Unsupervised tracking algorithm for precise traffic estimation in panoramic scenes

The traffic experiment conducted by physicist Sugiyama in 2007 has been a seminal work in transportation research. In the experiment, a group of vehicles are instructed to drive on a circular track starting with uniform initial spacing. The isolated experimental environment provides a safe, economic, and controlled environment to study free flow traffic and stop-and-go waves. This dissertation introduces a novel method that automates the data collection process in such an environment. Specifically, the vehicle trajectories are measured using a 360-degree camera, and the fuel rates are recorded via on-board diagnostics (OBD) scanners. The video data from the 360-degree camera is then processed by an offline unsupervised computer vision algorithm. To validate the data collection method, the technique is then evaluated on a series of eight experiments. Validation analysis shows that the collected data are highly accurate, with a mean position bias of less than 0.002 m and a small standard deviation of 0.11 m. The positional data also yields highly reliable velocity estimates: the derived velocities are biased by only 0.02 m/s with a small standard deviation of 0.09 m/s. Beyond the experimental methodology, the produced trajectory and fuel rate data can be readily used to study human driving behaviors, to calibrate microsimulation models, to develop fuel consumption models, and to investigate engine emission. To facilitate future research, the source code and the data are made publicly available online

Vehicle Re-identification in Still Images: Application of Semi-supervised Learning and Re-ranking

Vehicle re-identification (re-ID), namely, finding exactly the same vehicle from a large number of vehicle images, remains a great challenge in computer vision. Most existing vehicle re-ID approaches follow a fully supervised learning methodology, in which sufficient labeled training data is required. However, this limits their scalability to realistic applications, due to the high cost of data labeling. In this paper, we adopted a Generative Adversarial Network (GAN) to generate unlabeled samples and enlarge the training set. A semi supervised learning scheme with the Convolutional Neural Networks (CNN) was proposed accordingly, which assigns a uniform label distribution to the unlabeled images to regularize the supervised model and improve the performance of the vehicle re-ID system. Besides, an improved re-ranking method based on the Jaccard distance and k-reciprocal nearest neighbors is proposed to optimize the initial rank list. Extensive experiments over the benchmark datasets VeR1-776, VehicleID and VehicleReID have demonstrated that the proposed method outperforms the state-of-the-art approaches for vehicle re-ID

Hemodynamic changes in progressive cerebral infarction: An observational study based on blood pressure monitoring.

Progressive cerebral infarction (PCI) is a common complication in patients with ischemic stroke that leads to poor prognosis. Blood pressure (BP) can indicate post-stroke hemodynamic changes which play a key role in the development of PCI. The authors aim to investigate the association between BP-derived hemodynamic parameters and PCI. Clinical data and BP recordings were collected from 80 patients with cerebral infarction, including 40 patients with PCI and 40 patients with non-progressive cerebral infarction (NPCI). Hemodynamic parameters were calculated from the BP recordings of the first 7 days after admission, including systolic and diastolic BP, mean arterial pressure, and pulse pressure (PP), with the mean values of each group calculated and compared between daytime and nighttime, and between different days. Hemodynamic parameters and circadian BP rhythm patterns  were compared between PCI and NPCI groups using t-test or non-parametric equivalent for continuous variables, Chi-squared test or Fisher's exact test for categorical variables, Cox proportional hazards regression analysis and binary logistic regression analysis for potential risk factors. In PCI and NPCI groups, significant decrease of daytime systolic BP appeared on the second and sixth days, respectively. Systolic BP and fibrinogen at admission, daytime systolic BP of the first day, nighttime systolic BP of the third day, PP, and the ratio of abnormal BP circadian rhythms were all higher in the PCI group. PCI and NPCI groups were significantly different in BP circadian rhythm pattern. PCI is associated with higher systolic BP, PP and more abnormal circadian rhythms of BP. [Abstract copyright: © 2023 The Authors. The Journal of Clinical Hypertension published by Wiley Periodicals LLC.

Composing MPC with LQR and Neural Network for Amortized Efficiency and Stable Control

Model predictive control (MPC) is a powerful control method that handles dynamical systems with constraints. However, solving MPC iteratively in real time, i.e., implicit MPC, remains a computational challenge. To address this, common solutions include explicit MPC and function approximation. Both methods, whenever applicable, may improve the computational efficiency of the implicit MPC by several orders of magnitude. Nevertheless, explicit MPC often requires expensive pre-computation and does not easily apply to higher-dimensional problems. Meanwhile, function approximation, although scales better with dimension, still requires pre-training on a large dataset and generally cannot guarantee to find an accurate surrogate policy, the failure of which often leads to closed-loop instability. To address these issues, we propose a triple-mode hybrid control scheme, named Memory-Augmented MPC, by combining a linear quadratic regulator, a neural network, and an MPC. From its standard form, we further derive two variants of such hybrid control scheme: one customized for chaotic systems and the other for slow systems. The proposed scheme does not require pre-computation and can improve the amortized running time of the composed MPC with a well-trained neural network. In addition, the scheme maintains closed-loop stability with any neural networks of proper input and output dimensions, alleviating the need for certifying optimality of the neural network in safety-critical applications.Comment: 13 pages, 10 figures, 2 table

Hemodynamic changes in progressive cerebral infarction:An observational study based on blood pressure monitoring

Progressive cerebral infarction (PCI) is a common complication in patients with ischemic stroke that leads to poor prognosis. Blood pressure (BP) can indicate post‐stroke hemodynamic changes which play a key role in the development of PCI. The authors aim to investigate the association between BP‐derived hemodynamic parameters and PCI. Clinical data and BP recordings were collected from 80 patients with cerebral infarction, including 40 patients with PCI and 40 patients with non‐progressive cerebral infarction (NPCI). Hemodynamic parameters were calculated from the BP recordings of the first 7 days after admission, including systolic and diastolic BP, mean arterial pressure, and pulse pressure (PP), with the mean values of each group calculated and compared between daytime and nighttime, and between different days. Hemodynamic parameters and circadian BP rhythm patterns were compared between PCI and NPCI groups using t‐test or non‐parametric equivalent for continuous variables, Chi‐squared test or Fisher's exact test for categorical variables, Cox proportional hazards regression analysis and binary logistic regression analysis for potential risk factors. In PCI and NPCI groups, significant decrease of daytime systolic BP appeared on the second and sixth days, respectively. Systolic BP and fibrinogen at admission, daytime systolic BP of the first day, nighttime systolic BP of the third day, PP, and the ratio of abnormal BP circadian rhythms were all higher in the PCI group. PCI and NPCI groups were significantly different in BP circadian rhythm pattern. PCI is associated with higher systolic BP, PP and more abnormal circadian rhythms of BP

Decentralized Vehicle Coordination: The Berkeley DeepDrive Drone Dataset

Decentralized multiagent planning has been an important field of research in robotics. An interesting and impactful application in the field is decentralized vehicle coordination in understructured road environments. For example, in an intersection, it is useful yet difficult to deconflict multiple vehicles of intersecting paths in absence of a central coordinator. We learn from common sense that, for a vehicle to navigate through such understructured environments, the driver must understand and conform to the implicit "social etiquette" observed by nearby drivers. To study this implicit driving protocol, we collect the Berkeley DeepDrive Drone dataset. The dataset contains 1) a set of aerial videos recording understructured driving, 2) a collection of images and annotations to train vehicle detection models, and 3) a kit of development scripts for illustrating typical usages. We believe that the dataset is of primary interest for studying decentralized multiagent planning employed by human drivers and, of secondary interest, for computer vision in remote sensing settings.Comment: 6 pages, 10 figures, 1 tabl