the advanced metering infrastructure (AMI), are designed and implemented for real-time wide-area monitoring of the system conditions. In general, the power grid is increasingly organized and managed as an interconnected network of many different individual components that operate intelligently in a distributed but connected manner, as opposed to the traditional centralized fashion. In other words, the power grid is evolving into a big Internet of Things (IoT).

In this paper, we study a cooperative perception scheme in a vehicular sensor network, attempting to fuse the semantic information provided by different sensors at multiple vehicles, so as to expand the vehicle’s perception range, eliminate blind spots, improve the ability to handle environmental interference and enhance the accuracy and robustness of the perception results. The key to guide the fusion process is the evaluation of the confidence levels of the outputs provided by various machine learning schemes implemented at individual sensors in the vehicular sensor network. We first propose an evaluation criterion termed as Environmental Sensitivity (ES), which is used to measure the sensitivity of the network to environmental changes. Based on the ES, we further evaluate the confidence of the perception output of neural networks and quantify the confidence level considering the abnormal level of the input data, the general performance of the perception algorithm, the detection performance and the ES of the network. Semantic information fusion algorithm is then developed based upon the confidence levels. Experiment results are provided to validate the proposed fusion method in various scenarios.

Abstract Sensing is an essential part in autonomous driving and intelligent transportation systems. It enables the vehicle to better understand itself and its surrounding environment. Vehicular networks support information sharing among different vehicles and hence enable the multi‐vehicle multi‐sensor cooperative sensing, which can greatly improve the sensing performance. However, there are a couple of issues to be addressed. First, the multi‐sensor data fusion needs to deal with heterogeneous data formats. Second, the cooperative sensing process needs to deal with low data quality and perception blind spots for some vehicles. In order to solve the above problems, in this paper the occupancy grid map is adopted to facilitate the fusion of multi‐vehicle and multi‐sensor data. The dynamic target detection frame and pixel information of the camera data are mapped to the static environment of the LiDAR point cloud, and the space‐based occupancy probability distribution kernel density estimation characterization fusion data is designed , and the occupancy grid map based on the probability level and the spatial level is generated. Real‐world experiments show that the proposed fusion framework is better compatible with the data information of different sensors and expands the sensing range by involving the collaborations among multiple vehicles in vehicular networks.

Abstract Vehicle behaviour prediction provides important information for decision‐making in modern intelligent transportation systems. People with different driving styles have considerably different driving behaviours and hence exhibit different behaviour tendency. However, most existing prediction methods do not consider the different tendencies in driving styles and apply the same model to all vehicles. Furthermore, most of the existing driver classification methods rely on offline learning that requires a long observation of driving history and hence are not suitable for real‐time driving behaviour analysis. To facilitate personalised models that can potentially improve vehicle behaviour prediction, the authors propose an algorithm that classifies drivers into different driving styles. The algorithm only requires data from a short observation window and it is more applicable for real‐time online applications compared with existing methods that require a long term observation. Experiment results demonstrate that the proposed algorithm can achieve consistent classification results and provide intuitive interpretation and statistical characteristics of different driving styles, which can be further used for vehicle behaviour prediction.

With the unprecedented development of smart vehicles and roadside units equipped with wireless connectivity, the transportation system is undergoing revolutionary changes in the past decade or two. Bearing safety and efficiency as the utmost objectives, the vehicular environments are witnessing explosive increase of various sensors onboard vehicles and equipped at transportation infrastructures. On the one hand, these sensors are destined to be wirelessly connected to provide more comprehensive situational awareness for transportation purposes. On the other hand, the abundance of sensor data of the environment can potentially shed light on the channel propagation characteristics that lie at the core of any communications system design. The integrated sensing and communications (ISACs) is henceforth both necessary and natural in vehicular communications networks (VCN). Different from existing ISAC works that target generic environments but are limited to dual-function radar-communications (DFRC), in this article we focus on transportation scenarios and applications but take a wholistic view of ISAC possibilities. First, we argue that, even though many sensors in transportation settings are nonradio-frequency (RF)-based, functional ISAC (fISAC) is feasible and necessary, in both communication-centric (CC) or sensing-centric (SC) modes. To facilitate this, the concept of synesthesia is introduced to ISAC to accommodate “machine senses” in the RF and non-RF formats. We then zoom in to RF-based sensors and propose the so-termed signaling ISAC (sISAC), with either unified-hardware (UH) or separate-hardware (SH) platforms, and delineate the unique issues arising in transportation settings. Several transportation-specific case studies are included to demonstrate these various ISAC regimes. Toward the end, the relationships of these ISAC subcategories are discussed with a roadmap laid out.

