The effectiveness of autonomous vehicles relies on reliable perception capabilities. Despite significant advancements in artificial intelligence and sensor fusion technologies, current single-vehicle perception systems continue to encounter limitations, notably visual occlusions and limited long-range detection capabilities. Collaborative Perception (CP), enabled by Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication, has emerged as a promising solution to mitigate these issues and enhance the reliability of autonomous systems. Beyond advancements in communication, the computer vision community is increasingly focusing on improving vehicular perception through collaborative approaches. However, a systematic literature review that thoroughly examines existing work and reduces subjective bias is still lacking. Such a systematic approach helps identify research gaps, recognize common trends across studies, and inform future research directions. In response, this study follows the PRISMA 2020 guidelines and includes 106peer-reviewed articles. These publications are analyzed based on modalities, collaboration schemes, and key perception tasks. Through a comparative analysis, this review illustrates how different methods address practical issues such as pose errors, temporal latency, communication constraints, domain shifts, heterogeneity, and adversarial attacks. Furthermore, it critically examines evaluation methodologies, highlighting a misalignment between current metrics and CP’s fundamental objectives. By delving into all relevant topics in-depth, this review offers valuable insights into challenges, opportunities, and risks, serving as a reference for advancing research in vehicular collaborative perception.

Indoor localization is crucial for applications such as navigation, asset tracking, and emergency response. Fingerprint-based methods that use RSSI are widely adopted; however, they fail under large environmental changes. Unmanned Vehicles (UVs) equipped with high precision sensors are able to collect fingerprints, serving as a promising way by forming a Vehicular Crowdsensing (VCS) campaign. In this paper, we propose “BRAVE”, a Bayesian RL Approach for VCS under Environment changing, while introducing a new metric “Calibration Benefit” to explicitly quantify how effectively a learned trajectory updates those regions of the fingerprint database that have changed and matter most for localization. Specifically, we propose a spatial-temporal Bayesian Network(BN) for change detection, a region rearrangement method for fewer restarts, and an optimistic strategy to balance the exploration and exploitation trade-offs in optimizing calibration benefit. Extensive results on two real-world datasets from SML Center (Shanghai) and Haopu Fashion City (Shanghai) demonstrate that BRAVE outperforms eight baselines and the derived dataset has better localization accuracy compared with the original dataset.

Vehicle-to-everything (V2X) communication plays a crucial role in enabling collaborative intelligence among autonomous vehicles, by utilizing paradigms such as Federated Learning (FL). However, the dynamic and decentralized nature of vehicular networks poses challenges, particularly in maintaining model convergence and robustness without centralized coordination. In this paper, we propose a dynamic Practical Byzantine Fault Tolerant consensus mechanism tailored for decentralized FL in vehicular environments. Our method optimizes federated averaging by addressing the high mobility and intermittent connectivity of vehicles. Through simulations, we evaluate its performance against baseline method, demonstrating improved resilience, efficiency, and adaptability in the presence of adversarial conditions.

Spatial Crowdsourcing (SC) has been proved as an effective paradigm for data acquisition in urban environments. Apart from using human participants, with the rapid development of unmanned vehicles (UVs) technologies, unmanned aerial or ground vehicles (UAVs, UGVs) are equipped with various high-precision sensors, enabling them to become new types of data collectors. However, UGVs’ operational range is constrained by the road network, and UAVs are limited by power supply, it is thus natural to use UGVs and UAVs together as a coalition, and more precisely, UGVs behave as the UAV carriers for range extensions to achieve complicated air-ground SC tasks. In this paper, we propose a novel communication-based multi-agent deep reinforcement learning method called "GARL", which consists of a multi-center attention-based graph convolutional network (GCN) to accurately extract UGV specific features from UGV stop network called "MC-GCN", and a novel GNN-based communication mechanism called "E-Comm" to make the cooperation among UGVs adaptive to constant changing of geometric shapes formed by UGVs. Extensive simulation results on two campuses of KAIST and UCLA campuses show that GARL consistently outperforms eight other baselines in terms of overall efficiency.

Spatial crowdsourcing (SC) has proven as a promising paradigm to employ human workers to collect data from diverse Point-of-Interests (PoIs) in a given area. Different from using human participants, we propose a novel air-ground SC scenario to fully take advantage of benefits brought by unmanned vehicles (UVs), including unmanned aerial vehicles (UAVs) with controllable high mobility and unmanned ground vehicles (UGVs) with abundant sensing resources. The objective is to maximize the amount of collected data, geographical fairness among all PoIs, and minimize the data loss and energy consumption, integrated as one single metric called "efficiency". We explicitly explore both individuality and cooperation natures of UAVs and UGVs by proposing a multi-agent deep reinforcement learning (MADRL) framework called "h/i-MADRL". Compatible with all multi-agent actor-critic methods, h/i-MADRL adds two novel plug-in modules: (a) h-CoPO, which models the cooperation preference among heterogeneous UAVs and UGVs; and (b) i-EOI, which extracts the UV’s individuality and encourages a better spatial division of work by adding intrinsic reward. Extensive experimental results on two real-world datasets on Purdue and NCSU campuses confirm that h/i-MADRL achieves a better exploration of both individuality and cooperation simultaneously, resulting in a better performance in terms of efficiency compared with five baselines.

Intelligent Transportation Systems (ITS) utilises the growing trend of both communication technologies and intelligent analytics to make transportation systems more smart and efficient. Federated Learning, a privacy-preserving machine learning paradigm shows promise in being applied in this field. However, the data and device heterogeneity, and highly dynamic environment in ITS pose challenges to the performance of federated learning. One of the recent approaches to address the challenges are to choose proper participants from available clients during training. However, this research field is not fully investigated yet, and many works are still based on the classic random-based selection scheme. In this paper, we present Newt, an enhanced federated learning approach. On one hand, it includes a new client selection utility that explores the trade-off between accuracy performance in each round and system progress. On the other hand, it highlights a feedback control on the selector. Specifically, we implement a control on the selection frequency as a new dimension of client selection method design. We evaluate the proposed system with DNN training tasks on large scale FEMNIST-based datasets that are of different heterogeneity properties. The experiments show that our method outperforms the other baseline methods by as large as 20%.

