Predicting vehicle trajectories at roundabouts is crucial for road safety, as it enables advanced driver-assistance systems (ADAS) and autonomous vehicles to anticipate and respond to other drivers' intentions effectively. This capability enhances situational awareness, reduces the risk of collisions, and contributes to smoother traffic flow. This paper presents an open-source dataset designed to predict vehicle turning intentions at roundabouts, integrating YOLOv8 for object detection and DeepSORT for multi-target tracking. The dataset includes vehicle timestamps, pixel coordinates and heading angles, utilizing monocular ranging to map vehicles to an actual coordinate system. It supports real-time collision prediction, driver alerts, and the detection of abnormal behaviors such as sudden lane changes or harsh braking. This dataset contributes to the development of safer and more efficient traffic management and autonomous driving systems. The dataset is available for public access on GitHub11Details of the roundabout video dataset can be found in: https://github.com/zhoudashi2016/Roundabout-Video-Dataset-for-ITS..

Roundabouts present unique challenges in intelligent transportation systems due to their complex geometry, dynamic interactions, and the limited availability of high-quality datasets. Existing methodologies typically lack contextual richness and perform inadequately when addressing the non-linear nature of roundabout behavior. This paper introduces a data augmentation framework that leverages large language models (LLMs), specifically a fine-tuned GPT-2, to enrich roundabout datasets with semantically meaningful behavioral patterns. Our approach initiates with extracting vehicle features from real-world video using YOLOv8 and DeepSORT, subsequently using a feedforward neural network (FNN) to extract three latent feature, and training GPT-2 on this corpus to generate high-level behavioral labels, thereby semantically enriching the dataset's diversity and semantic depth. To validate the effectiveness of our framework, we train a long short-term memory (LSTM) model for trajectory prediction. Evaluation with an LSTM predictor shows that models trained on synthetic GPT-generated data outperform those using original data, with lower average relative error (1.46% vs. 1.72%) and improved accuracy at the 100th percentile (62% vs. 54%). Experimental results show that our augmented synthetic dataset is capable of improving the robustness of prediction, establishing a new paradigm for intelligent traffic modeling through the integration of foundational models. Our code is available at https://github.com/Rebecca689/llm-roundabout.

One of the most significant technical research challenges of space-air-ground-sea integrated networks is to realize satisfactory wireless data transmission in underwater environments. However, although numerous types of communications, e.g., radio frequency, optical and acoustic, have been investigated, the performance of wireless underwater communications still cannot satisfy the ever-increasing high data rate and long communication distance requirements. This paper demonstrates an early attempt regarding performance analysis of acoustic intelligent reflecting surface (RIS)-assisted wireless underwater communication. The minimization of transmission power is formulated subject to a list of constraints. The analytic scheme is proposed, where two key system parameters, e.g., reflection angle and acoustic signal frequency, are investigated. Numerical results verify that acoustic RIS-assisted wireless underwater communication can significantly enhance signal transmission quality and decrease interference. The results obtained in this paper can be applied to future autonomous underwater vehicles and robot system designs.

In recent years, the convergence of driver monitoring systems (DMS), cloud technologies, and self-driving cars has gained increasing attention. Driver monitoring systems use a variety of camera and sensor technologies to detect the driver's state in real time, including fatigue levels, and stress levels based on emotional state. Such systems help to improve driving safety and reduce accidents caused by driver fatigue or emotional high pressure. This paper proposes an in-vehicle driver emotion recognition cloud computing scheme. Contact sensors are used in conjunction with a steering wheel to collect the electrical skin signals from the driver's palm and transmit the data to a cloud server via a 6G IoT device. The driver's emotional state such as fatigue, alertness, and stress level is measured by One-Class Support Vector Machines (OCSVM) combined with a Long Short-Term Memory (LSTM) recurrent neural network. When low mood, drowsiness, or lack of alertness are recognized, the driver is reminded and given feedback on safe driving through methods such as seat vibration and voice prompts. And the vehicle's automatic driving assistance mode will be turned on in extreme situations. The proposed method combines unsupervised learning with threshold-based wavelet denoising, effectively removing noise data generated by motion during the collection of human electrodermal signals. Experimental results demonstrate that the motion artifact removal algorithm presented in this paper exhibits superior denoising effects. Compared to traditional filtering algorithms, the SNR(Signal to Noise Ratio) is enhanced by 4.819 dB, while the RMSE(Root Mean Square Error) is reduced by 0.0385. Ultimately, the accuracy is improved by 2.44% compared to conventional emotion recognition methods.

Unmanned surface vehicles (USVs) are becoming increasingly significant in fulfilling integrated sensing, computing, and communication with the emergence of bidirectional computation tasks. However, Quality-of-Service provisioning is still challenging since USVs are restricted with limited onboard resources and direct links between them and shore-based terrestrial base stations (TBSs) are frequently blocked. This article proposes a novel reconfigurable intelligent surface (RIS)-assisted cooperative unmanned aerial vehicle (UAV)–USV mobile-edge computing (MEC) network architecture, where RIS-mounted tethered UAV (TUAV) and rotary-wing UAVs (RUAVs) are collaboratively utilized to serve USVs. RUAVs energy minimization is formulated by jointly considering TUAV hovering altitude, RIS phase-shift vector, RUAV service selection indicator, and RUAVs turning points. A heuristic solution is proposed to tackle the formulated problem, where the original problem is first decoupled into three subproblems, e.g., the joint optimization of RIS phase-shift vector and TUAV hovering altitude subproblem, RUAVs service selection indicator subproblem, and RUAVs turning points subproblem, each of which is solved by the proposed modified alternative direction method of multiplier (ADMM) algorithm, the proposed enhanced simulated annealing (ESA) algorithm and the proposed successive convex approximation (SCA)-based algorithm. In this way, the challenging problem can be efficiently solved iteratively. The results show that the proposed solution can decrease RUAVs energy consumption by nearly 29% compared to numerous selected advanced algorithms. Moreover, the performance of the proposed solution regarding typical penalty coefficients and number of RIS reflecting elements is investigated.