In autonomous driving, environment perception is the fundamental task for intelligent vehicles which provides the necessary environment information for other applications. The main issues in existing environment perception can be categorized into two aspects. On the one hand, all sensors are prone to measurement errors and failures. On the other hand, in complex driving environments, vehicles may encounter a variety of blind spots caused by vehicle occlusions, overlaps, and harsh weather conditions, which will cause sensors to experience low-quality data or to miss crucial environmental information. To cope with these issues, a multivehicle and multisensor (MVMS) cooperative perception method is presented to construct the occupancy grid map (OGM) of vehicles in a global view for the environment perception of autonomous driving. Distinct from existing environment perception methods, our proposed MVMS-OGM not only provides continuous geographical information but also captures and fuses continuous information with soft occupancy probabilities, resulting in more comprehensive and raw environmental information. Simulations and real-world experiments demonstrate that the proposed approach not only expands the perception range in comparison with single-vehicle sensing but also better captures the uncertainty of sensor data by fusing the occupancy probabilities with soft information.

Recent years have witnessed exciting developments in our transportation system with increasingly intelligent vehicles and infrastructure. The transportation system is envisioned to be highly heterogeneous, consisting of diverse participants with mixed intelligence and connectivity. Among them, autonomous vehicles have the highest intelligence and connectivity level and could contribute greatly to the operation of the transportation system in an efficient and reliable manner. However, the current design of autonomous driving techniques is mostly concerned with the autonomous vehicle at the individual level, and the overall transportation system does not provide proactive support to autonomous driving. In fact, the increasing intelligence and connectivity in transportation could be leveraged to significantly enhance the safety and efficiency of individual vehicles and the entire system. To facilitate this, vehicles need to interact and cooperate both among themselves and with the transportation infrastructure and management. In this article, we propose the societal intelligence (SI) framework. Different from the existing multientity intelligence frameworks, SI allows for much diverse interactions among the multiple entities at different levels and is thus suitable for transportation. In addition, we also render the driving process into four functional layers and demonstrate how the social intelligence framework can adapt to these layers, respectively.

For autonomous driving of unmanned vehicles in intelligent transportation systems, multi-vehicle cooperative perception supported by vehicular networks can greatly improve the accuracy and reliability of the perception decisions. Currently, the perception decisions for a single vehicle are mostly provided by neural networks. Therefore, in order to fuse the perception decisions from multiple vehicles, the credibility of the neural network outputs needs to be studied. Among various factors, the environment is one of the most important affecting vehicles' perception decisions. In this paper, we propose a new evaluation criteria for the neural networks used in the perception module of unmanned vehicles. This criterion is termed as Environmental Sensitivity (ES), indicates the sensitivity of the network to environmental changes. We design an algorithm to quantitatively measure the ES value of different perception networks based on the extracted features. Experimental results show that our algorithm can well capture the sensitivity of the network in different environments and the ES values will be helpful to the subsequent decision fusion process.

The mobility status of vehicles play a crucial role in most tasks of Autonomous Vehicles (AVs) and Intelligent Transportation System (ITS). To operate securely, a precise, stable and robust mobility tracking system is essential. Compared with self-tracking that relies only on mobility observations from on-board sensors (e.g. Global Positioning System (GPS), Inertial Measurement Unit (IMU) and camera), cooperative tracking increases the precision and reliability of mobility data greatly by integrating observations from road side units and nearby vehicles through V2X communications. Nevertheless, cooperative tracking can be quite vulnerable if there are malicious collaborators sending bogus observations in the network. In this paper, we present a dynamic sequential detection algorithm, dynamic model based mean state detection (DMMSD), to exclude bogus mobility data. Simulations validate the effectiveness and robustness of the proposed algorithm as compared with existing approaches.

