Uncertainty-Aware Vehicle Energy Efficiency Prediction using an Ensemble of Neural Networks

The transportation sector accounts for about 25% of global greenhouse gas emissions. Therefore, an improvement of energy efficiency in the traffic sector is crucial to reducing the carbon footprint. Efficiency is typically measured in terms of energy use per traveled distance, e.g. liters of fuel per kilometer. Leading factors that impact the energy efficiency are the type of vehicle, environment, driver behavior, and weather conditions. These varying factors introduce uncertainty in estimating the vehicles' energy efficiency. We propose in this paper an ensemble learning approach based on deep neural networks (ENN) that is designed to reduce the predictive uncertainty and to output measures of such uncertainty. We evaluated it using the publicly available Vehicle Energy Dataset (VED) and compared it with several baselines per vehicle and energy type. The results showed a high predictive performance and they allowed to output a measure of predictive uncertainty

Lokalisierung und Internationalisierung von Fahrerinformationssystemen

Ein Produkt, das auf verschiedenen Märkten eingeführt werden soll, kann bereits während der Entwicklung so gestaltet werden, dass eine nachfolgende Anpassung an die jeweiligen Märkte, die sogenannte "Lokalisierung", möglichst einfach durchgeführt werden kann. Diese Art der Produktentwicklung wird als "Internationalisierung" bezeichnet. In der vorliegenden Arbeit wurde untersucht, inwieweit kulturelle Unterschiede bei der Verwendung der Benutzeroberfläche von Fahrerinformationssystemen (FIS) existieren, vor allem in Bezug auf die Menüstruktur. Die dabei ermittelten Daten bildeten anschließend die Grundlage für eine beispielhafte Implementierung eines Systems mit internationalisierter Menüstruktur. Im Rahmen einer Online-Befragung unter BMW-Mitarbeitern in Deutschland, den USA und Japan wurden 47 Funktionen eines Fahrerinformationssystems in Bezug auf die fünf Parameter "Wichtigkeit", "Sichtbarkeit", "Häufigkeit", "sofort einzuschalten" und "Extraknopf" abgefragt. Immerhin 32% aller Funktionen wurden im Hinblick auf die drei Parameter "Wichtigkeit", "Sichtbarkeit" und "Häufigkeit" in mindestens zwei der Länder signifikant unterschiedlich eingeschätzt und 47% im Hinblick auf drei der fünf abgefragten Parameter. Die höchste Anzahl von Unterschieden wurde im Bereich "Kommunikation" festgestellt, gefolgt von "Unterhaltung". Dabei waren stärkere Abweichungen zwischen Japan und USA als zwischen Japan und Deutschland zu erkennen, am ähnlichsten waren die Antworten von Befragten aus Deutschland und den USA. Aus den erhobenen Daten wurden am Beispiel des Moduls "Unterhaltung" landestypische Menüstrukturen für Deutschland, USA und Japan abgeleitet und aus diesen eine allgemeine, den dreien zugrundeliegende Struktur. Diese Struktur wurde formalisiert und mittels Transformationen in die landestypischen Strukturen umgewandelt. Dies wurde mit Hilfe der Programme Extensible Markup Language (XML) für eine strukturierte Darstellung der Daten, Extensible Stylesheet Language Transformations (XSLT) und XML Path Language (Xpath) für das Transformieren eines Dokuments und Scalable Vector Graphics (auf XML basierende Auszeichnungssprache SVG) für das Erzeugen von Vektorgrafiken und Animationen durchgeführt. Damit wurde ein System implementiert, mit dem eine grundlegende Struktur durch Transformationen in eine landesspezifische Version umgewandelt werden konnte. Dadurch wurde unter anderem eine Trennung der Texte und Symbole von der Struktur erreicht, was eine spätere Übersetzung erleichtert und zugleich dafür sorgt, dass die Struktur selbst durch externe Scripte modifizierbar bleibt. So können im Rahmen einer Lokalisierung Änderungen leicht durchgeführt werden, ohne dass der Quellcode selbst geändert werden muss