Unmanned Surface Vehicles (USVs) in inland waterways have drawn increasing attention for their excellent capability to serve maritime time-consuming missions such as autonomous navigation and intelligent monitoring. However, USVs struggle to accomplish emerging computation-intensive tasks (e.g., sensor, telemetry, etc) timely due to the limited on-board resources. This paper proposes a novel reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicle (UAV) multi-access edge computing (MEC) network architecture to support low-latency USVs data computation with time window. Aiming to enhance USVs task processing efficiency, the minimization of USVs task processing time is formulated by jointly considering UAVs flight route selection, USVs execution mode selection, UAVs hovering coordinates and RIS phase shift vector. A heuristic solution is proposed to tackle the formulated challenging problem iteratively. The original problem is decoupled into three subproblems: an enhanced deferred acceptance algorithm is proposed to solve UAVs flight route selection subproblem; an enhanced Lagrangian relaxation method is proposed to solve USVs execution mode selection subproblem; a joint alternating direction method of multipliers (ADMM)-successive convex approximation (SCA)-based algorithm is proposed to solve UAVs hovering coordinates subproblem. Experiment results demonstrate that the proposed solution can decrease task processing time by approximately 54% compared with numerous selected advanced algorithms. Moreover, the performance of the proposed solution under typical UAVs caching capability and the number of UAVs has been investigated.

SummaryThe mobile video quality can be improved by video super-resolution (SR) especially when bandwidth is limited. To achieve real-time SR, the latest work, ClassSR (CVPR 19), divides frames into equal-size image blocks (IBs), and different-complexity SR models are used respectively to reduce the computational burden.

360-degree video streaming has gained significant popularity and has been employed in a variety of autonomous driving scenarios such as road traffic awareness and interactions between autonomous vehicles and pedestrians. However, maintaining an appropriate buffer size poses a major challenge for smooth playback and a high Quality of Experience (QoE). In this paper, we propose an adaptive buffer management scheme (BMQoE) that dynamically computes the optimal video bitrate based on the current buffer occupancy and available bandwidth. The proposed BMQoE scheme aims to improve QoE and bandwidth utilization by considering the client-side field of interest, bandwidth conditions, and buffer sizes. Experimental results show that the BMQoE scheme significantly reduces the duration of video stalls and improves the QoE of 360-degree video streaming by 16.7%, which is crucial for autonomous vehicles.

This paper proposes an intelligent reflecting surface (IRS)-assisted wireless inland ship multi-access edge computing (MEC) network architecture with time windows, where UAV is deployed to serve time-constrained unmanned surface vehicles (USVs) with IRS. The task execution efficiency maximization optimization problem is formulated by joint considering IRS phase-shift vector, UAVs hovering coordinates and the task execution indicator. To tackle the formulated challenging problem, a heuristic solution is proposed. First, an enhanced differential evolution algorithm is proposed to optimize UAVs hovering coordinates. Moreover, IRS phase-shift vector and task execution indicator are jointly optimized in an iterative manner by the proposed modified deferred acceptance algorithm. Numerical results verify the effectiveness of the proposed algorithm in comparison with some selected advanced algorithms in terms of task execution efficiency.

Non-terrestrial wireless communications have evolved into a technology enabler for seamless connectivity and ubiquitous computing services in the beyond fifth-generation (B5G) and sixth-generation (6G) networks, aiming to provision reliable and energy efficient communications among aerial platforms and ground mobile users. This paper considers intelligent reflecting surface (IRS)-assisted unmanned aerial vehicle (UAV)-empowered wireless communication, which exploits both the high mobility of UAV and passive beamforming gain brought by IRS. The energy minimization of rotary-wing UAV is formulated by jointly considering numerous quality of service (QoS) constraints with intricately coupled variables. To tackle the formulated challenging problem, a heuristic algorithm is proposed. First, we decouple it into several subproblems. Moreover, we jointly investigate offloading decisions of Internet of Thing (IoT) devices by the proposed enhanced differential evolution algorithm. Then, minorization-maximization algorithm (MMA) is utilized to solve the optimization of IRS phase shift-vector. Moreover, ant colony optimization (ACO) algorithm is proposed to optimize UAV flight route indicator matrix. Numerical results validate the effectiveness of the proposed algorithm. The results show that the proposed solution can remarkably decrease UAV flight distance while improving the network energy efficiency in comparison with numerous advanced algorithms.

In this paper, we present an experimental evaluation of the recently standardized Opus codec used in a VoIP context. Opus operates in both narrow and wideband modes, similar to Adaptive Multi-Rate (AMR). Through the use of the Wideband Perceptual Evaluation of Speech Quality (WB-PESQ) metric, we have conducted an extensive set of experiments using multiple audio samples encoded at different bit-rates, to investigate the impact of packet loss on resulting speech quality. Using these results, fitting functions for each bit-rate were computed to provide a straightforward manner of evaluating speech quality when given a specified packet loss rate. Using ns-2, a simulation analysis was conducted to evaluate the effect of background traffic on transmitted Opus streams. We observed that, when using different levels of background traffic, the observed packet loss rates varied heavily depending on the stream bit-rate. By correlating this information with the fitting functions derived previously, we were able to define switching thresholds. These are points where the speech quality of a lower bit-rate stream is greater than that of a higher bit-rate stream for the same levels of link bandwidth saturation.