Methodology of Driving Scenario Extraction Based on Vehicle Trajectory
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Program
Infrastructure and environmental engineering (MPIEE), MSc
Publicerad
2023
Författare
WANG, XINHAO
WU, HUAXIANG
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
In the development of autonomous drive functions, in-house testing and simulation are
indispensable, which are very dependent on large amounts of data. Unfortunately,
labeling the data and picking up useful scenarios are difficult since there is so much
redundant information that is useless. This is why the thesis is born.
Specifically, the thesis aims to mine as many interesting driving scenarios as possible
with the help of rule-based methods and clustering methods and to find a better way to
do scenario extraction from data. Based on the open-source data set NuScenes, the rule
based method is first used but with less than satisfactory results due to too little
information. The total number of objects cut in, fast approaching, and object cut-out
scenarios is around 81, which is relatively small compared to the entire data set, and the
classification precision rate is about 50%. Then, clustering models including
HDBSCAN, K-means, GMM, Hierarchical clustering, and DBSCAN are tried. On the
basis of these models, the work done includes but is not limited to cross-comparing the
impact of distance matrices obtained from DTW and LCSS, different partitioning
settings, and whether to scale the input data. Certainly, hyperparameter adjustment is
also done by the Optuna algorithm. The results from the rule-based method are used to
generate metrics and objectives for clustering models. Consequently, the best 𝐹2-score
is from the K-means model, which is 0.1688. For comparison, the same method and
idea are used on another data set named NGSIM. It turns out that the rule-based results
are very good, with the precision rate reaching up to 90% because of the rich
information provided like lane ID. Correspondingly, the clustering results improved a
lot, reaching 0.4653 for the DBSCAN model, even though it is still far from the ideal
result.
In summary, the external lane and object information is quite crucial for rule-based
method classifying scenarios. Besides, the rule-based method is more efficient than
clustering in scenario classification and clustering models may not be applicable to
driving scenario extraction based on relative trajectory classification.
Beskrivning
Ämne/nyckelord
NuScenes, NGSIM, rule-based, clustering, scenario extraction.