arXiv subjects

Pei Li

Publications and source records attributed to Pei Li.

At least 55 records · Page 3Linked to original sources

arXiv2024

Enhancing Pedestrian Trajectory Prediction with Crowd Trip Information

Pedestrian trajectory prediction is essential for various applications in active traffic management, urban planning, traffic control, crowd management, and autonomous driving, aiming to enhance traffic safety and efficiency. Accurately predicting pedestrian trajectories requires a deep understanding of individual behaviors, social interactions, and road environments. Existing studies have developed various models to capture the influence of social interactions and road conditions on pedestrian trajectories. However, these approaches are limited by the lack of a comprehensive view of social interactions and road environments. To address these limitations and enhance the accuracy of pedestrian trajectory prediction, we propose a novel approach incorporating trip information as a new modality into pedestrian trajectory models. We propose RNTransformer, a generic model that utilizes crowd trip information to capture global information on social interactions. We incorporated RNTransformer with various socially aware local pedestrian trajectory prediction models to demonstrate its performance. Specifically, by leveraging a pre-trained RNTransformer when training different pedestrian trajectory prediction models, we observed improvements in performance metrics: a 1.3/2.2% enhancement in ADE/FDE on Social-LSTM, a 6.5/28.4% improvement on Social-STGCNN, and an 8.6/4.3% improvement on S-Implicit. Evaluation results demonstrate that RNTransformer significantly enhances the accuracy of various pedestrian trajectory prediction models across multiple datasets. Further investigation reveals that the RNTransformer effectively guides local models to more accurate directions due to the consideration of global information. By exploring crowd behavior within the road network, our approach shows great promise in improving pedestrian safety through accurate trajectory predictions.

cs.CV↗

arXiv2024

Three-layer deep learning network random trees for fault detection in chemical production process

With the development of technology, the chemical production process is becoming increasingly complex and large-scale, making fault detection particularly important. However, current detective methods struggle to address the complexities of large-scale production processes. In this paper, we integrate the strengths of deep learning and machine learning technologies, combining the advantages of bidirectional long and short-term memory neural networks, fully connected neural networks, and the extra trees algorithm to propose a novel fault detection model named three-layer deep learning network random trees (TDLN-trees). First, the deep learning component extracts temporal features from industrial data, combining and transforming them into a higher-level data representation. Second, the machine learning component processes and classifies the features extracted in the first step. An experimental analysis based on the Tennessee Eastman process verifies the superiority of the proposed method.

cs.LG↗

arXiv2024

Demystifying the Physics of Deep Reinforcement Learning-Based Autonomous Vehicle Decision-Making

With the advent of universal function approximators in the domain of reinforcement learning, the number of practical applications leveraging deep reinforcement learning (DRL) has exploded. Decision-making in autonomous vehicles (AVs) has emerged as a chief application among them, taking the sensor data or the higher-order kinematic variables as the input and providing a discrete choice or continuous control output. There has been a continuous effort to understand the black-box nature of the DRL models, but so far, there hasn't been any discussion (to the best of authors' knowledge) about how the models learn the physical process. This presents an overwhelming limitation that restricts the real-world deployment of DRL in AVs. Therefore, in this research work, we try to decode the knowledge learnt by the attention-based DRL framework about the physical process. We use a continuous proximal policy optimization-based DRL algorithm as the baseline model and add a multi-head attention framework in an open-source AV simulation environment. We provide some analytical techniques for discussing the interpretability of the trained models in terms of explainability and causality for spatial and temporal correlations. We show that the weights in the first head encode the positions of the neighboring vehicles while the second head focuses on the leader vehicle exclusively. Also, the ego vehicle's action is causally dependent on the vehicles in the target lane spatially and temporally. Through these findings, we reliably show that these techniques can help practitioners decipher the results of the DRL algorithms.

cs.RO↗

arXiv2024

Wide-bandgap semiconductor of three-dimensional unconventional stoichiometric NaCl2 crystal

