检索结果-内蒙古大学图书馆

Robust Autonomous Vehicle Pursuit Without Expert Steering Labels

学校读者我要写书评

暂无评论

IEEE Robotics and Automation Letters 2023年第10期8卷 6595-6602页

作者： Pan, Jiaxin Zhou, Changyao Gladkova, Mariia Khan, Qadeer Cremers, Daniel Technical University of Munich Computer Vision Group Garching85748 Germany Munich Data Science Institute Garching85748 Germany Munich Center for Machine Learning Munchen80333 Germany University of Oxford OxfordOX1 3AZ United Kingdom

In this work, we present a learning method for both lateral and longitudinal motion control of an ego-vehicle for the task of vehicle pursuit. The car being controlled does not have a pre-defined route, rather it reactively adapts to follow a target vehicle while maintaining a safety distance. To train our model, we do not rely on steering labels recorded from an expert driver, but effectively leverage a classical controller as an offline label generation tool. In addition, we account for the errors in the predicted control values, which can lead to a loss of tracking and catastrophic crashes of the controlled vehicle. To this end, we propose an effective data augmentation approach, which allows to train a network that is capable of handling different views of the target vehicle. During the pursuit, the target vehicle is firstly localized using a Convolutional Neural Network. The network takes a single RGB image along with cars' velocities and estimates target vehicle's pose with respect to the ego-vehicle. This information is then fed to a Multi-Layer Perceptron, which regresses the control commands for the ego-vehicle, namely throttle and steering angle. We extensively validate our approach using the CARLA simulator on a wide range of terrains. Our method demonstrates real-time performance, robustness to different scenarios including unseen trajectories and high route completion. © 2016 IEEE.

关键词： Deep learning

Multi-Vehicle Trajectory Prediction at Intersections using State and Intention Information

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Zhu, Dekai Khan, Qadeer Cremers, Daniel Computer Vision Group CIT Technical University of Munich This work was funded by the Munich Center for Machine Learning Germany

Traditional approaches to prediction of future trajectory of road agents rely on knowing information about their past trajectory. This work rather relies only on having knowledge of the current state and intended direction to make predictions for multiple vehicles at intersections. Furthermore, message passing of this information between the vehicles provides each one of them a more holistic overview of the environment allowing for a more informed prediction. This is done by training a neural network which takes the state and intent of the multiple vehicles to predict their future trajectory. Using the intention as an input allows our approach to be extended to additionally control the multiple vehicles to drive towards desired paths. Experimental results demonstrate the robustness of our approach both in terms of trajectory prediction and vehicle control at intersections. The complete training and evaluation code for this work is available here: https:// ***/Dekai21/Multi_Agent_Intersection. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

MV-Match: Multi-View Matching for Domain-Adaptive Identification of Plant Nutrient Deficiencies

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Yi, Jinhui Luo, Yanan Deichmann, Marion Schaaf, Gabriel Gall, Juergen Computer Vision Group University of Bonn Bonn Germany Plant Nutrition Group University of Bonn Bonn Germany Lamarr Institute for Machine Learning and Artificial Intelligence Germany

An early, non-invasive, and on-site detection of nutrient deficiencies is critical to enable timely actions to prevent major losses of crops caused by lack of nutrients. While acquiring labeled data is very expensive, collecting images from multiple views of a crop is straightforward. Despite its relevance for practical applications, unsupervised domain adaptation where multiple views are available for the labeled source domain as well as the unlabeled target domain is an unexplored research area. In this work, we thus propose an approach that leverages multiple camera views in the source and target domain for unsupervised domain adaptation. We evaluate the proposed approach on two nutrient deficiency datasets. The proposed method achieves state-of-the-art results on both datasets compared to other unsupervised domain adaptation methods. The dataset and source code are available at https://***/jh-yi/MV-Match. Copyright © 2024, The Authors. All rights reserved.

