Review on Automatic Car Parking Indicator System

Parking is costly and limited in almost every major city in the world. An Automatic car parking systems for meeting near term parking demand are needed. There is need to develop a vacant parking slot detection and tracking system. Around view monitor (AVM) image sequence makes it possible with 360-degree scene Bird’s eye view camera. Around view monitor (AVM) captures the image sequence and on combining of each images empty slot is detected. The Ultrasonic sensor is useful to determine the adjacent vehicle. Hierarchical tree structure based parking slot marking method is used to recognize the parking slot marking. After combining sequentially detected parking slot, empty parking slot is recognized and the driver has to select one of the empty parking slots and drive into it. DOI: 10.17762/ijritcc2321-8169.16048

Extending parking assistance for automative user interfaces

Nowadays the trend in the automotive industry is to integrate systems that go beyond the scope of just maneuvering the car. Navigation, communication, and entertainment functions have become usual in most cars. The multitude of sensors present in vehicles today can be used to collect information that can be shared with other drivers in order to make the roads safer and cleaner. A more troubling issue that affects drivers is the search for free parking spots, because of the time waste, fuel consumtion and effort. There are already solutions available that try to help drivers diminish these problems, like crowdsourcing smartphone apps, but they are still far away from being a reliable solution. The overall goal of this thesis is to find new ways of providing parking information to drivers. This information is collected from vehicles which are equipped with latest sensoric hardware capable of detecting parking spaces while driving and distribute these information to the cloud, sharing it with other drivers using smartphones or vehicle's integrated displays. Though the idea is simple, there are many challanges that need to be addressed. The thesis will also look into ways of improving parking surveillance for vehicles to make them less susceptible to vandalism and thefts, by using latest vehicle-integrated video camera systems. A study will be made to see what information drivers want to have related to parking and how this information can be displayed to them. Further, a cloud based-implementation of such a system will be presented in detail and an evaluation will be made to see how the system behaves in the real world.Der aktuelle Trend der Automobilindustrie ist es Systeme zu integrieren, die über das Ziel hinausgehen ein Fahrzeug lediglich zu fahren. Navigations-, Kommunikations- und Entertainmentfunktionen sind inzwischen üblich in vielen Fahrzeugen. Die Vielzahl an verfügbaren Sensoren, die heutzutage in Fahrzeugen verfügbar sind, ermöglichen es Informationen zu sammeln welche mit anderen Fahrern geteilt werden können, um Straßen sicherer und sauberer zu machen. Ein nervenauftreibendes Problem, welches viele Fahrer aufgrund des Zeitverlustes, des Benzinverbrauchs und des Aufwands beeinflusst, ist die Suche nach freien Parkplätzen. Es existieren bereits Lösungen, diese Probleme für den Fahrer verringern, wie z.B. Crowdsourcing Smartphone Apps, aber diese sind immer noch weit davon entfernt zuverlässige Lösungen darzustellen. Das übergreifende Ziel dieser Thesis ist es neue Möglichkeiten zu finden dem Fahrer Parkinformationen zur Verfügung zu stellen. Diese Informationen werden von Fahrzeugen gesammelt, welche mit der neuesten Sensorik ausgestattet sind, die es ermöglicht während der Fahrt Parkplätze zu detektieren und diese Informationen über die Cloud zu verteilen und somit mit anderen Fahrern über Smartphones oder integrierte Displays zu teilen. Obwohl die Idee recht einfach ist gibt es viele Herausforderungen, die bewältigt werden müssen. Dazu wurde auch eine Studie ausgetragen, um zu untersuchen welche Informationen Fahrer im Bezug auf Parken gerne zur Verfügung hätten und wie diese Informationen ihnen angezeigt werden können. Weiterhin werden in dieser Thesis Möglichkeiten evaluiert die Überwachung von Fahrzeugen durch die Verwendung von in Fahrzeug integrierten Videosystemen zu verbessern. Eine Cloud-basierte Implementierung des beschriebenen Systems wird im Detail präsentiert und eine darauf basierende Evaluierung vorgestellt, um zu sehen wie sich derartige Systeme in der realen Welt verhalten

Road Information Extraction from Mobile LiDAR Point Clouds using Deep Neural Networks

Urban roads, as one of the essential transportation infrastructures, provide considerable motivations for rapid urban sprawl and bring notable economic and social benefits. Accurate and efficient extraction of road information plays a significant role in the development of autonomous vehicles (AVs) and high-definition (HD) maps. Mobile laser scanning (MLS) systems have been widely used for many transportation-related studies and applications in road inventory, including road object detection, pavement inspection, road marking segmentation and classification, and road boundary extraction, benefiting from their large-scale data coverage, high surveying flexibility, high measurement accuracy, and reduced weather sensitivity. Road information from MLS point clouds is significant for road infrastructure planning and maintenance, and have an important impact on transportation-related policymaking, driving behaviour regulation, and traffic efficiency enhancement. Compared to the existing threshold-based and rule-based road information extraction methods, deep learning methods have demonstrated superior performance in 3D road object segmentation and classification tasks. However, three main challenges remain that impede deep learning methods for precisely and robustly extracting road information from MLS point clouds. (1) Point clouds obtained from MLS systems are always in large-volume and irregular formats, which has presented significant challenges for managing and processing such massive unstructured points. (2) Variations in point density and intensity are inevitable because of the profiling scanning mechanism of MLS systems. (3) Due to occlusions and the limited scanning range of onboard sensors, some road objects are incomplete, which considerably degrades the performance of threshold-based methods to extract road information. To deal with these challenges, this doctoral thesis proposes several deep neural networks that encode inherent point cloud features and extract road information. These novel deep learning models have been tested by several datasets to deliver robust and accurate road information extraction results compared to state-of-the-art deep learning methods in complex urban environments. First, an end-to-end feature extraction framework for 3D point cloud segmentation is proposed using dynamic point-wise convolutional operations at multiple scales. This framework is less sensitive to data distribution and computational power. Second, a capsule-based deep learning framework to extract and classify road markings is developed to update road information and support HD maps. It demonstrates the practical application of combining capsule networks with hierarchical feature encodings of georeferenced feature images. Third, a novel deep learning framework for road boundary completion is developed using MLS point clouds and satellite imagery, based on the U-shaped network and the conditional deep convolutional generative adversarial network (c-DCGAN). Empirical evidence obtained from experiments compared with state-of-the-art methods demonstrates the superior performance of the proposed models in road object semantic segmentation, road marking extraction and classification, and road boundary completion tasks