DiversityGAN: Diversity-Aware Vehicle Motion Prediction via Latent Semantic Sampling
This addresses the need for diverse trajectory prediction in autonomous driving systems, though it is an incremental improvement over existing GAN-based methods.
The paper tackles the problem of generating diverse vehicle trajectories for autonomous driving by developing DiversityGAN, which extends GANs with a low-dimensional semantic space to capture maneuvers like merging and turning. The results show state-of-the-art prediction performance and improved coverage of trajectory semantics on a public dataset.
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.