Vehicle Trajectory Prediction (VTP) plays a pivotal role in the Internet of Vehicles (IoV), significantly aiding in motion planning and accident prevention. Nonetheless, the field faces challenges in distributed data collection and trajectory privacy protection. Existing approaches often incur substantial communication overheads and are not suited for contemporary road environments. Furthermore, there is a noticeable absence of a standardized benchmark. In response to these challenges, our paper introduces an innovative Federated Learning (FL) methodology for VTP. We leverage roadside units (RSUs) as the FL clients, rather than directly using vehicles. This strategy minimizes resource consumption and suits for the current scenario where trajectories are collected by RSUs. In addition, we present FedVTP, a comprehensive benchmark for federated spatial-temporal graph solutions in VTP. FedVTP integrates various strategies and eases for future expansions. We conduct extensive experiments to evaluate the effectiveness of our approach, with detailed analyses provided within FedVTP. This benchmark further encourages the development of new FL strategies for VTP, while enabling equitable comparisons among research works in the field. To facilitate further research and collaboration, our source code will be accessible at https://github.com/FedVTP/FedVTP.

Robustness and safety concerns of perception systems are of great importance for autonomous vehicle navigation applications. Recent researches demonstrate that the surrounding dynamic object detection results of current perception systems can be easily interfered or attacked to mislead the navigation performance of the victim vehicle. In this paper, we develop a GNN based relation learning network to detect the abnormal information in the vehicle perception results, by investigating the relations among the surrounding dynamic objects and also the overall scenario information. Our underlying logic is that the motion of each surrounding object is also affected by its neighbors as well as the whole traffic scenario information, so there should exist a certain amount of consistency among those agents. Learning their spatiotemporal relations provides critical information for detecting the abnormal perception information. Experimental results on the standard CARLA simulator demonstrate our effectiveness in various scenarios and scalability to unseen cases.

This article investigates the multirobot cooperative navigation problem based on raw visual observations. A fully end-to-end learning framework is presented, which leverages graph neural networks to learn local motion coordination and utilizes deep reinforcement learning to generate visuomotor policy that enables each robot to move to its goal without the need of environment map and global positioning information. Experimental results show that, with a few tens of robots, our approach achieves comparable performance with the state-of-the-art imitation learning-based approaches with bird-view state inputs. We also illustrate our generalizability to crowded and large environments and our scalability to ten times number of the training robots. In addition, we demonstrate that our model trained for multirobot case can also improve the success rate in the single-robot navigation task in unseen environments. Note to Practitioners—With the development of intelligent industrial and logistic systems, robotic transportation systems are widely implemented. However, existing multirobot path coordination and navigation approaches are basically under some unreasonable assumptions, which are very hard to be implemented in practical scenarios. This article aims to greatly promote the real application of learning-based multirobot cooperative navigation approach, in order to achieve the following. First, we introduce an end-to-end reinforcement learning framework instead of the commonly used imitation learning strategy, as the latter one needs exhaustive training data to cover all the scenarios and does not have the required generalizability. Second, we directly use the raw sensor data instead of the commonly used bird-eye-view semantic observations, as the latter one is generally not representative of practical application scenario from the robot perspective and cannot solve the occlusion issue. Third, we interpret our learned model to illustrate which parts of the input and shared observations contribute most to the robots’ final actions. The above interpretability ensures predictability (thus safety) of our visuomotor policy in practical applications. Our learned visuomotor policy has the ability to coordinate dozens of robots by only using raw visual observations in unknown environments without map nor global localization information, this is the first time in the literature. Our future work includes solving the sim-to-real issue and conducting physical experiments.

Artificial intelligence-enhanced autonomous unmanned systems, such as large-scale autonomous robot networks, are widely used in logistic and industrial applications. In this article, we address the integrated task assignment, path planning, and coordination problem applied for large-scale robot networks with the existence of uncertainties. In particular, a novel generalized conflict graph is designed which encodes the traveling time cost of the subsequent path planning result of each task-robot assignment and also includes the predicted path conflicts of each two assignments. An integrated optimization problem which aims to minimize the total traveling cost and potential path conflicts simultaneously is first formulated and then transformed into a linear programming instance to obtain the optimal solution. In particular, to satisfy the real-time requirement in large-scale systems, a greedy solution is presented which has the near-optimal performance but can decrease the computational complexity by orders of magnitude. The optimality, scalability, robustness, and efficiency of our approach are demonstrated by comprehensive comparisons with existing state-of-the-art approaches. Note to Practitioners—With the development of artificial intelligence techniques, large-scale autonomous robot networks are increasingly used in the logistic warehouses, unmanned container terminals, and intelligence transportation systems. This article considers the large-scale networks with hundreds or even thousands of unmanned robots which are implemented in lifelong transportation systems with uncertainties existed in practical execution process. Our main concept is to simultaneously minimize the total time cost of all the tasks and the potential motion conflicts among all the robots in the subsequent execution stage, thus alleviating robot congestions, balancing traffic distributions, increasing system efficiency, and improving the robustness and scalability. Lifelong simulations with thousand robots illustrate that our approach can reduce more than 30% of the time steps consumed for coordinating robot motion conflicts, and in the meantime, the throughput, and overall system efficiency are improved. However, simulation results show that the system improvement decreases in the presence of extreme high uncertainties (such as temporary motion and communication failures of the robot), due to the inaccurate conflict prediction in the integrated optimization stage. Our future work includes the deep-learning-based traffic evolution prediction and the online reallocation and planning in highly dynamic scenarios.

This paper presents a simple, dynamic approach to logo detection and recognition in document images. Although there are literatures on both logo detection and logo recognition issues, Current methods lack the adaptability to variable real-world documents. In this paper we initially observe this deficiency from a different point of view and reveal its inherent causation. Then we reorganize the structure of the logo detection and recognition procedures and integrate them into a unified framework. By applying feedback and selecting proper features, we make our framework dynamic and interactive. Experiments show that the proposed method outperforms existing methods in document processing domain.

A new method of logo detection in document images is proposed in this paper. It is based on the boundary extension of feature rectangles of which the definition is also given in this paper. This novel method takes advantage of a layout assumption that logos have background (white spaces) surrounding it in a document. Compared with other logo detection methods, this new method has the advantage that it is independent on logo shapes and very fast. After the logo candidates are detected, a simple decision tree is used to reduce the false positive from the logo candidate pool. We have tested our method on a public image database involving logos. Experiments show that our method is more precise and robust than the previous methods and is well qualified as an effective assistance in document retrieval.

This paper presents the application of collective swarm intelligence in solving problems of non-multiplexing communication in powerline sensor network. Based on collective intelligence behavior in nature, we design a system that allows local sensors to collect local information acquired from the powerline channel. As the information becomes available, the sensor can adaptively adjust its parameters in order to achieve a common goal of non-multiplexing sensor communication throughout entire network. The non-multiplexing sensor communications is implemented using underdetermined blind source separation (uBSS) based on the finite alphabet property of the data. The details of hardware implementation are provided as well as the result from the test-bed network.