Teaching Vehicles to Anticipate: A Systematic Study on Probabilistic Behavior Prediction Using Large Data Sets

By observing their environment as well as other traffic participants, humans are enabled to drive road vehicles safely. Vehicle passengers, however, perceive a notable difference between non-experienced and experienced drivers. In particular, they may get the impression that the latter ones anticipate what will happen in the next few moments and consider these foresights in their driving behavior. To make the driving style of automated vehicles comparable to the one of human drivers with respect to comfort and perceived safety, the aforementioned anticipation skills need to become a built-in feature of self-driving vehicles. This article provides a systematic comparison of methods and strategies to generate this intention for self-driving cars using machine learning techniques. To implement and test these algorithms we use a large data set collected over more than 30000 km of highway driving and containing approximately 40000 real-world driving situations. We further show that it is possible to classify driving maneuvers upcoming within the next 5 s with an Area Under the ROC Curve (AUC) above 0.92 for all defined maneuver classes. This enables us to predict the lateral position with a prediction horizon of 5 s with a median lateral error of less than 0.21 m.Comment: the paper has been accepted for publication in IEEE Transcations on Intelligent Transportation Systems (T-ITS) 16 pages 13 figures 12 table

Towards Incorporating Contextual Knowledge into the Prediction of Driving Behavior

Predicting the behavior of surrounding traffic participants is crucial for advanced driver assistance systems and autonomous driving. Most researchers however do not consider contextual knowledge when predicting vehicle motion. Extending former studies, we investigate how predictions are affected by external conditions. To do so, we categorize different kinds of contextual information and provide a carefully chosen definition as well as examples for external conditions. More precisely, we investigate how a state-of-the-art approach for lateral motion prediction is influenced by one selected external condition, namely the traffic density. Our investigations demonstrate that this kind of information is highly relevant in order to improve the performance of prediction algorithms. Therefore, this study constitutes the first step towards the integration of such information into automated vehicles. Moreover, our motion prediction approach is evaluated based on the public highD data set showing a maneuver prediction performance with areas under the ROC curve above 97% and a median lateral prediction error of only 0.18m on a prediction horizon of 5s.Comment: the article has been accepted for publication during the 23rd IEEE Intelligent Transportation Systems Conference (ITSC), 7 pages, 6 figures, 1 tabl

CPD: Crowd-based Pothole Detection

Potholes and other damages of the road surface constitute a problem being as old as roads are. Still, potholes are widespread and affect the driving comfort of passengers as well as road safety. If one knew about the exact locations of potholes, it would be possible to repair them selectively or at least to warn drivers about them up to their repair. However, both scenarios require their detection and localization. For this purpose, we propose a crowd-based approach that enables as many of the vehicles already driving on our roads as possible to detect potholes and report them to a centralized back-end application. Whereas each single vehicle provides only limited and imprecise information, it is possible to determine these information more precisely when collecting them at a large scale. These more exact information may, for example, be used to warn following vehicles about potholes lying ahead to increase overall safety and comfort. In this work, this idea is examined and an offline executable version of the desired system is implemented. Additionally, the approach is evaluated with a large database of real-world sensor readings from a testing fleet and therefore its feasibility is proved. Our investigation shows that the suggested CPD approach is promising to bring customers a benefit by an improved driving comfort and higher road safety

A Fleet Learning Architecture for Enhanced Behavior Predictions during Challenging External Conditions

Already today, driver assistance systems help to make daily traffic more comfortable and safer. However, there are still situations that are quite rare but are hard to handle at the same time. In order to cope with these situations and to bridge the gap towards fully automated driving, it becomes necessary to not only collect enormous amounts of data but rather the right ones. This data can be used to develop and validate the systems through machine learning and simulation pipelines. Along this line this paper presents a fleet learning-based architecture that enables continuous improvements of systems predicting the movement of surrounding traffic participants. Moreover, the presented architecture is applied to a testing vehicle in order to prove the fundamental feasibility of the system. Finally, it is shown that the system collects meaningful data which are helpful to improve the underlying prediction systems.Comment: the article has been accepted for publication during the 2020 IEEE Symposium Series on Computational Intelligence (SSCI) within the IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (CIVTS), 7 pages, 6 figure

Predicting the Time Until a Vehicle Changes the Lane Using LSTM-based Recurrent Neural Networks