关键词：

How to Choose a Reinforcement-learning Algorithm

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Bongratz, Fabian Golkov, Vladimir Mautner, Lukas Libera, Luca Della Heetmeyer, Frederik Czaja, Felix Rodemann, Julian Cremers, Daniel Computer Vision Group Technical University of Munich Germany Munich Center for Machine Learning Germany Department of Statistics Ludwig-Maximilians-Universität Munich Germany

The field of reinforcement learning offers a large variety of concepts and methods to tackle sequential decision-making problems. This variety has become so large that choosing an algorithm for a task at hand can be challenging. In this work, we streamline the process of choosing reinforcement-learning algorithms and action-distribution families. We provide a structured overview of existing methods and their properties, as well as guidelines for when to choose which methods. An interactive version of these guidelines is available online at https://***/.MSC Codes 62M45 © 2024, CC BY-NC-SA.

关键词： Adversarial machine learning

LiDAR View Synthesis for Robust Vehicle Navigation Without Expert Labels

学校读者我要写书评

暂无评论

LiDAR View Synthesis for Robust Vehicle Navigation Without E...

International Conference on Intelligent Transportation

作者： Jonathan Schmidt Qadeer Khan Daniel Cremers Computer Vision Group School of Computation Information and Technology Technical University of Munich Munich Center for Machine Learning (MCML) University of Oxford

Deep learning models for self-driving cars require a diverse training dataset to manage critical driving scenarios on public roads safely. This includes having data from divergent trajectories, such as the oncoming traffic lane or sidewalks. Such data would be too dangerous to collect in the real world. Data augmentation approaches have been proposed to tackle this issue using RGB images. However, solutions based on LiDAR sensors are scarce. Therefore, we propose synthesizing additional LiDAR point clouds from novel viewpoints without physically driving at dangerous positions. The LiDAR view synthesis is done using mesh reconstruction and ray casting. We train a deep learning model, which takes a LiDAR scan as input and predicts the future trajectory as output. A waypoint controller is then applied to this predicted trajectory to determine the throttle and steering labels of the ego-vehicle. Our method neither requires expert driving labels for the original nor the synthesized LiDAR sequence. Instead, we infer labels from LiDAR odometry. We demonstrate the effectiveness of our approach in a comprehensive online evaluation and with a comparison to concurrent work. Our results show the importance of synthesizing additional LiDAR point clouds, particularly in terms of model robustness. Code and supplementary visualizations are available at: https://***/lidar-synthesis/

关键词：

Multi Agent Navigation in Unconstrained Environments using a Centralized Attention based Graphical Neural Network Controller

学校读者我要写书评

暂无评论

Multi Agent Navigation in Unconstrained Environments using a...

International Conference on Intelligent Transportation

作者： Yining Ma Qadeer Khan Daniel Cremers Computer Vision Group School of Computation Information and Technology Technical University of Munich Munich Center for Machine Learning (MCML) University of Oxford

In this work, we propose a learning based neural model that provides both the longitudinal and lateral control commands to simultaneously navigate multiple vehicles. The goal is to ensure that each vehicle reaches a desired target state without colliding with any other vehicle or obstacle in an unconstrained environment. The model utilizes an attention based Graphical Neural Network paradigm that takes into consideration the state of all the surrounding vehicles to make an informed decision. This allows each vehicle to smoothly reach its destination while also evading collision with the other agents. The data and corresponding labels for training such a network is obtained using an optimization based procedure. Experimental results demonstrate that our model is powerful enough to generalize even to situations with more vehicles than in the training data. Our method also outperforms comparable graphical neural network architectures. Project page which includes the code and supplementary information can be found here: https://***/multi-agent-control/

关键词：

A survey of historical document image datasets

学校读者我要写书评

暂无评论

A survey of historical document image datasets

作者： Nikolaidou, Konstantina Seuret, Mathias Mokayed, Hamam Liwicki, Marcus EISLAB Machine Learning Group Luleå University of Technology Aurorum 1 Norrbotten Luleå97187 Sweden Pattern Recognition Lab Computer Vision Group Friedrich-Alexander-Universität Martensstr. 3 Bavaria Erlangen91058 Germany