The mobility status of self and surrounding vehicles provides important information to various tasks in autonomous driving (AD) and intelligent transportation system (ITS). Accordingly, a precise, stable, and robust mobility tracking framework is essential. Compared with self-tracking that relies only on mobility observations from onboard sensors [e.g., global positioning system (GPS), inertial measurement unit (IMU), and camera], cooperative tracking markedly increases the precision and reliability of the mobility information by integrating observations from roadside units (RSUs) and nearby vehicles through vehicle-to-everything (V2X) communications in the Internet of Vehicles (IoV). Nevertheless, cooperative tracking can be quite vulnerable if there are malicious users sending bogus observations in the cooperative network. In this article, we present a malicious user detection framework, which includes two sequential detection algorithms and a secure mobility data exchange and fusion model to detect and remove bogus mobility information and integrate proposed detection algorithms with previous data fusion algorithms, which secures the cooperative mobility tracking in AD, ITS. Simulations validate the effectiveness and robustness of the proposed framework under different types of attacks.

Sensor fusion maybe used to improve detection performance in applications. The idea is to make decisions locally, and then transmit them to a global fusion centre where the global decision is made. For global decision making, Bayes or Neyman–Pearson reasoning determines the optimal use of the local decision variables. However, the determination of the local decision variables that minimise global error probability is intractable. In this study, the authors design local decisions that maximise the mutual information between a binary decision variable and the underlying binary state. This serves as a benchmark against which globally optimum solutions maybe compared. Then, they use the theory of large deviations (LDs) to determine a local decision rule that minimises asymptotic global error probability. The use of LD produces a one‐dimensional search on a receiver operating characteristic curve to equalise the error exponents for local false alarm and miss probabilities. Many interesting properties of the LD solution are proved. Numerical results illustrate the performance of the asymptotically optimum decision rule for finite collections of sensors.

The efficient design of evolving smart grid faces serious challenges of a variety of malicious attacks. In this paper, the complex decision making processes between a network of electricity users that perform wireless meter data aggregation via other users and multiple sophisticated wireless attackers that are able to act as eavesdroppers and as jammers are investigated. We model the interactions among users and attackers as a hybrid network formation-nonzero sum game. On the one hand, each user seeks to choose the next-hop user that can minimize the electricity use cost which reflects the security and reliability of its meter data transmission. On the other hand, the objective of the attackers is to choose whether to eavesdrop, jam, or use a combination of both strategies, in a way to increase the total network costs. To solve this game, we devised an algorithm based on fictitious play to reach a mixed-strategy Nash equilibrium. Simulation results suggested that the proposed meter data aggregation scheme enables the electricity users to significantly decrease their expected costs as well as adapt to sophisticated wireless attacks in the smart grid.

In this paper, we propose a two-layer game-theoretic framework to solve the network selection problem in heterogeneous wireless networks (HetNets). At the intra-network layer, a hierarchical game among the SP and its admitted users is employed, and the closed-form equilibrium solutions for the pricing and transmission rate are provided. Meanwhile, at the inter-network layer, all service providers (SP) and the active users are engaged in a dynamic hedonic game and finally self-organized into a Nash-stable coalition structure. The key feature of our proposed approach is the inclusion of social-behavioral (SoBe) constraints capturing the real-world user and SP preferences, hierarchy and membership. Not only that SoBe more accurately models practical scenarios, it also leads to markedly reduced unnecessary handovers, and well maintained call blocking rate. Simulations confirm the superior performance of SoBe in comparison with the widely adopted user-driven alternative in terms of nearly all critical performance criteria.

