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Rules of the Road: Predicting Driving Behavior With a Convolutional Model of Semantic Interactions

Hong, Joey and Sapp, Benjamin and Philbin, James (2019) Rules of the Road: Predicting Driving Behavior With a Convolutional Model of Semantic Interactions. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE , Piscataway, NJ, pp. 8446-8454. ISBN 978-1-7281-3293-8. https://resolver.caltech.edu/CaltechAUTHORS:20200116-142937887

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Abstract

We focus on the problem of predicting future states of entities in complex, real-world driving scenarios. Previous research has approached this problem via low-level signals to predict short time horizons, and has not addressed how to leverage key assets relied upon heavily by industry self-driving systems: (1) large 3D perception efforts which provide highly accurate 3D states of agents with rich attributes, and (2) detailed and accurate semantic maps of the environment (lanes, traffic lights, crosswalks, etc). We present a unified representation which encodes such high-level semantic information in a spatial grid, allowing the use of deep convolutional models to fuse complex scene context. This enables learning entity-entity and entity-environment interactions with simple, feed-forward computations in each timestep within an overall temporal model of an agent's behavior. We propose different ways of modelling the future as a distribution over future states using standard supervised learning. We introduce a novel dataset providing industry-grade rich perception and semantic inputs, and empirically show we can effectively learn fundamentals of driving behavior.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/cvpr.2019.00865DOIArticle
https://arxiv.org/abs/1906.08945arXivDiscussion Paper
Additional Information:© 2019 IEEE. The authors would also like to thank and acknowledge Kai Wang for his work on this project. Kai has been instrumental in coordinating the final version of the paper and preparing the dataset for release.
Record Number:CaltechAUTHORS:20200116-142937887
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200116-142937887
Official Citation:J. Hong, B. Sapp and J. Philbin, "Rules of the Road: Predicting Driving Behavior With a Convolutional Model of Semantic Interactions," 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019, pp. 8446-8454. doi: 10.1109/CVPR.2019.00865
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:100769
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:16 Jan 2020 22:37
Last Modified:16 Jan 2020 23:30

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