The expanding applications call for novel new-generation wide-bandgap semiconductors. Here, we show that a compound only composed of the ordinary elements Na and Cl, namely three-dimensional NaCl2 crystal, is a wide-bandgap semiconductor. This finding benefits from the breaking of conventional stoichiometry frameworks in the theoretical design, leading to the discovery of three-dimensional XY2 (X = Na, Li, K; Y = Cl, F, Br, I) crystals, with covalent bonds of Y pairs inducing the wide bandgap from 2.24 to 4.45 eV. Crucially, such an unexpected NaCl2 crystal was successfully synthesized under ambient conditions. The unconventional stoichiometric strategy with other chemical elements potentially yields more wide-bandgap semiconductors, offering the capability for bandgap tuning. These unconventional stoichiometric materials may also exhibit superconductivity, transparent inorganic electrides, high-energy-density, and beyond.

cond-mat.mtrl-sci↗

arXiv2024

Using physics-based simulation towards eliminating empiricism in extraterrestrial terramechanics applications

Recently, there has been a surge of international interest in extraterrestrial exploration targeting the Moon, Mars, the moons of Mars, and various asteroids. This contribution discusses how current state-of-the-art Earth-based testing for designing rovers and landers for these missions currently leads to overly optimistic conclusions about the behavior of these devices upon deployment on the targeted celestial bodies. The key misconception is that gravitational offset is necessary during the \textit{terramechanics} testing of rover and lander prototypes on Earth. The body of evidence supporting our argument is tied to a small number of studies conducted during parabolic flights and insights derived from newly revised scaling laws. We argue that what has prevented the community from fully diagnosing the problem at hand is the absence of effective physics-based models capable of simulating terramechanics under low gravity conditions. We developed such a physics-based simulator and utilized it to gauge the mobility of early prototypes of the Volatiles Investigating Polar Exploration Rover (VIPER), which is slated to depart for the Moon in November 2024. This contribution discusses the results generated by this simulator, how they correlate with physical test results from the NASA-Glenn SLOPE lab, and the fallacy of the gravitational offset in rover and lander testing. The simulator developed is open sourced and made publicly available for unfettered use; it can support principled studies that extend beyond trafficability analysis to provide insights into in-situ resource utilization activities, e.g., digging, bulldozing, and berming in low gravity.

astro-ph.IM↗

arXiv2024

Transmission Line Detection Based on Improved Hough Transform

To address the challenges of low detection accuracy and high false positive rates of transmission lines in UAV (Unmanned Aerial Vehicle) images, we explore the linear features and spatial distribution. We introduce an enhanced stochastic Hough transform technique tailored for detecting transmission lines in complex backgrounds. By employing the Hessian matrix for initial preprocessing of transmission lines, and utilizing boundary search and pixel row segmentation, our approach distinguishes transmission line areas from the background. We significantly reduce both false positives and missed detections, thereby improving the accuracy of transmission line identification. Experiments demonstrate that our method not only processes images more rapidly, but also yields superior detection results compared to conventional and random Hough transform methods.

cs.CV↗

arXiv2024

Ultrasensitive piezoelectric sensor based on two-dimensional Na2Cl crystals with periodic atom vacancies

Pursuing ultrasensitivity of pressure sensors has been a long-standing goal. Here, we report a piezoelectric sensor that exhibits supreme pressure-sensing performance, including a peak sensitivity up to 3.5*10^6 kPa^-1 in the pressure range of 1-100 mPa and a detection limit of less than 1 mPa, superior to the current state-of-the-art pressure sensors. These properties are attributed to the high percentage of periodic atom vacancies in the two-dimensional Na2Cl crystals formed within multilayered graphene oxide membrane in the sensor, which provides giant polarization with high stability. The sensor can even clearly detect the airflow fluctuations surrounding a flapping butterfly, which have long been the elusive tiny signals in the famous "butterfly effect". The finding represents a step towards next-generation pressure sensors for various precision applications.

physics.app-ph↗

arXiv2023

Solid state defect emitters with no electrical activity

Point defects may introduce defect levels into the fundamental band gap of the host semiconductors that alter the electrical properties of the material. As a consequence, the in-gap defect levels and states automatically lower the threshold energy of optical excitation associated with the optical gap of the host semiconductor. It is, therefore, a common assumption that solid state defect emitters in semiconductors ultimately alter the conductivity of the host. Here we demonstrate on a particular defect in 4H silicon carbide that a yet unrecognized class of point defects exists which are optically active but electrically inactive in the ground state.

cond-mat.mtrl-sci↗

arXiv2023

PFL-LSTR: A privacy-preserving framework for driver intention inference based on in-vehicle and out-vehicle information

