2 citations · 5 across the 4 of their papers we have counts for
11 papers
KING: Generating Safety-Critical Driving Scenarios for Robust Imitation via Kinematics Gradients
Niklas Hanselmann, Katrin Renz, Kashyap Chitta +2
Simulators offer the possibility of safe, low-cost development of self-driving systems. However, current driving simulators exhibit naïve behavior models for background traffic. Ha…
Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers
Apratim Bhattacharyya, Daniel Olmeda Reino, Mario Fritz +1
Accurate prediction of pedestrian and bicyclist paths is integral to the development of reliable autonomous vehicles in dense urban environments. The interactions between vehicle a…
Haar Wavelet based Block Autoregressive Flows for Trajectories
Apratim Bhattacharyya, Christoph-Nikolas Straehle, Mario Fritz +1
Prediction of trajectories such as that of pedestrians is crucial to the performance of autonomous agents. While previous works have leveraged conditional generative models like GA…
Normalizing Flows with Multi-Scale Autoregressive Priors
Shweta Mahajan, Apratim Bhattacharyya, Mario Fritz +2
Flow-based generative models are an important class of exact inference models that admit efficient inference and sampling for image synthesis. Owing to the efficiency constraints o…
"Best-of-Many-Samples" Distribution Matching
Apratim Bhattacharyya, Mario Fritz, Bernt Schiele
Generative Adversarial Networks (GANs) can achieve state-of-the-art sample quality in generative modelling tasks but suffer from the mode collapse problem. Variational Autoencoders…
Conditional Flow Variational Autoencoders for Structured Sequence Prediction
Apratim Bhattacharyya, Michael Hanselmann, Mario Fritz +2
Prediction of future states of the environment and interacting agents is a key competence required for autonomous agents to operate successfully in the real world. Prior work for s…