Abstract
This paper proposes a Recurrent Neural Network (RNN) for cyclist path prediction to learn the effect of contextual cues on the behavior directly in an end- to-end approach, removing the need for any annotations. The proposed RNN incorporates three distinct contextual cues: one related to actions of the cyclist, one related to the location of the cyclist on the road, and one related to the interaction between the cyclist and the egovehicle. The RNN predicts a Gaussian distribution over the future position of the cyclist one second into the future with a higher accuracy, compared to a current state-of-the-art model that is based on dynamic mode annotations, where our model attains an average prediction error of 33 cm one second into the future.
| Original language | English |
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| Title of host publication | Proceedings IEEE Symposium Intelligent Vehicles (IV 2019) |
| Place of Publication | Piscataway, NJ, USA |
| Publisher | IEEE |
| Pages | 824-830 |
| ISBN (Electronic) | 978-1-7281-0560-4 |
| DOIs | |
| Publication status | Published - 2019 |
| Event | IEEE Intelligent Vehicles Symposium 2019 - Paris, France Duration: 9 Jun 2019 → 12 Jun 2019 |
Conference
| Conference | IEEE Intelligent Vehicles Symposium 2019 |
|---|---|
| Abbreviated title | IV 2019 |
| Country/Territory | France |
| City | Paris |
| Period | 9/06/19 → 12/06/19 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.