Anomaly detection (AD) aims to distinguish abnormal instances from what is defined as normal, which strongly correlates with the safe and robust applications of machine learning. A well-performed anomaly detector often relies on the training on massive labeled data, while it is of high cost to annotate data in practice. Fortunately, this dilemma can be solved by transferring the knowledge of a label-rich dataset (source domain) to assist the learning on the label-scarce dataset (target domain), which is known as domain adaptation in transfer learning. In this paper, we propose a Multi-spectral Cross-domain Representation Alignment (MsRA) method for the anomaly detection in the domain adaptation setting, where we can only access normal source data andlimitednormal target data. Specifically, MsRA first constructs multi-spectral feature representations by fusing different frequency components of the original features, which mitigates the information scarcity due to limited target training data by capturing richer input pattern information. Then we employ the adversarial training strategy to learn domain-invariant features and force the features of normal data to be more compact by the center clustering. Finally, the distance of each sample to the prototype of normal class can be used as its anomaly score, where the prototype is the center of both source and target data. In this way, we achieve anomaly detection in an end-to-end manner, without two-stage training for feature extraction and anomaly detection. Comprehensive experiments on cross-domain anomaly detection benchmarks validate the effectiveness of MsRA.

With the current large scale deployment of machine learning technologies, such as those on cloud servers and edge and IoT hardwares, machine learning systems have been widely prevalence. Practical requirement has driven their performance increase in both academia and industry. However, the application requirement varies greatly across different applications, and directly using off-the-shelf systems might not be sufficient in many cases. In this work, we first propose to implement a series of techniques to optimize performance of convolution operation, one of the most important operations, in constructing deep learning networks. Besides, we also propose to apply the automated empirical optimisation of software approach to improve the performance of operators in machine learning system, most notably across various hardware platforms. Evaluation compared to existing libraries on different hardware devices has proved the efficiency of our proposed method.

With the massive amount of data generated from mobile devices and the increase of computing power of edge devices, the paradigm of Federated Learning has attracted great momentum. In federated learning, distributed and heterogeneous nodes collaborate to learn model parameters. However, while providing benefits such as privacy by design and reduced latency, the heterogeneous network present challenges to the synchronisation methods, or barrier control methods, used in training, regarding system progress and model convergence etc. The design of these barrier mechanisms is critical for the performance and scalability of federated learning systems. We propose a new barrier control technique called Probabilistic Synchronous Parallel (PSP). In contrast to existing mechanisms, it introduces a sampling primitive that composes with existing barrier control mechanisms to produce a family of mechanisms with improved convergence speed and scalability. Our proposal is supported with a convergence analysis of PSP-based SGD algorithm. In practice, we also propose heuristic techniques that further improve the efficiency of PSP. We evaluate the performance of proposed methods using the federated learning specific FEMNSIT dataset. The evaluation results show that PSP can effectively achieve good balance between system efficiency and model accuracy, mitigating the challenge of heterogeneity in federated learning.

Emerging Machine Learning (ML) techniques, such as Deep Neural Network, are widely used in today's applications and services. However, with social awareness of privacy and personal data rapidly rising, it becomes a pressing and challenging societal issue to both keep personal data private and benefit from the data analytics power of ML techniques at the same time. In this paper, we argue that to avoid those costs, reduce latency in data processing, and minimise the raw data revealed to service providers, many future AI and ML services could be deployed on users' devices at the Internet edge rather than putting everything on the cloud. Moving ML-based data analytics from cloud to edge devices brings a series of challenges. We make three contributions in this paper. First, besides the widely discussed resource limitation on edge devices, we further identify two other challenges that are not yet recognised in existing literature: lack of suitable models for users, and difficulties in deploying services for users. Second, we present preliminary work of the first systematic solution, i.e. Zoo, to fully support the construction, composing, and deployment of ML models on edge and local devices. Third, in the deployment example, ML service are proved to be easy to compose and deploy with Zoo. Evaluation shows its superior performance compared with state-of-art deep learning platforms and Google ML services.

Machine Learning (ML) techniques have begun to dominate data analytics applications and services. Recommendation systems are the driving force of online service providers such as Amazon. Finance analytics has quickly adopted ML to harness large volume of data in such areas as fraud detection and risk-management. Deep Neural Network (DNN) is the technology behind voice-based personal assistance, self-driving cars [1], image processing [3], etc. Many popular data analytics are deployed on cloud computing infrastructures. However, they require aggregating users’ data at central server for processing. This architecture is prone to issues such as increased service response latency, communication cost, single point failure, and data privacy concerns.

Dynamic event detection by using participatory sensing paradigms has received growing interests in recent years, where detection tasks are assigned to smart device users who can potentially collect needed sensory data from the equipped sensors. These data can be utilized to detect interested events like noise, air pollution, or even earthquake. Since most existing solutions focus on centralized detection approaches that, however, usually cause heavy communication overhead, it is strongly desired to design distributed solutions to reduce energy consumption while achieving a high level of detection accuracy. In this paper, we first present a novel Minimum Cut based centralized detection algorithm as the performance benchmark, and then introduce a novel distributed, energy-efficient solution, where an optimization problem is formulated and an optimal solution is derived. Simulations based on a real-trace driven data set in Beijing demonstrate the effectiveness of our proposed algorithms.