Intelligent vehicle anticipation of the movement intentions of other drivers can reduce collisions. Typically, when a human driver of another vehicle (referred to as the target vehicle) engages in specific behaviors such as checking the rearview mirror prior to lane change, a valuable clue is therein provided on the intentions of the target vehicle's driver. Furthermore, the target driver's intentions can be influenced and shaped by their driving environment. For example, if the target vehicle is too close to a leading vehicle, it may renege the lane change decision. On the other hand, a following vehicle in the target lane is too close to the target vehicle could lead to its reversal of the decision to change lanes. Knowledge of such intentions of all vehicles in a traffic stream can help enhance traffic safety. Unfortunately, such information is often captured in the form of images/videos. Utilization of personally identifiable data to train a general model could violate user privacy. Federated Learning (FL) is a promising tool to resolve this conundrum. FL efficiently trains models without exposing the underlying data. This paper introduces a Personalized Federated Learning (PFL) model embedded a long short-term transformer (LSTR) framework. The framework predicts drivers' intentions by leveraging in-vehicle videos (of driver movement, gestures, and expressions) and out-of-vehicle videos (of the vehicle's surroundings - frontal/rear areas). The proposed PFL-LSTR framework is trained and tested through real-world driving data collected from human drivers at Interstate 65 in Indiana. The results suggest that the PFL-LSTR exhibits high adaptability and high precision, and that out-of-vehicle information (particularly, the driver's rear-mirror viewing actions) is important because it helps reduce false positives and thereby enhances the precision of driver intention inference.

cs.RO↗

arXiv2023

Carbon cluster emitters in silicon carbide

Defect qubits in 4H-SiC are outstanding candidates for numerous applications in the rapidly emerging field of quantum technology. Carbon clusters can act as emission sources that may appear after thermal oxidation of 4H-SiC or during irradiation which kicks out carbon atoms from their sites. These fluorescent carbon clusters could interfere with the already established vacancy-related qubits that generated with irradiation techniques. In this study, we systematically investigate the electronic structure, formation energy, dissociation energy, vibrational properties, and the full fluorescence spectrum of carbon clusters involving up to four carbon atoms in 4H-SiC by means of density functional theory calculations. All the possible local configurations for these carbon clusters are carefully evaluated. We find the electronic and vibronic properties of the carbon clusters depend strongly on the local configuration of the 4H-SiC lattice. By comparing the calculated and previously observed fluorescence spectra in 4H-SiC, we identify several carbon clusters as stable visible emitters in 4H-SiC. The paired carbon interstitial defects are identified as the source of the 463-nm triplet and the 456.6-nm emitters. The 471.8-nm emitter in 4H-SiC is associated with tri-carbon antisite clusters. Our findings provide plausible explanation for the origin of visible emission lines in 4H-SiC and propose the possible configurations of carbon clusters which are helpful for the quantum information processing application through qubits in 4H-SiC.

quant-ph↗

arXiv2023

Exceptionally strong coupling of defect emission in hexagonal boron nitride to stacking sequences

Van der Waals structures present a unique opportunity for tailoring material interfaces and integrating photonic functionalities. By precisely manipulating the twist angle and stacking sequences, it is possible to elegantly tune and functionalize the electronic and optical properties of layered van der Waals structures. Among these materials, two-dimensional hexagonal boron nitride (hBN) stands out for its remarkable optical properties and wide band gap, making it a promising host for solid state single photon emitters at room temperature. Previous investigations have demonstrated the observation of bright single photon emission in hBN across a wide range of wavelengths. In this study, we unveil an application of van der Waals technology in modulating their spectral shapes and brightness by carefully controlling the stacking sequences and polytypes. Our theoretical analysis reveals remarkably large variations in the Huang-Rhys factors-an indicator of the interaction between a defect and its surrounding lattice-reaching up to a factor of 3.3 for the same defect in different stackings. We provide insights into the underlying mechanism behind these variations, shedding light on the design principles necessary to achieve rational and precise control of defect emission. This work paves the way for enhancing defect identification and facilitating the engineering of highly efficient single photon sources and qubits using van der Waals materials.

