Context-based cyclist path prediction using Recurrent Neural Networks

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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 languageEnglish
Title of host publicationProceedings IEEE Symposium Intelligent Vehicles (IV 2019)
Place of PublicationPiscataway, NJ, USA
ISBN (Electronic)978-1-7281-0560-4
Publication statusPublished - 2019
EventIEEE Intelligent Vehicles Symposium 2019 - Paris, France
Duration: 9 Jun 201912 Jun 2019


ConferenceIEEE Intelligent Vehicles Symposium 2019
Abbreviated titleIV 2019

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