Attention-based lane change prediction

Scheel O, Nagaraja NS, Schwarz L, Navab N, Tombari F (2019)

Publication Type: Conference contribution

Publication year: 2019

Journal

Proceedings - IEEE International Conference on Robotics and Automation Institute of Electrical and Electronics Engineers

Publisher: Institute of Electrical and Electronics Engineers Inc.

Book Volume: 2019-May

Pages Range: 8655-8661

Conference Proceedings Title: Proceedings - IEEE International Conference on Robotics and Automation

Event location: Montreal, QC, CAN

ISBN: 9781538660263

DOI: 10.1109/ICRA.2019.8793648

Abstract

Lane change prediction of surrounding vehicles is a key building block of path planning. The focus has been on increasing the accuracy of prediction by posing it purely as a function estimation problem at the cost of model understandability. However, the efficacy of any lane change prediction model can be improved when both corner and failure cases are humanly understandable. We propose an attention-based recurrent model to tackle both understandability and prediction quality. We also propose metrics which reflect the discomfort felt by the driver. We show encouraging results on a publicly available dataset and proprietary fleet data.

Involved external institutions

BMW AG - Bayerische Motoren Werke / BMW Group DE Germany (DE) Technische Universität München (TUM) DE Germany (DE)

How to cite

APA:

Scheel, O., Nagaraja, N.S., Schwarz, L., Navab, N., & Tombari, F. (2019). Attention-based lane change prediction. In Proceedings - IEEE International Conference on Robotics and Automation (pp. 8655-8661). Montreal, QC, CAN: Institute of Electrical and Electronics Engineers Inc..

MLA:

Scheel, Oliver, et al. "Attention-based lane change prediction." Proceedings of the 2019 International Conference on Robotics and Automation, ICRA 2019, Montreal, QC, CAN Institute of Electrical and Electronics Engineers Inc., 2019. 8655-8661.

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