This paper presents a systematic literature review of image datasets for document image analysis, focusing on historical documents, such as handwritten manuscripts and early prints. Finding appropriate datasets for historical document analysis is a crucial prerequisite to facilitate research using different machine learning algorithms. However, because of the very large variety of the actual data (e.g., scripts, tasks, dates, support systems, and amount of deterioration), the different formats for data and label representation, and the different evaluation processes and benchmarks, finding appropriate datasets is a difficult task. This work fills this gap, presenting a meta-study on existing datasets. After a systematic selection process (according to PRISMA guidelines), we select 65 studies that are chosen based on different factors, such as the year of publication, number of methods implemented in the article, reliability of the chosen algorithms, dataset size, and journal outlet. We summarize each study by assigning it to one of three pre-defined tasks: document classification, layout structure, or content analysis. We present the statistics, document type, language, tasks, input visual aspects, and ground truth information for every dataset. In addition, we provide the benchmark tasks and results from these papers or recent competitions. We further discuss gaps and challenges in this domain. We advocate for providing conversion tools to common formats (e.g., COCO format for computer vision tasks) and always providing a set of evaluation metrics, instead of just one, to make results comparable across studies. © 2022, The Author(s).

关键词： Deterioration

Depth Estimation using Weighted-loss and Transfer learning

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Hafeez, Muhammad Adeel Madden, Michael G. Sistu, Ganesh Ullah, Ihsan Machine Learning Research Group School of Computer Science University of Galway Ireland Insight SFI Research Centre for Data Analytics University of Galway Ireland Valeo Vision Systems Tuam Ireland

Depth estimation from 2D images is a common computer vision task that has applications in many fields including autonomous vehicles, scene understanding and robotics. The accuracy of a supervised depth estimation method mainly relies on the chosen loss function, the model architecture, quality of data and performance metrics. In this study, we propose a simplified and adaptable approach to improve depth estimation accuracy using transfer learning and an optimized loss function. The optimized loss function is a combination of weighted losses to which enhance robustness and generalization: Mean Absolute Error (MAE), Edge Loss and Structural Similarity Index (SSIM). We use a grid search and a random search method to find optimized weights for the losses, which leads to an improved model. We explore multiple encoder-decoder-based models including DenseNet121, DenseNet169, DenseNet201, and EfficientNet for the supervised depth estimation model on NYU Depth Dataset v2. We observe that the EfficientNet model, pre-trained on ImageNet for classification when used as an encoder, with a simple upsampling decoder, gives the best results in terms of RSME, REL and log10: 0.386, 0.113 and 0.049, respectively. We also perform a qualitative analysis which illustrates that our model produces depth maps that closely resemble ground truth, even in cases where the ground truth is flawed. The results indicate significant improvements in accuracy and robustness, with EfficientNet being the most successful architecture. Copyright © 2024, The Authors. All rights reserved.

关键词： Signal encoding

Beyond the Known: Adversarial Autoencoders in Novelty Detection

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Asad, Muhammad Ullah, Ihsan Sistu, Ganesh Madden, Michael G. Machine Learning Research Group School of Computer Science University of Galway Ireland Insight SFI Research Centre for Data Analytics University of Galway Ireland Valeo Vision Systems Tuam Ireland

In novelty detection, the goal is to decide if a new data point should be categorized as an inlier or an outlier, given a training dataset that primarily captures the inlier distribution. Recent approaches typically use deep encoder and decoder network frameworks to derive a reconstruction error, and employ this error either to determine a novelty score, or as the basis for a one-class classifier. In this research, we use a similar framework but with a lightweight deep network, and we adopt a probabilistic score with reconstruction error. Our methodology calculates the probability of whether the sample comes from the inlier distribution or not. This work makes two key contributions. The first is that we compute the novelty probability by linearizing the manifold that holds the structure of the inlier distribution. This allows us to interpret how the probability is distributed and can be determined in relation to the local coordinates of the manifold tangent space. The second contribution is that we improve the training protocol for the network. Our results indicate that our approach is effective at learning the target class, and it outperforms recent state-of-the-art methods on several benchmark datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Anomaly detection