Högskolan i Skövde

Driver intention recognition (DIR) studies increasingly rely on deep neural networks. Deep neural networks have achieved top performance for many different tasks. However, apart from image classifications and semantic segmentation for mobile phones, it is not a common practice for components of advanced driver assistance systems to explicitly analyze the complexity and performance of the network’s architecture. Therefore, this paper applies neural architecture search to investigate the effects of the deep neural network architecture on a real-world safety critical application with limited computational capabilities. We explore a pre-defined search space for three deep neural network layer types that are capable to handle sequential data (a long-short term memory, temporal convolution, and a time-series transformer layer), and the influence of different data fusion strategies on the driver intention recognition performance. A set of eight search strategies are evaluated for two driver intention recognition datasets. For the two datasets, we observed that there is no search strategy clearly sampling better deep neural network architectures. However, performing an architecture search improves the model performance compared to the original manually designed networks. Furthermore, we observe no relation between increased model complexity and better driver intention recognition performance. The result indicate that multiple architectures can yield similar performance, regardless of the deep neural network layer type or fusion strategy. However, the optimal complexity, layer type and fusion remain unknown upfront.

The health of a machine tool directly affects its ability to produce components with high precision. Therefore, monitoring and diagnosing early faults can enhance its reliability resulting in an improvement in manufacturing throughput and overall product quality. This paper concerns condition monitoring of the ballscrew drive, a machine tool component that transforms rotary motion of the drive shaft into linear motion of the work table along the guideways. The degradation of the ballscrew drive is often characterized by backlash, which results in imprecise linear motion and, therefore, affects the position of guideways during machining operations. Many physical characteristics of the ballscrew drive, such as required torque, viscous friction, and Coulomb friction, change with the degradation of the ballscrew during its lifetime. The paper proposes a condition monitoring methodology consisting of four main steps: data collection, data preprocessing and feature engineering, model building, and anomaly detection. The machine tool drive system is operated under no-load condition at regular intervals to capture health data using Siemens Analyze MyCondition instrumentation. Subsequently, the data is preprocessed and features are extracted from raw signals using the wavelet transform approach. The unsupervised machine learning technique, principal component analysis, is used to reduce the dimensionality of the dataset and find feature combinations that capture most of the variation in the data. Next, Hotelling’s T2 statistic is computed for each sample on a rolling basis, and anomalous behavior is detected based consistent deviations beyond the moving median of Hotelling’s T2 statistic. The proposed methodology is applied on condition monitoring data from a Swedish automotive manufacturer and the health assessments are validated against backlash measurements obtained from a different conditional monitoring test. This shows that the health status of a ballscrew can be derived directly from its physical characteristics.

Driver intention recognition (DIR) methods mostly rely on deep neural networks (DNNs). To use DNNs in asafety-critical real-world environment it is essential to quantify how confident the model is about the producedpredictions. Therefore, this study evaluates the performance and calibration of a temporal convolutionalnetwork (TCN) for multiple probabilistic deep learning (PDL) methods (Bayes-by-Backprop, Monte-Carlodropout, Deep ensembles, Stochastic Weight averaging - Gaussian, Multi SWA-G, cyclic Stochastic GradientHamiltonian Monte Carlo). Notably, we formalize an approach that combines optimization-based pre-trainingwith Hamiltonian Monte-Carlo (PT-HMC) sampling, aiming to leverage the strengths of both techniques. Ouranalysis, conducted on two pre-processed open-source DIR datasets, reveals that PT-HMC not only matchesbut occasionally surpasses the performance of existing PDL methods. One of the remaining challenges thatprohibits the integration of a PDL-based DIR system into an actual car is the computational requirements toperform inference. Therefore, future work could focus on optimizing PDL methods to be more computationallyefficient without sacrificing performance or the ability to estimate uncertainties.

Climate change has impacted global temperatures, triggering extreme weather and adverse environmental effects. In Sweden, these changes have caused shifts in weather patterns, leading to disruptions in infrastructure. This, in turn, has influenced water turbidity levels, negatively impacting water quality. To tackle these issues, a study was conducted using machine learning to predict turbidity with six meteorological variables collected for two years. Our preliminary research showed a substantial influence of seasonal changes on water turbidity, especially air temperature. Identifying supporting indicators such as lagged features is crucial and considerably improved the turbidity prediction performance for two of the machine learning models used. However, the study also identified challenges like data collection and uncertainty issues. We recommend improving data collection quality with higher frequency, minimizing geographical gaps between data collection points, sharing calibration assumptions, checking the sensors regularly, and accounting for data anomalies. Understanding these challenges and their potential implications could lead to more methodological enhancements.

