Structuring Validation Targets of a Machine Learning Function Applied to Automated Driving

2018 ◽  
pp. 45-58 ◽  
Author(s):  
Lydia Gauerhof ◽  
Peter Munk ◽  
Simon Burton
Keyword(s):  
Machine Learning ◽  
Automated Driving ◽  
Learning Function

scholarly journals Predict+Optimise with Ranking Objectives: Exhaustively Learning Linear Functions

2019 ◽  
Author(s):  
Emir Demirovic ◽  
Peter J. Stuckey ◽  
James Bailey ◽  
Jeffrey Chan ◽  
Christopher Leckie ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computational Complexity ◽  
Combinatorial Optimisation ◽  
Linear Functions ◽  
Optimal Parameters ◽  
Common Goal ◽  
Learning Function ◽  
Input Parameters ◽  
Theoretical Insight ◽  
Insight Into

We study the predict+optimise problem, where machine learning and combinatorial optimisation must interact to achieve a common goal. These problems are important when optimisation needs to be performed on input parameters that are not fully observed but must instead be estimated using machine learning. Our contributions are two-fold: 1) we provide theoretical insight into the properties and computational complexity of predict+optimise problems in general, and 2) develop a novel framework that, in contrast to related work, guarantees to compute the optimal parameters for a linear learning function given any ranking optimisation problem. We illustrate the applicability of our framework for the particular case of the unit-weighted knapsack predict+optimise problem and evaluate on benchmarks from the literature.

Machine Learning in Gifted Education: A Demonstration Using Neural Networks

2019 ◽  
Vol 63 (4) ◽  
pp. 243-252 ◽  
Author(s):  
Jaret Hodges ◽  
Soumya Mohan
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Supervised Learning ◽  
Language Processing ◽  
Gifted Education ◽  
Learning Algorithms ◽  
Simulated Data ◽  
Machine Learning Algorithms ◽  
Automated Driving ◽  
The Neural Network

Machine learning algorithms are used in language processing, automated driving, and for prediction. Though the theory of machine learning has existed since the 1950s, it was not until the advent of advanced computing that their potential has begun to be realized. Gifted education is a field where machine learning has yet to be utilized, even though one of the underlying problems of gifted education is classification, which is an area where learning algorithms have become exceptionally accurate. We provide a brief overview of machine learning with a focus on neural networks and supervised learning, followed by a demonstration using simulated data and neural networks for classification issues with a practical explanation of the mechanics of the neural network and associated R code. Implications for gifted education are then discussed. Finally, the limitations of supervised learning are discussed. Code used in this article can be found at https://osf.io/4pa3b/

scholarly journals Approach to Define the Reliability of Safety-Related Machine Learning Based Functions in Highly Automated Driving

2021 ◽  
Author(s):  
Pengyu Si ◽  
Ossmane Krini ◽  
Nadine Müller ◽  
Aymen Ouertani
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Pedestrian Detection ◽  
Quality Criteria ◽  
Automated Driving ◽  
Safety Requirements ◽  
Functional Reliability ◽  
Current Standards ◽  
Highly Automated Driving

Current standards cannot cover the safety requirements of machine learning based functions used in highly automated driving. Because of the opacity of neural networks, some self-driving functions cannot be developed following the V-model. These functions require the expansion of the standards. This paper focuses on this gap and defines functional reliability for such functions to help the future standards control the quality of machine learning based functions. As an example, reliability functions for pedestrian detection are built. Since the quality criteria in computer vision do not consider safety, new approaches for expression and evaluation of this reliability are designed.

scholarly journals Genetic Evolutionary Learning Fitness Function (GELFF) for Feature Diagnosis to Software Fault Prediction

2019 ◽  
Vol 8 (11S) ◽  
pp. 1151-1161
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Fitness Function ◽  
Fault Prediction ◽  
Evolutionary Learning ◽  
Software Fault Prediction ◽  
Underlying Process ◽  
Learning Function ◽  
Wide Range ◽  
Software Fault

Nowadays, proper feature selection f+orFault prediction is very perplexing task. Improper feature selection may lead to bad result. To avoid this, there is a need to find the aridity of software fault. This is achieved by finding the fitness of the evolutionaryAlgorithmic function. In this paper, we finalize the Genetic evolutionarynature of our Feature set with the help of Fitness Function. Feature Selection is the objective of the prediction model tocreate the underlying process of generalized data. The wide range of data like fault dataset, need the better objective function is obtained by feature selection, ranking, elimination and construction. In this paper, we focus on finding the fitness of the machine learning function which is used in the diagnostics of fault in the software for the better classification.

Machine Learning as a Key Technology for Implementing Automated Driving

ATZ worldwide ◽  
2021 ◽  
Vol 123 (5-6) ◽  
pp. 44-49
Author(s):  
Adrian Sonka ◽  
Silvia Thal ◽  
Roman Henze ◽  
Lars Seghorn
Keyword(s):  
Machine Learning ◽  
Automated Driving ◽  
Key Technology

Machine Learning for Automated Driving

ATZ worldwide ◽  
2019 ◽  
Vol 121 (12) ◽  
pp. 46-49
Author(s):  
Peter Schiekofer ◽  
Yusuf Erdogan ◽  
Stefan Schindler ◽  
Markus Wendl
Keyword(s):  
Machine Learning ◽  
Automated Driving

scholarly journals Why drivers are frustrated: results from a diary study and focus groups

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Esther Bosch ◽  
Klas Ihme ◽  
Uwe Drewitz ◽  
Meike Jipp ◽  
Michael Oehl
Keyword(s):  
Machine Learning ◽  
Focus Groups ◽  
Diary Study ◽  
Automated Driving ◽  
Driver Behaviour ◽  
Coping Methods ◽  
Recent Developments ◽  
Mitigation Methods ◽  
User Focus ◽  
Shed Light

Abstract Introduction Designing emotion-aware systems has become a manageable aim through recent developments in computer vision and machine learning. In the context of driver behaviour, especially negative emotions like frustration have shifted into the focus of major car manufacturers. Recognition and mitigation of the same could lead to safer roads in manual and more comfort in automated driving. While frustration recognition and also general mitigation methods have been previously researched, the knowledge of reasons for frustration is necessary to offer targeted solutions for frustration mitigation. However, up to the present day, systematic investigations about reasons for frustration behind the wheel are lacking. Methods Therefore, in this work a combination of diary study and user focus groups was employed to shed light on reasons why humans become frustrated during driving. In addition, participants of the focus groups were asked for their usual coping methods with frustrating situations. Results It was revealed that the main reasons for frustration in driving are related to traffic, in-car reasons, self-inflicted causes, and weather. Coping strategies that drivers use in everyday life include cursing, distraction by media and thinking about something else, amongst others. This knowledge will help to design a frustration-aware system that monitors the driver’s environment according to the spectrum of frustration causes found in the research presented here.

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