Incomplete vehicle information completion using generative adversarial network to enhance the safety of autonomous driving

2021 ◽  
Author(s):  
Jingzhi Tu ◽  
Gang mei ◽  
Francesco Piccialli
Keyword(s):  
Autonomous Driving ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Vehicle Information

scholarly journals Vehicle trajectory prediction and generation using LSTM models and GANs

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0253868
Author(s):  
Luca Rossi ◽  
Andrea Ajmar ◽  
Marina Paolanti ◽  
Roberto Pierdicca
Keyword(s):  
Traffic Congestion ◽  
Short Term Memory ◽  
Autonomous Driving ◽  
Generative Models ◽  
Trajectory Prediction ◽  
Generative Adversarial Network ◽  
Displacement Error ◽  
Adversarial Network ◽  
Vehicle Trajectories ◽  
Vehicle Trajectory

Vehicles’ trajectory prediction is a topic with growing interest in recent years, as there are applications in several domains ranging from autonomous driving to traffic congestion prediction and urban planning. Predicting trajectories starting from Floating Car Data (FCD) is a complex task that comes with different challenges, namely Vehicle to Infrastructure (V2I) interaction, Vehicle to Vehicle (V2V) interaction, multimodality, and generalizability. These challenges, especially, have not been completely explored by state-of-the-art works. In particular, multimodality and generalizability have been neglected the most, and this work attempts to fill this gap by proposing and defining new datasets, metrics, and methods to help understand and predict vehicle trajectories. We propose and compare Deep Learning models based on Long Short-Term Memory and Generative Adversarial Network architectures; in particular, our GAN-3 model can be used to generate multiple predictions in multimodal scenarios. These approaches are evaluated with our newly proposed error metrics N-ADE and N-FDE, which normalize some biases in the standard Average Displacement Error (ADE) and Final Displacement Error (FDE) metrics. Experiments have been conducted using newly collected datasets in four large Italian cities (Rome, Milan, Naples, and Turin), considering different trajectory lengths to analyze error growth over a larger number of time-steps. The results prove that, although LSTM-based models are superior in unimodal scenarios, generative models perform best in those where the effects of multimodality are higher. Space-time and geographical analysis are performed, to prove the suitability of the proposed methodology for real cases and management services.

Strategies for supplementing recurrent neural network training for spatio-temporal prediction

2019 ◽  
Vol 67 (7) ◽  
pp. 545-556 ◽  
Author(s):  
Mark Schutera ◽  
Stefan Elser ◽  
Jochen Abhau ◽  
Ralf Mikut ◽  
Markus Reischl
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Data Augmentation ◽  
Autonomous Driving ◽  
Temporal Data ◽  
Generative Adversarial Network ◽  
Temporal Prediction ◽  
Adversarial Network ◽  
Augmentation Strategy ◽  
Spatio Temporal

Abstract In autonomous driving, prediction tasks address complex spatio-temporal data. This article describes the examination of Recurrent Neural Networks (RNNs) for object trajectory prediction in the image space. The proposed methods enhance the performance and spatio-temporal prediction capabilities of Recurrent Neural Networks. Two different data augmentation strategies and a hyperparameter search are implemented for this purpose. A conventional data augmentation strategy and a Generative Adversarial Network (GAN) based strategy are analyzed with respect to their ability to close the generalization gap of Recurrent Neural Networks. The results are then discussed using single-object tracklets provided by the KITTI Tracking Dataset. This work demonstrates the benefits of augmenting spatio-temporal data with GANs.

scholarly journals A Novel Method for Estimating Monocular Depth Using Cycle GAN and Segmentation

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2567
Author(s):  
Dong-hoon Kwak ◽  
Seung-ho Lee
Keyword(s):  
Visual Information ◽  
Spatial Information ◽  
Three Dimensional ◽  
Depth Estimation ◽  
Autonomous Driving ◽  
Depth Information ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Novel Method ◽  
Monocular Depth

