Effective monitoring of unusual transportation behaviors, such as wrong-way cycling (i.e., riding a bicycle or e-bike against designated traffic flow), is crucial for optimizing law enforcement deployment and traffic planning. However, accurately recording all wrong-way cycling events is both unnecessary and infeasible in resource-constrained environments, as it requires high-resolution cameras for evidence collection and event detection. To address this challenge, we propose WWC-Predictor, a novel method for efficiently estimating the wrong-way cycling ratio, defined as the proportion of wrong-way cycling events relative to the total number of cycling movements over a given time period. The core innovation of our method lies in accurately detecting wrong-way cycling events in sparsely sampled frames using a light-weight detector, then estimating the overall ratio using an autoregressive moving average model. To evaluate the effectiveness of our method, we construct a benchmark dataset consisting of 35 minutes of video sequences with minute-level annotations. Our method achieves an average error rate of a mere 1.475% while consuming only 19.12% GPU time required by conventional tracking methods, validating its effectiveness in estimating the wrong-way cycling ratio. Our source code is publicly available at:https://github.com/VICA-Lab-HKUST-GZ/WWC-Predictor

Autonomous navigation is one of the key techniques of the extensive applications of unmanned aerial vehicles (UAVs) in various fields, such as urban traffic management, disaster response, and intelligent logistics. However, 3D cooperative navigation of UAVs in dense urban environments continues to face significant challenges, including extensive state spaces, trajectory smoothness, collaborative operations, and complex motion control. To address these challenges, we propose a distance-attention augmented reinforcement learning (DA2RL) algorithm to enable safe and smooth cooperative navigation of UAVs. In DA2RL, a distance-attention-based actor network is developed to refine the observation space and capture sequential information during training. A historical feature flow-based critic network is then developed for more accurate action decision evaluation. Additionally, several non-sparse reward functions are designed to further accelerate the training process. Finally, numerical experiments and comparison results demonstrate that DA2RL achieves superior navigation performance and generalization capability compared to recent benchmark works.

Multi-UAV autonomous navigation serves as a fundamental basis for a wide array of applications across various fields. However, achieving cooperative navigation in complex and unknown three-dimensional environments remains a significant challenge. In this paper, a novel attention-based augmented reinforcement learning algorithm is proposed for multi-UAV navigation (A2RL-NAV) in 3D environments. Specifically, we incorporate an LSTM network along with an attention mechanism within the actor network. Additionally, we design some goal-oriented reward functions to address the issue of sparse rewards. Finally, we validate the effectiveness and robustness of our approach by comparing the proposed algorithm with several baseline algorithms through simulation experiments.

Over the past decade, unmanned aerial vehicles (UAVs) have been widely applied in many areas, such as goods delivery, disaster monitoring, search and rescue etc. In most of these applications, autonomous navigation is one of the key techniques that enable UAV to perform various tasks. However, UAV autonomous navigation in complex environments presents significant challenges due to the difficulty in simultaneously observing, orientation, decision and action. In this work, an efficient state-decomposition deep deterministic policy gradient algorithm is proposed for UAV autonomous navigation (SDDPG-NAV) in 3-D complex environments. In SDDPG-NAV, a novel state-decomposition method that uses two subnetworks for the perception-related and target-related states separately is developed to establish more appropriate actor networks. We also designed some objective-oriented reward functions to solve the sparse reward problem, including approaching the target, and avoiding obstacles and step award functions. Moreover, some training strategies are introduced to maintain the balance between exploration and exploitation, and the network is well trained with numerous experiments. The proposed SDDPG-NAV algorithm is capable of adapting to surrounding environments with generalized training experiences and effectively improves UAV’s navigation performance in 3-D complex environments. Comparing with the benchmark DDPG and TD3 algorithms, SDDPG-NAV exhibits better performance in terms of convergence rate, navigation performance, and generalization capability.

Autonomous navigation of multiple unmanned aerial vehicles (UAVs) serves as the foundation for their widespread applications in various fields. However, multi-UAV cooperative navigation is a challenging problem because it is difficult for UAVs to plan efficient paths while avoiding collisions with both neighboring UAVs and other obstacles. In this work, an efficient reinforcement learning (RL)-based cooperative navigation algorithm (RL-CN) is proposed to make optimal decisions in dense and dynamic environments. In RL-CN, the multi-UAV cooperative navigation is formulated as a Markov decision process and an enhanced RL method is proposed for continuous control of multiple UAVs. To solve the sparse reward problem, a group of reward functions are designed in reward shaping. Next, a staged-tuning with two subactor networks strategy is developed to accelerate the training process and improve the navigation performance. Then, an enhanced prioritized experience replay strategy is designed by considering both Temporal Difference (TD) error and connectivity trend. Thus, high-value transitions are more frequently selected to further enhance the training process. Finally, we conduct comprehensive simulation experiments and provide comparative results to substantiate the effectiveness and robustness of RL-CN algorithm.

Over the past decades, unmanned aerial vehicles (UAVs) have been widely used in both military and civilian fields. In these applications, flocking motion is a fundamental but crucial operation of multi-UAV systems. Traditional flocking motion methods usually designed for a specific environment. However, the real environment is mostly unknown and stochastic, which greatly reduces the practicality of these methods. In this article, deep reinforcement learning (DRL) is used to realize the flocking motion of multi-UAV systems. Considering that the sim-to-real problem restricts the application of DRL to the flocking motion scenario, a digital twin (DT)-enabled DRL training framework is proposed to solve this problem. The DRL model can learn from DT and be quickly deployed on the real-world UAV with the help of DT. Under this training framework, this article proposes an actor–critic DRL algorithm, named behavior-coupling deep deterministic policy gradient (BCDDPG), for the flocking motion problem, which is inspired by the flocking behavior of animals. Extensive simulations are conducted to evaluate the performance of BCDDPG. Simulation results show that BCDDPG achieves a higher average reward and performs better in terms of arrival rate and collision rate compared with the existing methods.

