UDP-YOLO: High Efficiency and Real-Time Performance of Autonomous Driving Technology

In recent years, autonomous driving technology has gradually appeared in our field of vision. It senses the surrounding environment by using radar, laser, ultrasound, GPS, computer vision and other technologies, and then identifies obstacles and various signboards, and plans a suitable path to control the driving of vehicles. However, some problems occur when this technology is applied in foggy environment, such as the low probability of recognizing objects, or the fact that some objects cannot be recognized because the fog's fuzzy degree makes the planned path wrong. In view of this defect, and considering that automatic driving technology needs to respond quickly to objects when driving, this paper extends the prior defogging algorithm of dark channel, and proposes UDP-YOLO network to apply it to automatic driving technology. This paper is mainly divided into two parts: 1. Image processing: firstly, the data set is discriminated whether there is fog or not, then the fogged data set is defogged by defogging algorithm, and finally, the defogged data set is subjected to adaptive brightness enhancement; 2. Target detection: UDP-YOLO network proposed in this paper is used to detect the defogged data set. Through the observation results, it is found that the performance of the model proposed in this paper has been greatly improved while balancing the speed

Artifact-free single image defogging

none2noIn this paper, we present a novel defogging technique, named CurL-Defog, with the aim of minimizing the insertion of artifacts while maintaining good contrast restoration and visibility enhancement. Many learning-based defogging approaches rely on paired data, where fog is artificially added to clear images; this usually provides good results on mildly fogged images but is not effective for difficult cases. On the other hand, the models trained with real data can produce visually impressive results, but unwanted artifacts are often present. We propose a curriculum learning strategy and an enhanced CycleGAN model to reduce the number of produced artifacts, where both synthetic and real data are used in the training procedure. We also introduce a new metric, called HArD (Hazy Artifact Detector), to numerically quantify the number of artifacts in the defogged images, thus avoiding the tedious and subjective manual inspection of the results. HArD is then combined with other defogging indicators to produce a solid metric that is not deceived by the presence of artifacts. The proposed approach compares favorably with state-of-the-art techniques on both real and synthetic datasets.noneGraffieti G.; Maltoni D.Graffieti G.; Maltoni D

Quality and field of vision - a review of the needs of drivers and riders: final report.

Quality and field of vision - a review of the needs of drivers and riders: final report

Improvement of driver night vision in foggy environments by structured light projection

Nowadays, fog is still a natural phenomenon that hinders our ability to detect targets, particularly in the field of driving where accidents are increasing. In the literature we find different studies determining the range of visibility, improving the quality of an image, determining the characteristics of fog, etc. In this work we propose the possibility of using a structured lighting system, on which we project the light towards the target, limiting the field lighting. We have developed a scattering light propagation model to simulate and subsequently study the veil luminance, generated by backscattering, to predict the decrease in visibility. This simulation considers the type of fog, the relative orientation of various elements (observer, light source and targets). We have built a fog chamber to validate the experimental params of the described system. The results obtained from both the simulation and the experimental measurements demonstrate that it is possible to obtain a high contrast enhancement for viewing a target when illuminated as described. Clearly, this kind of lighting technology will improve the road safety in foggy night environments. The results of this work can also be extrapolated to any situation in which the visibility of an observer is compromised by the environment, such as heavy rain, smoke from fires, among others

Sensor Technologies for Intelligent Transportation Systems

Modern society faces serious problems with transportation systems, including but not limited to traffic congestion, safety, and pollution. Information communication technologies have gained increasing attention and importance in modern transportation systems. Automotive manufacturers are developing in-vehicle sensors and their applications in different areas including safety, traffic management, and infotainment. Government institutions are implementing roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. By seamlessly integrating vehicles and sensing devices, their sensing and communication capabilities can be leveraged to achieve smart and intelligent transportation systems. We discuss how sensor technology can be integrated with the transportation infrastructure to achieve a sustainable Intelligent Transportation System (ITS) and how safety, traffic control and infotainment applications can benefit from multiple sensors deployed in different elements of an ITS. Finally, we discuss some of the challenges that need to be addressed to enable a fully operational and cooperative ITS environment

Mapping and Deep Analysis of Image Dehazing: Coherent Taxonomy, Datasets, Open Challenges, Motivations, and Recommendations