Naturalistic yielding behavior of drivers at an unsignalized intersection based on survival analysis

In recent years, autonomous vehicles have become increasingly popular, leading to extensive research on their safe and efficient operation. Understanding road yielding behavior is crucial for incorporating the appropriate driving behavior into algorithms. This paper focuses on investigating drivers' yielding behavior at unsignalized intersections. We quantified and modelled the speed reduction time for vulnerable road users at a zebra crossing using parametric survival analysis. We then evaluated the impact of speed reduction time in two different interaction scenarios, compared to the baseline condition of no interaction through an accelerated failure time regression model with the log-logistic distribution. The results demonstrate the unique characteristics of each yielding behavior scenario, emphasizing the need to account for these variations in the modelling process of autonomous vehicles

Method for Comparison of Surrogate Safety Measures in Multi-Vehicle Scenarios

With the race towards higher levels of automation in vehicles, it is imperative to guarantee the safety of all involved traffic participants. Yet, while high-risk traffic situations between two vehicles are well understood, traffic situations involving more vehicles lack the tools to be properly analyzed. This paper proposes a method to compare Surrogate Safety Measures values in highway multi-vehicle traffic situations such as lane-changes that involve three vehicles. This method allows for a comprehensive statistical analysis and highlights how the safety distance between vehicles is shifted in favor of the traffic conflict between the leading vehicle and the lane-changing vehicle.Comment: 6 page

Implementation of Road Safety Perception in Autonomous Vehicles in a Lane Change Scenario

Understanding human driving behavior is crucial to develop autonomous vehicles' algorithms. However, most low level automation, such as the one in advanced driving assistance systems (ADAS), is based on objective safety measures, which are not always aligned with what the drivers perceive as safe and their correspondent driving behavior. Finding the bridge between the subjective perception and objective safety measures has been analyzed in this paper focusing specifically on lane-change scenarios. Results showed statistically significant differences between what is perceived as safe by drivers and objective metrics depending on the specific maneuver and location of drivers

Road Markings Segmentation from LIDAR Point Clouds using Reflectivity Information

Lane detection algorithms are crucial for the development of autonomous vehicles technologies. The more extended approach is to use cameras as sensors. However, LIDAR sensors can cope with weather and light conditions that cameras can not. In this paper, we introduce a method to extract road markings from the reflectivity data of a 64-layers LIDAR sensor. First, a plane segmentation method along with region grow clustering was used to extract the road plane. Then we applied an adaptive thresholding based on Otsu s method and finally, we fitted line models to filter out the remaining outliers. The algorithm was tested on a test track at 60km/h and a highway at 100km/h. Results showed the algorithm was reliable and precise. There was a clear improvement when using reflectivity data in comparison to the use of the raw intensity data both of them provided by the LIDAR sensor

Development of a ROS-based Architecture for Intelligent Autonomous on Demand Last Mile Delivery

This paper presents the development of the JKU-ITS Last Mile Delivery Robot. The proposed approach utilizes a combination of one 3D LIDAR, RGB-D camera, IMU and GPS sensor on top of a mobile robot slope mower. An embedded computer, running ROS1, is utilized to process the sensor data streams to enable 2D and 3D Simultaneous Localization and Mapping, 2D localization and object detection using a convolutional neural network

Response of Vulnerable Road Users to Visual Information from Autonomous Vehicles in Shared Spaces

Completely unmanned autonomous vehicles have been anticipated for a while. Initially, these are expected to drive only under certain conditions on some roads, and advanced functionality is required to cope with the ever-increasing challenges of safety. To enhance the public's perception of road safety and trust in new vehicular technologies, we investigate in this paper the effect of several interaction paradigms with vulnerable road users by developing and applying algorithms for the automatic analysis of pedestrian body language. We assess behavioral patterns and determine the impact of the coexistence of AVs and other road users on general road safety in a shared space for VRUs and vehicles. Results showed that the implementation of visual communication cues for interacting with VRUs is not necessarily required for a shared space in which informal traffic rules apply.Comment: Published paper in the IEEE Intelligent Transportation Systems Conference - ITSC 201