To plan safe and comfortable trajectories for automated vehicles on highways, accurate predictions of traffic situations are needed. So far, a lot of research effort has been spent on detecting lane change maneuvers rather than on estimating the point in time a lane change actually happens. In practice, however, this temporal information might be even more useful. This paper deals with the development of a system that accurately predicts the time to the next lane change of surrounding vehicles on highways using long short-term memory-based recurrent neural networks. An extensive evaluation based on a large real-world data set shows that our approach is able to make reliable predictions, even in the most challenging situations, with a root mean squared error around 0.7 seconds. Already 3.5 seconds prior to lane changes the predictions become highly accurate, showing a median error of less than 0.25 seconds. In summary, this article forms a fundamental step towards downstreamed highly accurate position predictions.Comment: the article has been accepted for publication in IEEE Robotics and Automation Letters (RA-L); the article has been submitted to RA-L with IEEE ICRA conference option; if the article will be presented during the conference will be decided independently; 8 pages, 5 figures, 6 table

PENGARUH TEKANAN KETAATAN DAN KOMPLEKSITAS TUGAS TERHADAP AUDIT JUDGMENT (Survey Terhadap Lima Kantor AkuntanPublik di Kota Bandung)

ABSTRAK Seperti yang kita ketahui bahwa seorang auditor dalam melakukan tugasnya membuat audit judgment dipengaruhi banyak faktor, baik bersifat teknis dan non teknis. Salah satu faktor non teknis adalah aspek perilaku individual. Aspek perilaku individu, sebagai salah satu faktor yang banyak mempengaruhi pembuatan audit judgment. Pada penelitian ini ada beberapa faktor yang mempengaruhi audit judgment yaitu tekanan ketaatan dan kompleksitas tugas. Dalam penelitian ini penullis ingin mengetahui sejauh mana “tekanan ketaatan dan kompleksitas tugas terhadap audit judgment”. Sedangkan tujuan dari penelitian ini adalah untuk mengetahui dan mempelajari tekanan ketaatan dan kompleksitas tugas terhadap audit judgment. Hipotesis yang diuji dalam penelitian ini adalah “ jika tekanan ketaatan dan kompleksitas tugas baik, maka audit judgment akan meningkat ( baik pula)”. Hipotesis ini berdasarkan asumsi bahwa tekanan ketaatan dan kompleksitas tugas berpengaruh terhadap audit judgment.dalam penelitian ini penulis menggunakan metode deskriptif asosiatif dengan pendekatan survey dan tes statistik. Penelitian ini terdiri dari atas variabel X1 dan X2 dan audit judgment sebagai veriabel Y atau variabel independen. Uji statistik dilakukan dengan mengolah data dari hasil jawaban kuesioner. Dalam penelitian ini, peulis menyebarkan angket kepada 5 Kantor Akuntan Publik di Kota Bandung khusunya untuk para auditor. Pengumpulan data dilakukan dengan cara penyebaran kuesioner yang telah diuji validitasnya dan reabilitasnya. Penelitian ini dilakukan di 5 KAP di Kota Bandung. Pengambilan sampel ini menggunakan purposive sampling berukuran 28 orang responden. Untuk uji hipotesis penelitian, penulis melakukannya dengan uji t untuk masing-masing variabel X1,X2, dan Y. Dari hasil uji tHitung tekanan ketaatan terhadap audit judgment tHitung =4,178>ttabel = 1.705 kompleksitas tugas terhadap audit judgment 5 tHitung = 3.364 > ttabel = 1,705. Maka, dari hasil uji hipotesis tersebut penulis menyimpulkan bahwa hipotesis penelitian diterima (Ho ditolak, Ha diterima) artinya terdapat pengaruh antara terkanan ketaatan terhadap audit judgment dan kompleksitas tugas terhadap audit judgment Untuk mencari besarnya pengaruh Tekanan ketaatan dan Kompleksitas Tugas terhadap Audit Judgment secara simultan penulis melakukannya dengan uji f dengan koefisien determinasi (KD). Dari hasil uji fhitung dan > f table yaitu 16,182>3,370. Kata kunci : Tekanan Ketaatan dan Kompleksitas tugas Terhadap Audit Judgmen

The Luxembourg Parkinson’s Study: A Comprehensive Approach for Stratification and Early Diagnosis

