Fakultät Elektrotechnik und Informatik
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A Novel Approach for Researching Crossing Behavior and Risk Acceptance: The Pedestrian Simulator
(2016)
Normalization of Micro-Doppler Spectra for Cyclists Using High-Resolution Projection Technique
(2019)
First Person Trolley Problem: Evaluation of Drivers' Ethical Decisions in a Driving Simulator
(2016)
A Bermuda Triangle?: A Review of Method Application and Triangulation in User Experience Evaluation
(2018)
Drowsiness Detection and Warning in Manual and Automated Driving: Results from Subjective Evaluation
(2018)
Who is Generation A? Investigating the Experience of Automated Driving for Different Age Groups
(2018)
Machine Learning Architectures for the Estimation of Predicted Occupancy Grids in Road Traffic
(2018)
This paper introduces a novel machine learning architecture for an efficient estimation of the probabilistic space-time representation of complex traffic scenarios. A detailed representation of the future traffic scenario is of significant importance for autonomous driving and for all active safety systems. In order to predict the future spacetime representation of the traffic scenario, first the type of traffic scenario is identified and then the machine learning algorithm maps the current state of the scenario to possible future states. The input to the machine learning algorithms is the current state representation of a traffic scenario, termed as the Augmented Occupancy Grid (AOG). The output is the probabilistic space-time representation which includes uncertainties regarding the behaviour of the traffic participants and is termed as the Predicted Occupancy Grid (POG). The novel architecture consists of two Stacked Denoising Autoencoders (SDAs) and a set of Random Forests. It is then compared with the other two existing architectures that comprise of SDAs and DeconvNet. The architectures are validated with the help of simulations and the comparisons are made both in terms of accuracy and computational time. Also, a brief overview on the applications of POGs in the field of active safety is presented.
In road traffic, critical situations pass by as quickly as they appear. Within the blink of an eye, one has to come to a decision, which can make the difference between a low severity, high severity or fatal crash. Because time is important, a machine learning driven Crash Severity Predictor (CSP) is presented which provides the estimated crash severity distribution of an imminent crash in less than 0.2ms. This is 63⋅ 103 times faster compared to predicting the same distribution through computationally expensive numerical simulations. With the proposed method, even very complex crash data, like the results of Finite Element Method (FEM) simulations, can be made available ahead of a collision. Knowledge, which can be used to prepare occupants and vehicle to an imminent crash, activate and adjust safety measures like airbags or belt tensioners before of a collision or let self-driving vehicles go for the maneuver with the lowest crash severity. Using a real-world crash test it is shown that significant safety potential is left unused if instead of the CSP-proposed driving maneuver, no or the wrong actions are taken.
A Hybrid Machine Learning Approach for Planning Safe Trajectories in Complex Traffic-Scenarios
(2016)
Simulation-Based Evaluation of ETSI ITS-G5 and Cellular-VCS in a Real-World Road Traffic Scenario
(2018)
Combined Accelerated Stress Test with In-Situ Thermal Impedance Monitoring to Access LED Reliability
(2019)
Process development and reliability of sintered high power chip size packages and flip chip LEDs
(2018)
Verbesserung modellbasierter Überschlagserkennung mittels Schätzung relevanter Fahrzeugparameter
(2012)
The control flow of programs can be represented by directed graphs. In this paper we provide a uniform and detailed formal basis for control flow graphs combining known definitions and results with new aspects. Two graph reductions are defined using only syntactical information about the graphs, but no semantical information about the represented programs. We prove some properties of reduced graphs and also about the paths in reduced graphs. Based on graphs, we define statement coverage and branch coverage such that coverage notions correspond to node coverage, and edge coverage, respectively.