cond-mat.mes-hall↗

arXiv2022

FedBA: Non-IID Federated Learning Framework in UAV Networks

With the development and progress of science and technology, the Internet of Things(IoT) has gradually entered people's lives, bringing great convenience to our lives and improving people's work efficiency. Specifically, the IoT can replace humans in jobs that they cannot perform. As a new type of IoT vehicle, the current status and trend of research on Unmanned Aerial Vehicle(UAV) is gratifying, and the development prospect is very promising. However, privacy and communication are still very serious issues in drone applications. This is because most drones still use centralized cloud-based data processing, which may lead to leakage of data collected by drones. At the same time, the large amount of data collected by drones may incur greater communication overhead when transferred to the cloud. Federated learning as a means of privacy protection can effectively solve the above two problems. However, federated learning when applied to UAV networks also needs to consider the heterogeneity of data, which is caused by regional differences in UAV regulation. In response, this paper proposes a new algorithm FedBA to optimize the global model and solves the data heterogeneity problem. In addition, we apply the algorithm to some real datasets, and the experimental results show that the algorithm outperforms other algorithms and improves the accuracy of the local model for UAVs.

cs.LG↗

arXiv2022

Ultrafast ion sieving in two dimensional graphene oxide membranes

Ultrahigh water permeance, together with a high rejection rate through nanofiltration and separation membranes1,2, is crucial but still challenging for multivalent ion sieving in water treatment processes of desalination, separation, and purification3,4. To date, no theory or equation has ever been quantitatively clarified the mechanism of water permeance in two-dimensional (2D) membranes, despite intensive and prolonged searches. Here, we established a new general equation of permeation through 2D membranes, and experimentally achieved unprecedented advances in water permeance one to two orders of magnitude higher than state-of-the-art membranes while simultaneously maintaining high ion rejection rates for multivalent metal ions, by staking nano-sized reduced graphene oxide (nano-rGO) flakes into nanofiltration membranes. The equation is simply based on a fundamental steady-state flow assumption and provides an essential description of water permeance through 2D membranes, demonstrating that the ultrahigh water permeance is attributed to the high effective channel area and shortened channel length elicited from the nano-sized-flake stacking effects in nano-rGO membranes, consistent with our theoretical simulations and previous experiments. These results pave the way for fabrication of advanced 2D nanofiltration membranes to realize a breakthrough in water permeance with exceptional ion sieving performance.

physics.flu-dyn↗

arXiv2022

A Probabilistic Framework for Estimating the Risk of Pedestrian-Vehicle Conflicts at Intersections

Pedestrian safety has become an important research topic among various studies due to the increased number of pedestrian-involved crashes. To evaluate pedestrian safety proactively, surrogate safety measures (SSMs) have been widely used in traffic conflict-based studies as they do not require historical crashes as inputs. However, most existing SSMs were developed based on the assumption that road users would maintain constant velocity and direction. Risk estimations based on this assumption are less unstable, more likely to be exaggerated, and unable to capture the evasive maneuvers of drivers. Considering the limitations among existing SSMs, this study proposes a probabilistic framework for estimating the risk of pedestrian-vehicle conflicts at intersections. The proposed framework loosen restrictions of constant speed by predicting trajectories using a Gaussian Process Regression and accounts for the different possible driver maneuvers with a Random Forest model. Real-world LiDAR data collected at an intersection was used to evaluate the performance of the proposed framework. The newly developed framework is able to identify all pedestrian-vehicle conflicts. Compared to the Time-to-Collision, the proposed framework provides a more stable risk estimation and captures the evasive maneuvers of vehicles. Moreover, the proposed framework does not require expensive computation resources, which makes it an ideal choice for real-time proactive pedestrian safety solutions at intersections.

cs.LG↗

arXiv2022

6G-enabled Edge AI for Metaverse: Challenges, Methods, and Future Research Directions

6G-enabled edge intelligence opens up a new era of Internet of Everything and makes it possible to interconnect people-devices-cloud anytime, anywhere. More and more next-generation wireless network smart service applications are changing our way of life and improving our quality of life. As the hottest new form of next-generation Internet applications, Metaverse is striving to connect billions of users and create a shared world where virtual and reality merge. However, limited by resources, computing power, and sensory devices, Metaverse is still far from realizing its full vision of immersion, materialization, and interoperability. To this end, this survey aims to realize this vision through the organic integration of 6G-enabled edge AI and Metaverse. Specifically, we first introduce three new types of edge-Metaverse architectures that use 6G-enabled edge AI to solve resource and computing constraints in Metaverse. Then we summarize technical challenges that these architectures face in Metaverse and the existing solutions. Furthermore, we explore how the edge-Metaverse architecture technology helps Metaverse to interact and share digital data. Finally, we discuss future research directions to realize the true vision of Metaverse with 6G-enabled edge AI.

