13 citations · 17 across the 5 of their papers we have counts for
5 papers
Just Label What You Need: Fine-Grained Active Selection for Perception and Prediction through Partially Labeled Scenes
Sean Segal, Nishanth Kumar, Sergio Casas +4
Self-driving vehicles must perceive and predict the future positions of nearby actors in order to avoid collisions and drive safely. A learned deep learning module is often respons…
Diverse Complexity Measures for Dataset Curation in Self-driving
Abbas Sadat, Sean Segal, Sergio Casas +4
Modern self-driving autonomy systems heavily rely on deep learning. As a consequence, their performance is influenced significantly by the quality and richness of the training data…
Universal Embeddings for Spatio-Temporal Tagging of Self-Driving Logs
Sean Segal, Eric Kee, Wenjie Luo +3
In this paper, we tackle the problem of spatio-temporal tagging of self-driving scenes from raw sensor data. Our approach learns a universal embedding for all tags, enabling effici…
End-to-end Contextual Perception and Prediction with Interaction Transformer
Lingyun Luke Li, Bin Yang, Ming Liang +4
In this paper, we tackle the problem of detecting objects in 3D and forecasting their future motion in the context of self-driving. Towards this goal, we design a novel approach th…
Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction
Ajay Jain, Sergio Casas, Renjie Liao +4
Self-driving vehicles plan around both static and dynamic objects, applying predictive models of behavior to estimate future locations of the objects in the environment. However, f…