Modern image processing techniques use three-dimensional (3D) images, which contain spatial information such as depth and scale, in addition to visual information. These images are indispensable in virtual reality, augmented reality (AR), and autonomous driving applications. We propose a novel method to estimate monocular depth using a cycle generative adversarial network (GAN) and segmentation. In this paper, we propose a method for estimating depth information by combining segmentation. It uses three processes: segmentation and depth estimation, adversarial loss calculations, and cycle consistency loss calculations. The cycle consistency loss calculation process evaluates the similarity of two images when they are restored to their original forms after being estimated separately from two adversarial losses. To evaluate the objective reliability of the proposed method, we compared our proposed method with other monocular depth estimation (MDE) methods using the NYU Depth Dataset V2. Our results show that the benchmark value for our proposed method is better than other methods. Therefore, we demonstrated that our proposed method is more efficient in determining depth estimation.

Depth from stacked light field images using generative adversarial network

2019 ◽  
Vol 2019 (11) ◽  
pp. 270-1-270-8
Author(s):  
Ji-Hun Mun ◽  
Yo-Sung Ho
Keyword(s):  
Light Field ◽  
Generative Adversarial Network ◽  
Adversarial Network

Flight Trajectory Pattern Generalization and Abnormal Flight Detection with Generative Adversarial Network

2021 ◽  
Author(s):  
Muhammet Aksoy ◽  
Orhan Ozdemir ◽  
Guney Guner ◽  
Baris Baspinar ◽  
Emre Koyuncu
Keyword(s):  
Flight Trajectory ◽  
Generative Adversarial Network ◽  
Pattern Generalization ◽  
Adversarial Network ◽  
Trajectory Pattern

ORGANIC (1).pdf

2017 ◽  
Author(s):  
Benjamin Sanchez-Lengeling ◽  
Carlos Outeiral ◽  
Gabriel L. Guimaraes ◽  
Alan Aspuru-Guzik
Keyword(s):  
Machine Learning ◽  
Learning Community ◽  
Chemical Species ◽  
Material Design ◽  
Organic Photovoltaic ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Photovoltaic Material

Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over molecular space that matches with a certain set of desirable metrics. This methodology combines two successful techniques from the machine learning community: a Generative Adversarial Network (GAN), to create non-repetitive sensible molecular species, and Reinforcement Learning (RL), to bias this generative distribution towards certain attributes. We explore several applications, from optimization of random physicochemical properties to candidates for drug discovery and organic photovoltaic material design.

Role of General Adversarial Networks in Mammogram Analysis: A Review

2020 ◽  
Vol 16 (7) ◽  
pp. 863-877
Author(s):  
Annapoorani Gopal ◽  
Lathaselvi Gandhimaruthian ◽  
Javid Ali
Keyword(s):  
Breast Tumor ◽  
Deep Neural Networks ◽  
Training Data ◽  
Learning Technology ◽  
Breast Cancers ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Tumor Extraction

The Deep Neural Networks have gained prominence in the biomedical domain, becoming the most commonly used networks after machine learning technology. Mammograms can be used to detect breast cancers with high precision with the help of Convolutional Neural Network (CNN) which is deep learning technology. An exhaustive labeled data is required to train the CNN from scratch. This can be overcome by deploying Generative Adversarial Network (GAN) which comparatively needs lesser training data during a mammogram screening. In the proposed study, the application of GANs in estimating breast density, high-resolution mammogram synthesis for clustered microcalcification analysis, effective segmentation of breast tumor, analysis of the shape of breast tumor, extraction of features and augmentation of the image during mammogram classification have been extensively reviewed.

scholarly journals Realistic Sonar Image Simulation Using Generative Adversarial Network

2019 ◽  
Vol 52 (21) ◽  
pp. 291-296 ◽  
Author(s):  
Minsung Sung ◽  
Jason Kim ◽  
Juhwan Kim ◽  
Son-Cheol Yu
Keyword(s):  
Image Simulation ◽  
Generative Adversarial Network ◽  
Sonar Image ◽  
Adversarial Network
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