137 citations · 242 across the 5 of their papers we have counts for
5 papers
StopNet: Scalable Trajectory and Occupancy Prediction for Urban Autonomous Driving
Jinkyu Kim, Reza Mahjourian, Scott Ettinger +4
We introduce a motion forecasting (behavior prediction) method that meets the latency requirements for autonomous driving in dense urban environments without sacrificing accuracy.…
Attentional Bottleneck: Towards an Interpretable Deep Driving Network
Jinkyu Kim, Mayank Bansal
Deep neural networks are a key component of behavior prediction and motion generation for self-driving cars. One of their main drawbacks is a lack of transparency: they should prov…
MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction
Yuning Chai, Benjamin Sapp, Mayank Bansal +1
Predicting human behavior is a difficult and crucial task required for motion planning. It is challenging in large part due to the highly uncertain and multi-modal set of possible…
ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst
Mayank Bansal, Alex Krizhevsky, Abhijit Ogale
Our goal is to train a policy for autonomous driving via imitation learning that is robust enough to drive a real vehicle. We find that standard behavior cloning is insufficient fo…
Geometric Polynomial Constraints in Higher-Order Graph Matching
Mayank Bansal, Kostas Daniilidis
Correspondence is a ubiquitous problem in computer vision and graph matching has been a natural way to formalize correspondence as an optimization problem. Recently, graph matching…