Radio frequency identification (RFID) has been widely used in various areas, such as logistics, healthcare, manufacture and so on. However, tag collision problem greatly affects the performance of RFID systems by reducing bandwidth utilization and increasing identification delay etc. In this paper, we propose a dynamic query tree anti-collision (DQTA) protocol, which dynamically adjusts the number of subgroups of each collision slot. Based on the number of consecutive colliding bits in tag response k, DQTA splits colliding tags into 2^k subgroups. Dividing colliding tags into more appropriate subgroups, the number of collision slots and transmitted message bits are effectively reduced. Comparing with the most related state-of-art works, the proposed DQTA protocol can identify tags with less time and fewer transmitting message bits.

Directional antennas have numerous advantages over omnidirectional antennas in CSMA/CA ad hoc networks. However, estimating the available bandwidth of a flow in the medium access process in such a network is very challenging. In this paper, we present a passive available bandwidth estimation algorithm for directional ad hoc networks, termed PABE-D. The estimation process consists of two phases, i.e., the preliminary and refined estimation phases. In the preliminary phase, the available transmission/reception duration in each beam of the node and the available bandwidth of the directional link are obtained by analyzing the available duration of both the sender and receiver. In the refined phase, we analyze the effect of the directional hidden terminal and deafness problems to further improve the estimate accuracy. The performance of our proposed PABE-D algorithm is evaluated in two typical topologies, i.e., the parallel and random grid topologies. The simulation results demonstrate that our algorithm can effectively estimate the available bandwidth in directional ad hoc networks.

Being able to provide a relatively easy and inexpensive way to collect data, portable readers have gained increasing popularity in wide-ranging RFID applications. In order to maximize the reader’s battery life, efficient tag identification protocols are of paramount importance in large-scale passive radio frequency identification (RFID) systems. This paper proposes a time- and energy-aware protocol based on$M$-ary collision tree (MCT) for efficient RFID tag identification. Thanks to Manchester encoding, the proposed MCT protocol recursively divides colliding tags into$M$subsets with at least two nonempty ones according to the information of$\log _2 M$colliding bits. Through the new MCT recognition process, MCT can effectively identify all the tags within its reading range. Theoretic analysis demonstrates that it takes the proposed MCT protocol fewer numbers of collision slots and message bits to identify all the tags, which reduces not only the identification time but also the energy cost. Simulation results are also presented to show that compared with other benchmark works, the proposed MCT protocol is able to reduce the average identification time and energy cost by at least 16.12% and 15.73%, respectively.

In a mobile RFID system, a large number of tags move in and out of the system continuously, so that the reader has very limited time to recognize all the tags. As a result, the effective and efficient identification of tags in mobile environments is a more challenging problem compared to conventional static RFID systems. In this paper, we propose an efficient bit-detecting (EBD) protocol to accelerate the reading process of large-scale mobile RFID systems. In these systems, some previously recognized tags, i.e., known tags, may stay in the reader's reading range for two consecutive reading cycles, and some unknown tags may newly participate in the current reading cycle. In the proposed EBD protocol, a new bit monitoring method is proposed to detect the presence of known tags using a small number of slots, and to retrieve their IDs from the back-end database. Next, an$M$-ary bit-detecting tree recognition method is proposed to rapidly recognize unknown tags without generating any idle slots. This new protocol is shown to perform better than existing methods reported in the literature. Both theoretic and simulation results are present to demonstrate that the proposed protocol is superior to existing protocols in terms of lower time cost.

Radio frequency identification (RFID) technology has been employed in wide-raging application domains. In most RFID applications, time-efficient identification of missing tags is one of the most fundamental objectives, especially for asset management and anti-theft purposes. In this paper, we propose a time-efficient pair-wise collision-resolving missing tag identification (PCMTI) protocol for large-scale RFID systems. In the protocol, two novel strategies, i.e., the pair-reply and two-collision slot (i.e., a slot with two exact tag responses) resolving strategies, are proposed. The pair-reply strategy can verify two tags in one short response slot simultaneously, while the two-collision slot resolving strategy further increases the number of tags verified in each frame. Both theoretical analysis and simulated results are presented to demonstrate the superiority of the proposed PCMTI protocol, which is capable of outperforming the state-of-the-art comparative protocols with at least a 30% reduction in average identification time for verifying one tag.

View synthesis prediction (VSP) is an important tool for improving the coding efficiency in the next generation three-dimensional (3-D) video systems. However, VSP will result in a new type of inter-view error propagation when the multi-view video plus depth (MVD) data are transmitted over the lossy networks. In this paper, this new type of error propagation is characterized and modeled. Firstly, a new analytic model is formulated to estimate the expected transmission distortion caused by error propagation from the synthesized reference view. Then, the compound impact of the transmission distortions of both the texture video and the depth map on the quality of the synthetic reference view is mathematically analysed. Our extensive simulation results demonstrate that the proposed transmission distortion model is very accurate.

The performance of RFID MAC protocols greatly influences RFID systems. Previous research work concentrates on reducing either the recognition time or the energy consumption of tags. In this work, we develop an efficient RFID MAC protocol which shows good performance in terms of both low energy consumption at tags and average recognition time to recognize one tag. We propose an efficient M-ary detecting tree RFID MAC protocol which uses a specially designed bit-detecting mechanism to recognize tags. Simulation results proves that the proposed protocol outperforms existing schemes in the literature in both energy consumption at the tag side and average recognition time.