Distributed pedestrian detection alerts based on data fusion with accurate localization

Among Advanced Driver Assistance Systems (ADAS) pedestrian detection is a common issue due to the vulnerability of pedestrians in the event of accidents. In the present work, a novel approach for pedestrian detection based on data fusion is presented. Data fusion helps to overcome the limitations inherent to each detection system (computer vision and laser scanner) and provides accurate and trustable tracking of any pedestrian movement. The application is complemented by an efficient communication protocol, able to alert vehicles in the surroundings by a fast and reliable communication. The combination of a powerful location, based on a GPS with inertial measurement, and accurate obstacle localization based on data fusion has allowed locating the detected pedestrians with high accuracy. Tests proved the viability of the detection system and the efficiency of the communication, even at long distances. By the use of the alert communication, dangerous situations such as occlusions or misdetections can be avoided.This work was supported by the Spanish Government through the Cicyt projects (GRANT TRA2010-20225-C03-01, GRANT TRA2010-20225-C03-03, GRANT TRA 2011-29454-C03-02 and iVANET TRA2010-15645) and CAM through SEGVAUTO-II (S2009/DPI-1509)

Reduction of Fuel Consumption and Exhaust Pollutant Using Intelligent Transport Systems

Greenhouse gas emitted by the transport sector around the world is a serious issue of concern. To minimize such emission the automobile engineers have been working relentlessly. Researchers have been trying hard to switch fossil fuel to alternative fuels and attempting to various driving strategies to make traffic flow smooth and to reduce traffic congestion and emission of greenhouse gas. Automobile emits a massive amount of pollutants such as Carbon Monoxide (CO), hydrocarbons (HC), carbon dioxide (CO2), particulate matter (PM), and oxides of nitrogen (NOx). Intelligent transport system (ITS) technologies can be implemented to lower pollutant emissions and reduction of fuel consumption. This paper investigates the ITS techniques and technologies for the reduction of fuel consumption and minimization of the exhaust pollutant. It highlights the environmental impact of the ITS application to provide the state-of-art green solution. A case study also advocates that ITS technology reduces fuel consumption and exhaust pollutant in the urban environment

Structural Identification Using A Low-cost Search Method

An easily implementable and trainable damage detection method is proposed and implemented for a simple truss structure. The approach uses the iterative search identification method and is compatible with low-cost and low-power microcontroller hardware. This method employs pattern matching for a data set from a strain sensor array and predicts location (truss member) and severity (member cross sectional area) of damage. As a health monitoring approach, the method is not as robust or rigorous as more complex methods. However, it has modest processing requirements and can handle noisy signals. The work presents an algorithm applied to a truss structure, the simulation performance from a finite-element-analysis, and a discussion of capabilities. The simulation demonstrates differing damage locations, damage severity, and signal noise. Its suitability for low-cost and low-power field processors is discussed. © 2009 IEEE

Estimation of Driver's Gaze Region from Head Position and Orientation using Probabilistic Confidence Regions

A smart vehicle should be able to understand human behavior and predict their actions to avoid hazardous situations. Specific traits in human behavior can be automatically predicted, which can help the vehicle make decisions, increasing safety. One of the most important aspects pertaining to the driving task is the driver's visual attention. Predicting the driver's visual attention can help a vehicle understand the awareness state of the driver, providing important contextual information. While estimating the exact gaze direction is difficult in the car environment, a coarse estimation of the visual attention can be obtained by tracking the position and orientation of the head. Since the relation between head pose and gaze direction is not one-to-one, this paper proposes a formulation based on probabilistic models to create salient regions describing the visual attention of the driver. The area of the predicted region is small when the model has high confidence on the prediction, which is directly learned from the data. We use Gaussian process regression (GPR) to implement the framework, comparing the performance with different regression formulations such as linear regression and neural network based methods. We evaluate these frameworks by studying the tradeoff between spatial resolution and accuracy of the probability map using naturalistic recordings collected with the UTDrive platform. We observe that the GPR method produces the best result creating accurate predictions with localized salient regions. For example, the 95% confidence region is defined by an area that covers 3.77% region of a sphere surrounding the driver.Comment: 13 Pages, 12 figures, 2 table