To ensure resilient and reliable meter data collection that is essential for the smart grid operation, we propose a QoS-oriented wireless routing scheme. Specifically tailored for the heterogeneity of the meter data traffic in the smart grid, we first design a novel utility function that not only jointly accounts for system throughput and transmission latency, but also allows for flexible tradeoff between the two with a strict transmission latency constraint, as desired by various smart meter applications. Then, we model the interactions among smart meter data concentrators as a mixed-strategy network formation game. To avoid potential information exchange which is not always practical in meter data collection scenario, a stochastic reinforcement learning algorithm with only private and incomplete information is proposed to solve the network formation problem. Such a problem formulation, together with our proposed stochastic learning algorithm on graph, results in a steady probabilistic route. Both contributions are novel and unique in comparison with existing work on this topic. Another distinct feature of our approach is its capability of effectively maintaining the QoS of smart meter data collection, even when the network is under fault or attack, as verified by simulations.

Spectrum sensing is an important building block to realize the cognitive radio concept. In order to combat fading in the wireless environment, cooperation among the sensing users is usually employed. In this paper, we develop a closed-form optimal local decision threshold for cooperative spectrum sensing in cognitive radio systems via large deviation analysis. The resultant strategy is independent of the total number of cooperating users. We show that it is not only asymptotically optimal when the number of sensing users approaches infinity, but also can achieve the maximum diversity. Numerical results are provided to verify our analysis.

Cognitive radio is gaining increasingly interest as a promising solution to current spectrum resource shortage. Within various tasks of cognitive radio, spectrum sensing is the fundamental one, but is challenged by wireless channel fading. By collecting diversity among different users, cooperative sensing can overcome the fading problem very well. Usually, only the local binary decisions are available for sensing cooperation due to limitation of the channel bandwidth. However, in our previous work [1], we have shown that this strategy will either sacrifice diversity or signal-to-noise ratio (SNR) gain. In this paper, we will study cooperative sensing with ternary local decisions. Compared with the binary cooperative sensing, this strategy will can regain diversity and recover the extra SNR loss by appropriate threshold selection, without increasing the decision forwarding bandwidth.

The battery nonlinearity has never been considered for energy analysis in relay networks. In this letter, we adopt the realistic nonlinear battery model, apply the battery energy consumption results in to general relay networks, investigate the optimum and suboptimum energy allocation solutions for relaying transmission, and establish the relay selection criterion from the battery energy efficiency perspective. Our analyses and comparisons show that relaying does not always increase the system energy efficiency. We further establish closed-form conditions that can be easily checked to determine whether the relay transmission is preferable to the direct transmission. Numerical examples are also presented to verify these results.

In this paper, we compare the battery power efficiencies of various pulse-based modulations widely adopted for their low complexity. Taking into account circuit modules and battery imperfectness, we establish simple closed-form analytical formulas which can be used to conveniently determine the relative preference between arbitrary pulse-based modulation pairs in terms of their actual average battery energy consumption.

Sensor nodes in wireless sensor networks (WSNs) are often expected to operate on batteries for a long period of time. Battery power efficiency (BPE) is therefore a critical factor dictating the lifetime of WSNs. In this paper, we aim to select the appropriate modulation scheme from a battery power efficiency perspective. Pulse position modulation (PPM) and on-off keying (OOK), as low-complexity pulse-based modulation schemes, are used for a case study of our methodology. The analysis is based on a general model that integrates typical WSN transmission and reception modules with a realistic nonlinear battery model. We first present the quantitative comparison results under general system design criteria. Then, we illustrate the comparisons with theoretical and numerical results under the bit error rate (BER) system design criterion.

Spectrum sensing is a critical issue in cognitive radio networks. Cooperation among the secondary users is utilized to improve the performance of spectrum sensing. In this paper, we quantify the gain of cooperation in spectrum sensing by introducing the concept of diversity order. With different system performance metrics, we introduce different diversity quantities. We analyze the single-user sensing and the multi-user sensing with soft and hard information fusion strategies using the diversity quantities as our figure of merit. In particular, we discuss the selection of threshold in each sensing scheme with respect to the diversity performance and obtain the quantitative relationship among the diversity performance, threshold, and the number of cooperative users. We also observe and quantify the tradeoff between false alarm and missed detection performance in all spectrum sensing schemes.