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This paper addresses the traffic management problem for autonomous vehicles at intersections without traffic signals. In the current system, a road junction has no traffic signals when the traffic volume is low to medium. Installing infrastructure at each unsignalled crossing to coordinate autonomous cars can be formidable. We propose a novel decentralized strategy where the vehicles use a harmony matrix to find the best possible combination of the cars to cross the intersection without any crashes. We formulate a maximal clique problem using harmony matrix that maximizes the intersection throughput. This algorithm does not require communication between the vehicles. We compared our work with state-of-the-art communicative strategies and widely used traditional and modern methods for intersection management. Through extensive simulation, we showed that our algorithm is comparable to state-of-the-art and outperforms traditional methods.

Previously, non-autoregressive models were widely perceived as being superior in generation efficiency but inferior in generation quality due to the difficulties of modeling multiple target modalities. To enhance the multi-modality modeling ability, we propose the diffusion glancing transformer, which employs a modality diffusion process and residual glancing sampling. The modality diffusion process is a discrete process that interpolates the multi-modal distribution along the decoding steps, and the residual glancing sampling approach guides the model to continuously learn the remaining modalities across the layers. Experimental results on various machine translation and text generation benchmarks demonstrate that DIFFGLAT achieves better generation accuracy while maintaining fast decoding speed compared with both autoregressive and non-autoregressive models.

Cellular vehicular-to-everything (C-V2X) systems offer the potential for improving road safety, in part through the exchange of periodic basic safety messages (BSMs) between nearby vehicles. The reliability and latency of these messages is a key metric. Hybrid automatic repeat request (HARQ) retransmissions are one technique used to this end. However, HARQ may come at the expense of consuming the limited available wireless resources, especially in highly congested scenarios. This paper studies BSM transmission latency and reliability when HARQ retransmissions are used with the semi-persistent scheduling (SPS) in C-V2X transmission mode 4. We do so through extensive system-level simulations that closely follow the SPS process. Furthermore, we provide an analytical model for the tail behavior of the BSM latency distribution with HARQ retransmissions that is a good approximation to the simulation results. Our study reveals the impact of several deployment settings (e.g., bandwidth configurations and vehicle density).

Lane detection is a vital task for vehicles to navigate and localize their position on the road. To ensure reliable results, lane detection algorithms must have robust generalization performance in various road environments. However, despite the significant performance improvement of deep learning-based lane detection algorithms, their generalization performance in response to changes in road environments still falls short of expectations. In this paper, we present a novel framework for single-source domain generalization (SSDG) in lane detection. By decomposing data into lane structures and surroundings, we enhance diversity using High-Definition (HD) maps and generative models. Rather than expanding data volume, we strategically select a core subset of data, maximizing diversity and optimizing performance. Our extensive experiments demonstrate that our framework enhances the generalization performance of lane detection, comparable to the domain adaptation-based method.

We propose a diarization system, that estimates "who spoke when" based on spatial information, to be used as a front-end of a meeting transcription system running on the signals gathered from an acoustic sensor network (ASN). Although the spatial distribution of the microphones is advantageous, exploiting the spatial diversity for diarization and signal enhancement is challenging, because the microphones' positions are typically unknown, and the recorded signals are initially unsynchronized in general. Here, we approach these issues by first blindly synchronizing the signals and then estimating time differences of arrival (TDOAs). The TDOA information is exploited to estimate the speakers' activity, even in the presence of multiple speakers being simultaneously active. This speaker activity information serves as a guide for a spatial mixture model, on which basis the individual speaker's signals are extracted via beamforming. Finally, the extracted signals are forwarded to a speech recognizer. Additionally, a novel initialization scheme for spatial mixture models based on the TDOA estimates is proposed. Experiments conducted on real recordings from the LibriWASN data set have shown that our proposed system is advantageous compared to a system using a spatial mixture model, which does not make use of external diarization information.

While significant advancements have been made in the mechanical and task-specific controller designs of powered transfemoral prostheses, developing a task-adaptive control framework that generalizes across various locomotion modes and terrain conditions remains an open problem. This study proposes a task-adaptive learning quasi-stiffness control framework for powered prostheses that generalizes across tasks, including the torque-angle relationship reconstruction part and the quasi-stiffness controller design part. Quasi-stiffness is defined as the slope of the human joint's torque-angle relationship. To accurately obtain the torque-angle relationship in a new task, a Gaussian Process Regression (GPR) model is introduced to predict the target features of the human joint's angle and torque in the task. Then a Kernelized Movement Primitives (KMP) is employed to reconstruct the torque-angle relationship of a new task from multiple human demonstrations and estimated target features. Based on the torque-angle relationship of the new task, a quasi-stiffness control approach is designed for a powered prosthesis. Finally, the proposed framework is validated through practical examples, including varying speed and incline walking tasks. The proposed framework has the potential to expand to variable walking tasks in daily life for the transfemoral amputees.

