Prediction of Road User Behavior
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2018-06-28
Language
en
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Abstract
The workspace of advanced driver assistance systems is getting more complicated as they
start targeting more complex environments. For ensuring the safety performance of these
systems, recognizing the surrounding traffic behavior is not sufficient: the prediction of the
behavior of other road users is becoming essential. The main objective of this study is to
design the system that can predict the trajectories of road users within the 3-second
prediction horizon. Furthermore, the objective also includes making the toolchain for data
processing, training, and evaluation of the trajectory prediction system. A deep machine
learning method was proposed for the problem of trajectory prediction. The solution was
comprehensively studied on the naturalistic driving data that was available at TNO. The
proposed solution concept is proven as an efficient method for predicting the road user
trajectories. Although there is the clear evidence that the model should be trained with
more data, the study shows that the model has learned important properties, and it is
consistent in all the performance measures. The models are evaluated using five metrics:
position accuracy; lane change precision, recall, F1 score, and end lane accuracy. The
proposed method sufficiently outperformed linear extrapolation in the lane change
prediction and showed similar to linear extrapolation position accuracy. Moreover, the
method correctly models prediction variance that can be viewed as the quality estimation
of the prediction a priori.
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Faculteit der Sociale Wetenschappen