A Simulation Way to Investigate the Reason for Congestion in Urban——A Case Study in Hohhot China

In the case of high density traffic flow, traditional traffic data statistical analysis methods, which not only have certain errors and lead to inaccurate data, but also have many limitations such as labor consumption, can no longer meet the demand for traffic analysis. Drones for traffic data, based on an aerial bird\u27s-eye view, no offset, and error-free complete statistics of urban road shooting section of all data, while greatly reducing cost consumption. A multi-dimensional simulation model is established for the UAV data to the Hohhot central urban area\u27s road simulation platform. This project will test and explore multidimensional data in the simulation platform to investigate the congestion problem in Hohhot\u27s central city, as well as motor vehicle driving characteristics, non-motor vehicle driving behavior, road setting design, and other aspects, and provide optimization solutions for data-driven intelligent traffic control and management.https://digitalcommons.odu.edu/gradposters2023_engineering/1005/thumbnail.jp

RMP: A Random Mask Pretrain Framework for Motion Prediction

As the pretraining technique is growing in popularity, little work has been done on pretrained learning-based motion prediction methods in autonomous driving. In this paper, we propose a framework to formalize the pretraining task for trajectory prediction of traffic participants. Within our framework, inspired by the random masked model in natural language processing (NLP) and computer vision (CV), objects' positions at random timesteps are masked and then filled in by the learned neural network (NN). By changing the mask profile, our framework can easily switch among a range of motion-related tasks. We show that our proposed pretraining framework is able to deal with noisy inputs and improves the motion prediction accuracy and miss rate, especially for objects occluded over time by evaluating it on Argoverse and NuScenes datasets.Comment: IEEE International Conference on Intelligent Transportation Systems (ITSC 2023

Truncated-ARQ aided adaptive network coding for cooperative two-way relaying networks: cross-layer design and analysis

Network Coding (NC) constitutes a promising technique of improving the throughput of relay-aided networks. In this context, we propose a cross-layer design for both amplifyand- forward (AF-) and decode-and-forward two-way relaying (DF-TWR) based on the NC technique invoked for improving the achievable throughput under specific Quality of Service (QoS) requirements, such as the maximum affordable delay and error rate.We intrinsically amalgamate adaptive Analog Network Coding (ANC) and Network Coded Modulation (NCM) with truncated Automatic Repeat reQuest (ARQ) operating at the different OSI layers. At the data-link layer, we design a pair of improved NC-based ARQ strategies based on the Stop-andwait and the Selective-repeat ARQ protocols. At the physical layer, adaptive ANC/NCM are invoked based on our approximate packet error ratio (PER). We demonstrate that the adaptive ANC design can be readily amalgamated with the proposed protocols. However, adaptive NC-QAM suffers from an SNR-loss, when the transmit rates of the pair of downlink (DL) channels spanning from the relay to the pair of destinations are different. Therefore we develop a novel transmission strategy for jointly selecting the optimal constellation sizes for both of the relay-to-destination links that have to be adapted to both pair of channel conditions. Finally, we analyze the attainable throughput, demonstrating that our truncated ARQ-aided adaptive ANC/NCM schemes attain considerable throughput gains over the schemes dispensing with ARQ, whilst our proposed scheme is capable of supporting bidirectional NC scenarios

Vibration Damping of Carbon Nanotube Assembly Materials

Vibration reduction is of great importance in various engineering applications, and a material that exhibits good vibration damping along with high strength and modulus has become more and more vital. Owing to the superior mechanical property of carbon nanotube (CNT), new types of vibration damping material can be developed. This paper presents recent advancements, including our progresses, in the development of high-damping macroscopic CNT assembly materials, such as forests, gels, films, and fibers. In these assemblies, structural deformation of CNTs, zipping and unzipping at CNT connection nodes, strengthening and welding of the nodes, and sliding between CNTs or CNT bundles are playing important roles in determining the viscoelasticity, and elasticity as well. Towards the damping enhancement, strategies for micro-structure and interface design are also discussed