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arXiv · 1911.12736

DiversityGAN: Diversity-Aware Vehicle Motion Prediction via Latent Semantic Sampling

Xin Huang·Stephen G. McGill·Jonathan A. DeCastro·Luke Fletcher·John J. Leonard·Brian C. Williams·Guy Rosman
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Abstract

Vehicle trajectory prediction is crucial for autonomous driving and advanced driver assistant systems. While existing approaches may sample from a predicted distribution of vehicle trajectories, they lack the ability to explore it -- a key ability for evaluating safety from a planning and verification perspective. In this work, we devise a novel approach for generating realistic and diverse vehicle trajectories. We extend the generative adversarial network (GAN) framework with a low-dimensional approximate semantic space, and shape that space to capture semantics such as merging and turning. We sample from this space in a way that mimics the predicted distribution, but allows us to control coverage of semantically distinct outcomes. We validate our approach on a publicly available dataset and show results that achieve state-of-the-art prediction performance, while providing improved coverage of the space of predicted trajectory semantics.

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Xin Huang, Stephen G. McGill, Jonathan A. DeCastro, Luke Fletcher, John J. Leonard, Brian C. Williams, Guy Rosman. 2019-11-28. DiversityGAN: Diversity-Aware Vehicle Motion Prediction via Latent Semantic Sampling. https://arxiv.org/abs/1911.12736

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