Track systems effectively distribute loads, augmenting traction and maneuverability on unstable terrains, leveraging their expansive contact areas. This tracked locomotion capability also aids in hand manipulation of not only regular objects but also irregular objects. In this study, we present the design of a soft robotic finger with an active surface on an omni-adaptive network structure, which can be easily installed on existing grippers and achieve stability and dexterity for in-hand manipulation. The system's active surfaces initially transfer the object from the fingertip segment with less compliance to the middle segment of the finger with superior adaptability. Despite the omni-directional deformation of the finger, in-hand manipulation can still be executed with controlled active surfaces. We characterized the soft finger's stiffness distribution and simplified models to assess the feasibility of repositioning and reorienting a grasped object. A set of experiments on in-hand manipulation was performed with the proposed fingers, demonstrating the dexterity and robustness of the strategy.

The advent of autonomous vehicle technologies has significantly impacted various sectors, including motorsport, where Formula Student and Formula: Society of Automotive Engineers introduced autonomous racing classes. These offer new challenges to aspiring engineers, including the team at QUT Motorsport, but also raise the entry barrier due to the complexity of high-speed navigation and control. This paper presents an open-source solution using the Robot Operating System 2, specifically its open-source navigation stack, to address these challenges in autonomous Formula Student race cars. We compare off-the-shelf navigation libraries that this stack comprises of against traditional custom-made programs developed by QUT Motorsport to evaluate their applicability in autonomous racing scenarios and integrate them onto an autonomous race car. Our contributions include quantitative and qualitative comparisons of these packages against traditional navigation solutions, aiming to lower the entry barrier for autonomous racing. This paper also serves as a comprehensive tutorial for teams participating in similar racing disciplines and other autonomous mobile robot applications.

Accounting for the increased concern for public safety, automatic abnormal event detection and recognition in a surveillance scene is crucial. It is a current open study subject because of its intricacy and utility. The identification of aberrant events automatically, it's a difficult undertaking because everyone's idea of abnormality is different. A typical occurrence in one circumstance could be seen as aberrant in another. Automatic anomaly identification becomes particularly challenging in the surveillance footage with a large crowd due to congestion and high occlusion. With the use of machine learning techniques, this thesis study aims to offer the solution for this use case so that human resources won't be required to keep an eye out for any unusual activity in the surveillance system records. We have developed a novel generative adversarial network (GAN) based anomaly detection model. This model is trained such that it learns together about constructing a high dimensional picture space and determining the latent space from the video's context. The generator uses a residual Autoencoder architecture made up of a multi-stage channel attention-based decoder and a two-stream, deep convolutional encoder that can realise both spatial and temporal data. We have also offered a technique for refining the GAN model that reduces training time while also generalising the model by utilising transfer learning between datasets. Using a variety of assessment measures, we compare our model to the current state-of-the-art techniques on four benchmark datasets. The empirical findings indicate that, in comparison to existing techniques, our network performs favourably on all datasets.

Graph Convolutional Network (GCN) has been widely applied in transportation demand prediction due to its excellent ability to capture non-Euclidean spatial dependence among station-level or regional transportation demands. However, in most of the existing research, the graph convolution was implemented on a heuristically generated adjacency matrix, which could neither reflect the real spatial relationships of stations accurately, nor capture the multi-level spatial dependence of demands adaptively. To cope with the above problems, this paper provides a novel graph convolutional network for transportation demand prediction. Firstly, a novel graph convolution architecture is proposed, which has different adjacency matrices in different layers and all the adjacency matrices are self-learned during the training process. Secondly, a layer-wise coupling mechanism is provided, which associates the upper-level adjacency matrix with the lower-level one. It also reduces the scale of parameters in our model. Lastly, a unitary network is constructed to give the final prediction result by integrating the hidden spatial states with gated recurrent unit, which could capture the multi-level spatial dependence and temporal dynamics simultaneously. Experiments have been conducted on two real-world datasets, NYC Citi Bike and NYC Taxi, and the results demonstrate the superiority of our model over the state-of-the-art ones.