Developing travel time estimation methods using sparse GPS data

Existing methods of estimating travel time from GPS data are not able to simultaneously take account of the issues related to uncertainties associated with GPS and spatial road network data. Moreover, they typically depend upon high frequency data sources from specialist data providers which can be expensive and are not always readily available. The study reported here therefore sought to better estimate travel time using ‘readily available’ vehicle trajectory data from moving sensors such as buses, taxis and logistical vehicles equipped with GPS in ‘near’ real-time. To do this, accurate locations of vehicles on a link were first map-matched to reduce the positioning errors associated with GPS and digital road maps. Two mathematical methods were then developed to estimate link travel times from map-matched GPS fixes, vehicle speeds and network connectivity information with a special focus on sampling frequencies, vehicle penetration rates and time window lengths. GPS data from Interstate I-880 (California, USA) for a total of 73 vehicles over 6 hours were obtained from the UC-2 Berkeley’s Mobile Century Project, and these were used to evaluate several travel time estimation methods, the results of which were then validated against reference travel time data collected from high resolution video cameras. The results indicate that vehicle penetration rates, data sampling frequencies, vehicle coverage on the links and time window lengths all influence the accuracy of link travel time estimation. The performance was found to be best in the 5 minute time window length and for a GPS sampling frequency of 60 seconds

Motion Planning for Autonomous Vehicles in Partially Observable Environments

Unsicherheiten, welche aus Sensorrauschen oder nicht beobachtbaren Manöverintentionen anderer Verkehrsteilnehmer resultieren, akkumulieren sich in der Datenverarbeitungskette eines autonomen Fahrzeugs und führen zu einer unvollständigen oder fehlinterpretierten Umfeldrepräsentation. Dadurch weisen Bewegungsplaner in vielen Fällen ein konservatives Verhalten auf. Diese Dissertation entwickelt zwei Bewegungsplaner, welche die Defizite der vorgelagerten Verarbeitungsmodule durch Ausnutzung der Reaktionsfähigkeit des Fahrzeugs kompensieren. Diese Arbeit präsentiert zuerst eine ausgiebige Analyse über die Ursachen und Klassifikation der Unsicherheiten und zeigt die Eigenschaften eines idealen Bewegungsplaners auf. Anschließend befasst sie sich mit der mathematischen Modellierung der Fahrziele sowie den Randbedingungen, welche die Sicherheit gewährleisten. Das resultierende Planungsproblem wird mit zwei unterschiedlichen Methoden in Echtzeit gelöst: Zuerst mit nichtlinearer Optimierung und danach, indem es als teilweise beobachtbarer Markov-Entscheidungsprozess (POMDP) formuliert und die Lösung mit Stichproben angenähert wird. Der auf nichtlinearer Optimierung basierende Planer betrachtet mehrere Manöveroptionen mit individuellen Auftrittswahrscheinlichkeiten und berechnet daraus ein Bewegungsprofil. Er garantiert Sicherheit, indem er die Realisierbarkeit einer zufallsbeschränkten Rückfalloption gewährleistet. Der Beitrag zum POMDP-Framework konzentriert sich auf die Verbesserung der Stichprobeneffizienz in der Monte-Carlo-Planung. Erstens werden Informationsbelohnungen definiert, welche die Stichproben zu Aktionen führen, die eine höhere Belohnung ergeben. Dabei wird die Auswahl der Stichproben für das reward-shaped Problem durch die Verwendung einer allgemeinen Heuristik verbessert. Zweitens wird die Kontinuität in der Reward-Struktur für die Aktionsauswahl ausgenutzt und dadurch signifikante Leistungsverbesserungen erzielt. Evaluierungen zeigen, dass mit diesen Planern große Erfolge in Fahrversuchen und Simulationsstudien mit komplexen Interaktionsmodellen erreicht werden

A review of the role of sensors in mobile context-aware recommendation systems

Recommendation systems are specialized in offering suggestions about specific items of different types (e.g., books, movies, restaurants, and hotels) that could be interesting for the user. They have attracted considerable research attention due to their benefits and also their commercial interest. Particularly, in recent years, the concept of context-aware recommendation system has appeared to emphasize the importance of considering the context of the situations in which the user is involved in order to provide more accurate recommendations. The detection of the context requires the use of sensors of different types, which measure different context variables. Despite the relevant role played by sensors in the development of context-aware recommendation systems, sensors and recommendation approaches are two fields usually studied independently. In this paper, we provide a survey on the use of sensors for recommendation systems. Our contribution can be seen from a double perspective. On the one hand, we overview existing techniques used to detect context factors that could be relevant for recommendation. On the other hand, we illustrate the interest of sensors by considering different recommendation use cases and scenarios