7 citations · 8 across the 3 of their papers we have counts for
3 papers
Mitigating Covariate Shift in Imitation Learning for Autonomous Vehicles Using Latent Space Generative World Models
Alexander Popov, Alperen Degirmenci, David Wehr +9
We propose the use of latent space generative world models to address the covariate shift problem in autonomous driving. A world model is a neural network capable of predicting an…
NVRadarNet: Real-Time Radar Obstacle and Free Space Detection for Autonomous Driving
Alexander Popov, Patrik Gebhardt, Ke Chen +5
Detecting obstacles is crucial for safe and efficient autonomous driving. To this end, we present NVRadarNet, a deep neural network (DNN) that detects dynamic obstacles and drivabl…
MVLidarNet: Real-Time Multi-Class Scene Understanding for Autonomous Driving Using Multiple Views
Ke Chen, Ryan Oldja, Nikolai Smolyanskiy +5
Autonomous driving requires the inference of actionable information such as detecting and classifying objects, and determining the drivable space. To this end, we present Multi-Vie…