Our study aims to review and analyze the most relevant studies in the image dehazing field. Many aspects have been deemed necessary to provide a broad understanding of various studies that have been examined through surveying the existing literature. These aspects are as follows: datasets that have been used in the literature, challenges that other researchers have faced, motivations, and recommendations for diminishing the obstacles in the reported literature. A systematic protocol is employed to search all relevant articles on image dehazing, with variations in keywords, in addition to searching for evaluation and benchmark studies. The search process is established on three online databases, namely, IEEE Xplore, Web of Science (WOS), and ScienceDirect (SD), from 2008 to 2021. These indices are selected because they are sufficient in terms of coverage. Along with definition of the inclusion and exclusion criteria, we include 152 articles to the final set. A total of 55 out of 152 articles focused on various studies that conducted image dehazing, and 13 out 152 studies covered most of the review papers based on scenarios and general overviews. Finally, most of the included articles centered on the development of image dehazing algorithms based on real-time scenario (84/152) articles. Image dehazing removes unwanted visual effects and is often considered an image enhancement technique, which requires a fully automated algorithm to work under real-time outdoor applications, a reliable evaluation method, and datasets based on different weather conditions. Many relevant studies have been conducted to meet these critical requirements. We conducted objective image quality assessment experimental comparison of various image dehazing algorithms. In conclusions unlike other review papers, our study distinctly reflects different observations on image dehazing areas. We believe that the result of this study can serve as a useful guideline for practitioners who are looking for a comprehensive view on image dehazing

Detecting road boundaries and drivable regions in challenging weather conditions

Road detection is a core component of self-driving vehicle perception, where it covers detecting road boundaries and drivable road regions. It can also help human drivers to drive safely in lower visibility. The majority of current road detection techniques use camera and lidar sensors. These sensors struggle in inclement weather conditions. MMwave radar works well in all weather conditions. However, due to the low resolution of the radar, it is currently limited to object detection for cruise control applications. This thesis investigates the impact of bad weather on vision-based systems and introduces a camera and radar-based method for efficient road detection. We propose a novel approach to overcome the sparse resolution of mmwave-radars and use it in the segmentation task. We augment the nuScenes dataset with fog and rain and use it for our validation. We achieve 20% and 18% better road boundary and drivable region detection in inclement weather

Guidelines for data quality assurance

The UDRIVE project aims to colle ct on the region of 100,000 hours of naturalistic driving data in order to support the analysis related to o Crash causation, crash risk and normal driving o Distraction and inattention o Vulnerable road users o Driving styles related to eco-driving This document contains information relevant to data quality assurance for the UDRIVE project. Good quality data is a fundamental requirement for good quality analysis and data quality should be considered at all stages of the data processing chain: o Data Acquisition System Installation o During data collection o Database management • Data preprocessing • Data post-processing In order to deliver high quality data as an outcome from the UDRIVE project actions have been undertaken at each stage of the chain, following generic guidelines for data quality

Building the Hyperconnected Society- Internet of Things Research and Innovation Value Chains, Ecosystems and Markets

This book aims to provide a broad overview of various topics of Internet of Things (IoT), ranging from research, innovation and development priorities to enabling technologies, nanoelectronics, cyber-physical systems, architecture, interoperability and industrial applications. All this is happening in a global context, building towards intelligent, interconnected decision making as an essential driver for new growth and co-competition across a wider set of markets. It is intended to be a standalone book in a series that covers the Internet of Things activities of the IERC – Internet of Things European Research Cluster from research to technological innovation, validation and deployment.The book builds on the ideas put forward by the European Research Cluster on the Internet of Things Strategic Research and Innovation Agenda, and presents global views and state of the art results on the challenges facing the research, innovation, development and deployment of IoT in future years. The concept of IoT could disrupt consumer and industrial product markets generating new revenues and serving as a growth driver for semiconductor, networking equipment, and service provider end-markets globally. This will create new application and product end-markets, change the value chain of companies that creates the IoT technology and deploy it in various end sectors, while impacting the business models of semiconductor, software, device, communication and service provider stakeholders. The proliferation of intelligent devices at the edge of the network with the introduction of embedded software and app-driven hardware into manufactured devices, and the ability, through embedded software/hardware developments, to monetize those device functions and features by offering novel solutions, could generate completely new types of revenue streams. Intelligent and IoT devices leverage software, software licensing, entitlement management, and Internet connectivity in ways that address many of the societal challenges that we will face in the next decade

Aerospace medicine and biology. A continuing bibliography with indexes, supplement 240, January 1983

Reports, articles and other documents, numbering 357, introduced into the NASA scientific and technical information system in December 1982 are given