Open access
Autor(in)
Datum
2020Typ
- Doctoral Thesis
ETH Bibliographie
yes
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Abstract
Navigating through densely populated urban areas is one of the most important
challenges for self-driving vehicles. Accurate predictions are required to enable
safe and efficient interactions with other road users. Pedestrians in particular
pose major problems for current state of the art prediction systems. Apart from
well-understood short-term predictions, used for Automatic Emergency Braking
Systems, long-term predictions remain largely unresolved.
In this thesis, we aim to advance pedestrian prediction systems to enable human-
understandable automated driving. In view of a vehicle-centred road infrastructure
with high vehicle speeds and scarce pedestrian crossings, early detection of pedestrian
intentions and movements are the key to enabling such behaviour. These detections
can enable automated vehicles to perform light brake manoeuvres at an early stage
in order to let a pedestrian pass. This can eliminate the need to stop, which could
significantly improve traffic flow and at the same time increase overall safety.
Long-term full trajectory predictions are both costly and error-prone. Therefore
we propose a hierarchical prediction system that splits the prediction into multiple
simplified sub-problems using domain knowledge. Each sub-problem is designed
to predict a meaningful part of pedestrian movement and to detect and remove
pedestrians that are irrelevant for the current scenario as early as possible. Utilizing
the given road geometry to identify crosswalks we first predict the pedestrians’
hidden intent to cross the road. For all crossing pedestrians we then propose a
sparse motion prediction, providing a small set of key figures instead of a full
trajectory. We claim that these domain-specific key figures, namely a time-to-
cross and designated crossing point, are more than sufficient to describe future
pedestrian motions for the planning system of an automated vehicle. To overcome
problems from over-confident single value predictions we propose to utilize Quantile
Regression techniques to predict reasonable uncertainties.
Our evaluations show that we are able to robustly classify the pedestrians’ hidden
intent using both standard and deep learning algorithms. Additionally we show
that our hierarchical prediction system, including the sparse motion prediction,
is suitable for a real-time system integration. With our large real-world dataset,
featuring recordings from different crosswalks and days, we provide an evaluation
regarding prediction accuracy, computational load and generalizability. During this
analysis, we also found indications that it might be possible to transfer trained
models to previously unseen pedestrian crossings if the road geometry has at least
approximately the same pavement dimensions. Furthermore, we show how our
sparse motion prediction can be integrated into a situation-based planning approach
to allow safe and efficient real-time interactions with other traffic participants. For
this we evaluate different interaction scenarios regarding safety, time efficiency and
comfort impairment. We were able to show that in most of the scenarios it is
possible to minimize movement jerks and eliminate the need to stop. The overall
performance is only limited by very high traffic densities. Mehr anzeigen
Persistenter Link
https://doi.org/10.3929/ethz-b-000418654Publikationsstatus
publishedExterne Links
Printexemplar via ETH-Bibliothek suchen
Verlag
ETH ZurichThema
Automated driving; Prediction; Pedestrian; MACHINE LEARNING (ARTIFICIAL INTELLIGENCE); Deep LearningOrganisationseinheit
03737 - Siegwart, Roland Y. / Siegwart, Roland Y.
ETH Bibliographie
yes
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