Predicting Collisions in Mobile Robot Navigation by Kalman Filter

The growing trend of the use of robots in many areas of daily life makes it necessary to search for approaches to improve efficiency in tasks performed by robots. For that reason, we show, in this chapter, the application of the Kalman filter applied to the navigation of mobile robots, specifically the Time-to-contact (TTC) problem. We present a summary of approaches that have been taken to address the TTC problem. We use a monocular vision-based approach to detect potential obstacles and follow them over time through their apparent size change. Our approach collects information about obstacle data and models the behavior while the robot is approaching the obstacle, in order to predict collisions. We highlight some characteristics of the Kalman filter applied to our problem. Finally, we show of our results applied to sequences composed of 210 frames in different real scenarios. The results show a fast convergence of the model to the data and good fit even with noisy measures

Naturalistic Driver Intention and Path Prediction using Machine Learning

Autonomous vehicles are still yet to be available to the public. This is because there are a number of challenges that have not been overcome to ensure that autonomous vehicles can safely and efficiently drive on public roads. Accurate prediction of other vehicles is vital for safe driving, as interacting with other vehicles is unavoidable on public streets. This thesis explores reasons why this problem of scene understanding is still unsolved, and presents methods for driver intention and path prediction. The thesis focuses on intersections, as this is a very complex scenario in which to predict the actions of human drivers. There is very limited data available for intersection studies from the perspective of an autonomous vehicle. This thesis presents a very large dataset of over 23,000 vehicle trajectories, used to validate the algorithms presented in this thesis. This dataset was collected using a lidar based vehicle detection and tracking system onboard a vehicle. Analytics of this data is presented. To determine the intent of vehicle at an intersection, a method for manoeuvre classification through the use of recurrent neural networks is presented. This allows accurate predictions of which destination a vehicle will take at an unsignalised intersection, based on that vehicle's approach. The final contribution of this thesis presents a method for driver path prediction, based on recurrent neural networks. It produces a multi-modal prediction for the vehicle’s path with uncertainty assigned to each mode. The output modes are not hand labelled, but instead learned from the data. This results in there not being a fixed number of output modes. Whilst the application of this method is vehicle prediction, this method shows significant promise to be used in other areas of robotics

Entwicklung und Evaluierung eines kooperativen Interaktionskonzepts an Entscheidungspunkten für die teilautomatisierte, manöverbasierte Fahrzeugführung

Moderne Fahrerassistenzsysteme ermöglichen einen hohen Standard hinsichtlich Fahrkomfort und Sicherheit. Eine Lösung für die Problematik zunehmender Komplexität durch Kombination mehrerer Einzelsysteme und einen wichtigen Schritt in Richtung Vollautomatisierung bieten teilautomatisierte, kooperative Ansätze wie das manöverbasierte Fahrzeugführungskonzept Conduct-by-Wire. Gegenstand dieser Arbeit ist die Untersuchung der Fragestellung, ob eine kooperative Interaktion zwischen Fahrer und Automation zur Entscheidungsfindung hinsichtlich der Ausführbarkeit von Fahrmanövern im Kontext der teilautomatisierten, manöverbasierten Fahrzeugführung darstellbar ist. In dieser Arbeit wird ein Interaktionskonzept entwickelt, das die Anforderungen des Fahrers und der Automation gleichermaßen berücksichtigt. Zudem erfolgt eine Untersuchung der technischen Realisierbarkeit sowie der Gebrauchstauglichkeit im Rahmen einer Probandenstudie