Repository der Technischen Hochschule Ingolstadt
Not a member yet
4170 research outputs found
Sort by
PURSS: Towards Perceptual Uncertainty Aware Responsibility Sensitive Safety with ML
No full textAutomated driving is an ML-intensive problem and its safety depends on the integrity of perception as well as planning and control. Responsibility Sensitive Safety (RSS) is a recent approach to promote safe planning and control that relies on perfect perception; however, perceptual uncertainty is always present, and this causes the possibility of misperceptions that can lead an autonomous vehicle to allow unsafe actions. In this position paper, we sketch a novel proposal for a formal model of perception coupled with RSS to help mitigate the impact of misperception by using information about perceptual uncertainty. The approach expresses uncertainty as imprecise perceptions that are consumed by RSS and cause it to limit actions to those that support safe behaviour given the perceptual uncertainty. We illustrate our approach using examples and discuss its implications and limitations
Characterization and modeling of the swelling behavior of a LiFePO4 pouch cell in multiple operating conditions
No full textPattern Identifications in Transformed Acoustic Signals Using Classification Models
Get PDFPattern identifications in signals is necessary to discern variations from approved normal values in different scenarios. With machine learning algorithms, it is possible to use hybrid methods of pattern identifications such as feature extractions followed by classifications and/or clustering. This paper presents a pattern identification approach of acoustic signals using their transformations as inputs to classification algorithms. The analysis is carried out on two transformed versions of acoustic emission (AE) hits such as log transformations of peak hits and binary sequencing based on threshold crossing. A comparative analysis using custom data loss metrics is made to determine which inputs provide the best information in predictive methods for identifying commonly occurring patterns while acknowledging significant data loss. The methodology is conducted on transformed versions of a public dataset and the results show that patterns can be discerned to above 90% accuracy with the transformed datasets. The experimental results yield that actual source signals need not be utilized depending on which transformations suit the practical application
Increasing Safety of Vulnerable Road Users in Scenarios With Occlusion: A Collaborative Approach for Smart Infrastructures and Automated Vehicles
Get PDFThe impact of Automated Vehicles (AVs) on road traffic safety has become the focus of discussions among governmental organizations, academia, stakeholders, and OEMs. Questions about how safe the automated driving features should be and how the road infrastructure should be improved for the arrival of this new technology must be clarified to enable full acceptance by the customers and society and prepare the mobility of future cities. The fundamental architecture of automated vehicles comprises perception, planning, decision, and actuation. The operation of the perception system, which is responsible for understanding the environment in which the vehicle is inserted, relies mainly on the onboard sensors. However, the available ranging and vision sensors, e.g., LiDAR, radar, and camera, have several limitations. Scenarios with occlusion present a real challenge for state-of-the-art perception systems. The occlusion, caused by obstructing the sensors’ detection field, limits the vehicle’s perception ability and inhibits the detection of other road users in the surroundings, especially Vulnerable Road Users (VRUs). Infrastructure composed of Roadside Units (RSUs) equipped with infrastructure-based sensors can overcome the perception limitations of a system based solely on onboard sensors by monitoring the road environment with a larger field of view and reduced sensitivity to occlusion. This paper presents a collaborative approach for smart infrastructures and automated vehicles for vulnerable road users’ collision avoidance. The proposed extended perception system comprises four main modules: traffic monitoring, long-term motion prediction, collision risk assessment, and trajectory planning. In the event of a safety-critical scenario, the infrastructure generates a safe and comfortable evasive maneuver to avoid a possible collision. Hence, the proposed approach provides a complete solution to overcome scenarios with occluded VRUs. It allows AVs to react to a critical situation with a longer time-to-collision than other systems relying only on onboard sensors, increasing the chance of successful avoidance even when implementing smoother maneuvers. This contributes considerably to the safe and comfortable operation of automated vehicles
Investigation of the Flexibility Potential by Decoupling Building Mass and Room Temperature
Get PDFUsing the thermal building mass as a thermal storage received increasing attention in research during recent years. Due to the large mass of concrete, it offers a large storage capacity and thus a high potential for flexibility. However, passive heat losses during cool down of a thermally activated building influence room temperature and thus limit its flexibility potential. In this contribution a multi-layer activation concept was investigated which thermally decouples the building mass and room air. The study aims to analyse the cool down of a thermally activated building in terms of different charging parameters while considering other heat sources such as solar gains. A dynamic building simulation of a demonstration building was set-up and compared to simulative studies from literature to proof the validity of its dynamic behaviour. In the simulation model the room temperature could be kept above 19 °C between 100 - 190 h. However, when charging the building structure quickly, room temperatures above 24 °C are reached easily. Considering other heat sources such as solar gains, advanced control algorithms are required for efficient operation of the heating system