cs.NI↗

arXiv2022

Graph Neural Network-Based Scheduling for Multi-UAV-Enabled Communications in D2D Networks

In this paper, we jointly design the power control and position dispatch for Multi-unmanned aerial vehicle (UAV)-enabled communication in device-to-device (D2D) networks. Our objective is to maximize the total transmission rate of downlink users (DUs). Meanwhile, the quality of service (QoS) of all D2D users must be satisfied. We comprehensively considered the interference among D2D communications and downlink transmissions. The original problem is strongly non-convex, which requires high computational complexity for traditional optimization methods. And to make matters worse, the results are not necessarily globally optimal. In this paper, we propose a novel graph neural networks (GNN) based approach that can map the considered system into a specific graph structure and achieve the optimal solution in a low complexity manner. Particularly, we first construct a GNN-based model for the proposed network, in which the transmission links and interference links are formulated as vertexes and edges, respectively. Then, by taking the channel state information and the coordinates of ground users as the inputs, as well as the location of UAVs and the transmission power of all transmitters as outputs, we obtain the mapping from inputs to outputs through training the parameters of GNN. Simulation results verified that the way to maximize the total transmission rate of DUs can be extracted effectively via the training on samples. Moreover, it also shows that the performance of proposed GNN-based method is better than that of traditional means.

eess.SY↗

arXiv2021

Task-Based Graph Signal Compression

Graph signals arise in various applications, ranging from sensor networks to social media data. The high-dimensional nature of these signals implies that they often need to be compressed in order to be stored and transmitted. The common framework for graph signal compression is based on sampling, resulting in a set of continuous-amplitude samples, which in turn have to be quantized into a finite bit representation. In this work we study the joint design of graph signal sampling along with quantization, for graph signal compression. We focus on bandlimited graph signals, and show that the compression problem can be represented as a task-based quantization setup, in which the task is to recover the spectrum of the signal. Based on this equivalence, we propose a joint design of the sampling and recovery mechanisms for a fixed quantization mapping, and present an iterative algorithm for dividing the available bit budget among the discretized samples. Furthermore, we show how the proposed approach can be realized using graph filters combining elements corresponding the neighbouring nodes of the graph, thus facilitating distributed implementation at reduced complexity. Our numerical evaluations on both synthetic and real world data shows that the joint sampling and quantization method yields a compact finite bit representation of high-dimensional graph signals, which allows reconstruction of the original signal with accuracy within a small gap of that achievable with infinite resolution quantizers.

eess.SP↗

arXiv2021

A Novel Traffic Simulation Framework for Testing Autonomous Vehicles Using SUMO and CARLA

Traffic simulation is an efficient and cost-effective way to test Autonomous Vehicles (AVs) in a complex and dynamic environment. Numerous studies have been conducted for AV evaluation using traffic simulation over the past decades. However, the current simulation environments fall behind on two fronts -- the background vehicles (BVs) fail to simulate naturalistic driving behavior and the existing environments do not test the entire pipeline in a modular fashion. This study aims to propose a simulation framework that creates a complex and naturalistic traffic environment. Specifically, we combine a modified version of the Simulation of Urban MObility (SUMO) simulator with the Cars Learning to Act (CARLA) simulator to generate a simulation environment that could emulate the complexities of the external environment while providing realistic sensor outputs to the AV pipeline. In a past research work, we created an open-source Python package called SUMO-Gym which generates a realistic road network and naturalistic traffic through SUMO and combines that with OpenAI Gym to provide ease of use for the end user. We propose to extend our developed software by adding CARLA, which in turn will enrich the perception of the ego vehicle by providing realistic sensors outputs of the AVs surrounding environment. Using the proposed framework, AVs perception, planning, and control could be tested in a complex and realistic driving environment. The performance of the proposed framework in constructing output generation and AV evaluations are demonstrated using several case studies.

cs.RO↗