2 research outputs found
DiversityGAN: Diversity-Aware Vehicle Motion Prediction via Latent Semantic Sampling
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.Comment: 8 pages, 5 figures, 1 tabl
Hybrid control and learning with coresets for autonomous vehicles
© 2017 IEEE. Modern autonomous systems such as driverless vehicles need to safely operate in a wide range of conditions. A potential solution is to employ a hybrid systems approach, where safety is guaranteed in each individual mode within the system. This offsets complexity and responsibility from the individual controllers onto the complexity of determining discrete mode transitions. In this work we propose an efficient framework based on recursive neural networks and coreset data summarization to learn the transitions between an arbitrary number of controller modes that can have arbitrary complexity. Our approach allows us to efficiently gather annotation data from the large-scale datasets that are required to train such hybrid nonlinear systems to be safe under all operating conditions, favoring underexplored parts of the data. We demonstrate the construction of the embedding, and efficient detection of switching points for autonomous and non-autonomous car data. We further show how our approach enables efficient sampling of training data, to further improve either our embedding or the controllers