While genetic advances have successfully defined part of the complexity in Parkinson’s disease (PD), the clinical characterization of phenotypes remains challenging. Therapeutic trials and cohort studies typically include patients with earlier disease stages and exclude comorbidities, thus ignoring a substantial part of the real-world PD population. To account for these limitations, we implemented the Luxembourg PD study as a comprehensive clinical, molecular and device-based approach including patients with typical PD and atypical parkinsonism, irrespective of their disease stage, age, comorbidities, or linguistic background. To provide a large, longitudinally followed, and deeply phenotyped set of patients and controls for clinical and fundamental research on PD, we implemented an open-source digital platform that can be harmonized with international PD cohort studies. Our interests also reflect Luxembourg-specific areas of PD research, including vision, gait, and cognition. This effort is flanked by comprehensive biosampling efforts assuring high quality and sustained availability of body liquids and tissue biopsies. We provide evidence for the feasibility of such a cohort program with deep phenotyping and high quality biosampling on parkinsonism in an environment with structural specificities and alert the international research community to our willingness to collaborate with other centers. The combination of advanced clinical phenotyping approaches including device-based assessment will create a comprehensive assessment of the disease and its variants, its interaction with comorbidities and its progression. We envision the Luxembourg Parkinson’s study as an important research platform for defining early diagnosis and progression markers that translate into stratified treatment approaches

Knowledge discovery in databases with association rules

Die Datenanalyse mittels Assoziationsregeln ist eines der am häufigsten eingesetzten Data Mining-Verfahren und geht auf Arbeiten der Forschergruppe um Rakesh Agrawal am Forschungszentrum der IBM in Almaden, Kalifornien, USA, zurück. Dort wurden Anfang der neunziger Jahre Assoziationsregeln als Methode der Abhängigkeitsanalyse eingeführt und erste Algorithmen zur Assoziationsregelgenerierung entwickelt. In der vorliegenden Arbeit werden die etablierten Verfahren zur Generierung von Assoziationsregeln analysiert und systematisiert, wodurch ein besseres Verständnis der in der Literatur bisher nicht im Zusammenhang dargestellten Verfahren möglich wird. In Verbindung mit einer umfassenden Evaluierung der Laufzeiten und des Speicherbedarfs führt dies zu einer Neubewertung der Ansätze. Darauf aufbauend werden neue Verfahren zur Generierung von Assoziationsregeln abgeleitet. Diese beruhen auf einer optimierten Beschneidung des Suchraums, auf einem hybriden Vorgehen und auf der Einbeziehung einer eventuell vorhandenen Taxonomie. Im Rahmen einer Evaluierung erreichen die neu entwickelten Algorithmen in vielen Experimenten wesentlich kürzere Laufzeiten und einen geringeren Speicherbedarf als die bisherigen Algorithmen. Die vorgeschlagenen Verfahren sind insgesamt deutlich effizienter als die bisher bekannten Ansätze, insbesondere falls eine Taxonomie zu den Analysedaten zur Verfügung steht. In Verbindung mit der Effizienz der Verfahren steht die Integration der Regelgenerierung in den Wissensentdeckungsprozeß. Ein iterativer und interaktiver Prozeß setzt kurze Antwortzeiten voraus, die von den Verfahren auf großen Datenmengen oft nicht erreicht werden können. Für diese von algorithmischen Aspekten in den Hintergrund gedrängte Problematik wird im Rahmen der vorliegenden Arbeit ein Regelcache als Lösung vorgeschlagen. Der Regelcache ist so aufgebaut, daß dieser auch für viele Anfragen gültig bleibt, die Selektionen der zugrundeliegenden Datensätze beinhalten, und dadurch für solche Anfragen nicht neu initialisiert werden muß.Data analysis using association rules belongs to the fundamental data mining approaches and was introduced as a method aiming at dependency analysis by Rakesh Agrawal at the IBM Research Center in Almaden, California, USA. In this thesis, the established algorithms for association rule mining are analyzed and systemized. The chief goal is to learn more about the algorithms that thus far have not been described coherently. Together with the results of an exhaustive evaluation of runtime and memory usage, this leads to a changed appreciation of the different approaches. On the basis of the results obtained, new algorithms for the generation of association rules are developed. These algorithms rely on an optimized pruning of the search space, a hybrid approach, and the incorporation of a potentially available taxonomy. In a multitude of experiments carried out during a comprehensive evaluation, the new algorithms achieved not only much shorter runtimes but also a greatly reduced memory usage as compared to established approaches. All in all, the algorithms introduced are much more efficient than conventional approaches, in particular when a taxonomy on the data is available. Aligned with the efficiency of the algorithms is the aspect of integrating the rule generation into the process of knowledge discovery. An iterative and interactive process assumes short response times that cannot be reached by the algorithms on very huge datasets. For this often neglected problem, an extended rule cache is proposed. This rule cache stays valid even for many mining queries that include selections of the underlying data. Hence, for such queries, the cache does not need to be reinitialized