dc.rights.license | CC-BY-NC-ND | |
dc.contributor.advisor | Siebes, Arno P.J.M. | |
dc.contributor.advisor | Feelders, Ad J. | |
dc.contributor.author | Markotic, P.H.J. | |
dc.date.accessioned | 2018-10-08T17:01:11Z | |
dc.date.available | 2018-10-08T17:01:11Z | |
dc.date.issued | 2017 | |
dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/38166 | |
dc.description.abstract | The continuous development and integration of Automated Driving Systems (ADS)
leads to complex systems. Such systems need to be tested and validated thoroughly
for all situations these systems may encounter on the road, to assure their safety
and reliability. Test drives with ADS require millions of operational hours which
is infeasible. TNO proposes to acquire micro-traffic data (data collected on the
level of the individual vehicles) to generate real-world scenarios (situations) for
testing and validating ADS. Such scenarios are resembled by typical patterns in the
data. To achieve extraction and classification of scenarios from micro-traffic traffic
data, TNO already developed knowledge-driven, rule-based techniques. However,
a drawback of these techniques is the loss of generalization. This work proposes
an unsupervised data mining approach to mine events from real-world traffic data
to overcome this limitation. After decimation and discretization of the data, we
combine Frequent Itemset Mining and Frequent Sequence Mining over multiple
sensor outputs in the form of data streams for recognizing patterns that represent
real-world traffic scenarios. The method shows that different configurations can
result in different generalizations to satisfy the need of the expert, as different levels
of abstraction can be desired by the user. Finally by conducting experiments we
conclude which configuration provides the most desirable result in finding events
for longitudinal movement. | |
dc.description.sponsorship | Utrecht University | |
dc.format.extent | 2914077 | |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | |
dc.title | Pattern Recognition for Scenario Detection in Real-World Traffic Data | |
dc.type.content | Master Thesis | |
dc.rights.accessrights | Open Access | |
dc.subject.keywords | frequent,itemset,mining,multivariate,stream,data,tno | |
dc.subject.courseuu | Artificial Intelligence | |