Dataanalys och det närbesläktade området artificiell intelligens utvecklas snabbt och är av stort intresse för ledningsområdet då de kan erbjuda åtråvärda egenskaper som automatiserad hantering av stora datamängder och modeller för att tolka ny data. Den här rapporten omfattar en avskanning av fyra utvalda delområden: preskriptiv analys, osäkerhetshantering, förklarbarhet (XAI) och systemperspektiv. Rapporten innehåller dessutom en uppskattning av den framtida utvecklingen av delområdena och en jämförelse med avseende på deras mognadsgrad och relevans.

Data analysis and the closely related field of artificial intelligence are developing rapidly and are of great interest to the command and control field as they can offer desirable features such as automated handling of large data sets and models for interpreting new data. This report includes a scan of four selected sub-areas: prescriptive analysis, uncertainty management, explainability (XAI) and systems perspective. The report also contains an estimate of the future development of the sub-areas and a comparison with respect to their degree of maturity and relevance.

Real-world applications of artificial intelligence that can potentially harm human beings should be able to express uncertainty about the made predictions. Probabilistic deep learning (DL) methods (e.g., variational inference [VI], VI last layer [VI-LL], Monte-Carlo [MC] dropout, stochastic weight averaging - Gaussian [SWA-G], and deep ensembles) can produce a predictive uncertainty but require expensive MC sampling techniques. Therefore, we evaluated if the probabilistic DL methods are uncertain when making incorrect predictions for an open-source driver intention recognition dataset and if a surrogate DL model can reproduce the uncertainty estimates. We found that all probabilistic DL methods are significantly more uncertain when making incorrect predictions at test time, but there are still instances where the models are very certain but completely incorrect. The surrogate DL models trained on the MC dropout and VI uncertainty estimates were capable of reproducing a significantly higher uncertainty estimate when making incorrect predictions.

To ensure reliable network performance, anomaly detection is an important part of the telecommunication operators’ work. This includes that operators need to timely intervene with the network, should they encounter indications of network performance degradation. In this paper, we describe the results of an initial experiment for anomaly detection with regard to network performance, using topic modeling on base station run-time variable data collected from live Radio Access Networks (RANs). The results show that topic modeling clusters semantically related data in the same way as human experts would and that the anomalies in our test cases could be identified in latent Dirichlet allocation (LDA) topic models. Our experiment further reveals which information provided by the topic model is particularly usable to support human anomaly detection in this application domain.

Every year worldwide more than one million people die and a further 50 million people are injured in traffic accidents. Therefore, the development of car safety features that actively support the driver in preventing accidents, is of utmost importance to reduce the number of injuries and fatalities. However, to establish this support it is necessary that the advanced driver assistance system (ADAS) understands the driver’s intended behavior in advance. The growing variety of sensors available for vehicles together with improved computer vision techniques, hence led to increased research directed towards inferring the driver’s intentions. This article reviews 64 driver intention recognition studies with regard to the maneuvers considered, the driving features included, the AI methods utilized, the achieved performance within the presented experiments, and the open challenges identified by the respected researchers. The article provides a high level analysis of the current technology readiness level of driver intention recognition technology to address the challenges to enable reliable driver intention recognition, such as the system architecture, implementation, and the purpose of the technology.

A definition of information fusion (IF) as a field of research can benefit researchers within the field, who may use such a definition when motivating their own work and evaluating the contributions of others. Moreover, it can enable researchers and practitioners outside the field to more easily relate their own work to the field and more easily understand the scope of IF techniques and methods. Based on strengths and weaknesses of existing definitions, a definition is proposed that is argued to effectively fulfill the requirements that can be put on a definition of IF as a field of research. Although the proposed definition aims to be precise, it does not fully capture the richness and versatility of the IF field. To address that limitation, we highlight some topics to explore the scope of IF, covering the systems perspective of IF and its relation to ma-chine learning, optimization, robot behavior, opinion aggregation, and databases.

During the transition phase of traditional manufacturing companies towards smart factories, they are likely to experience challenges like lack of prehistoric data recordings or events on which the machine learning models need to be trained. This paper introduces a novel approach of artificially induced anomalies for data labelling. Moreover, for newly installed systems or a setup, which has not seen any kind of malfunction yet, the combination of artificially induced anomalies by experiments and machine learning model help to proactively prepare for any kind of future hindrance of the production systems. Two experiments were performed for detection of air leakage. The first one was designed to identify 'sensitive feature' and understand the behaviour of the sensor readings with respect to different state of the machine. The second one was performed to capture more data points pertaining to leaking state of machine on a normal production day since the first one was conducted on a maintenance break). RUSBoosted bagged trees model was built as a supervised machine-learning model, which was yielded 98.73% accuracy, 99.40% precision, recall of 99.21%, and F1 score of 99.30% on test data for detecting pneumatic leakage. As a conclusion, previously unknown hidden patterns and insights regarding temperature feature along with a standardized and systematic methodology are the important deliverables of this study.