7 citations · 14 across the 7 of their papers we have counts for
9 papers
Reward Observability and the Limits of Offline Checkpoint Selection in RSSM World Models
Nikolai Smolyanskiy, Jonathan Shock
We study the closed-loop properties of a recurrent state-space model (RSSM) world model trained on human demonstrations in Gymnasium's LunarLander-v3. We use the trained world mode…
Planning-aligned Token Compression for Long-Context Autonomous Driving
Zhixuan Liang, Yuxiao Chen, Yurong You +12
Monolithic vision-action models represent an emerging paradigm in autonomous driving. However, this architecture produces token sequences that quickly exceed real-time computationa…
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…
PredictionNet: Real-Time Joint Probabilistic Traffic Prediction for Planning, Control, and Simulation
Alexey Kamenev, Lirui Wang, Ollin Boer Bohan +6
Predicting the future motion of traffic agents is crucial for safe and efficient autonomous driving. To this end, we present PredictionNet, a deep neural network (DNN) that predict…
Deep Two-View Structure-from-Motion Revisited
Jianyuan Wang, Yiran Zhong, Yuchao Dai +4
Two-view structure-from-motion (SfM) is the cornerstone of 3D reconstruction and visual SLAM. Existing deep learning-based approaches formulate the problem by either recovering abs…
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…
On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network Approach
Nikolai Smolyanskiy, Alexey Kamenev, Stan Birchfield
We revisit the problem of visual depth estimation in the context of autonomous vehicles. Despite the progress on monocular depth estimation in recent years, we show that the gap be…
Toward Low-Flying Autonomous MAV Trail Navigation using Deep Neural Networks for Environmental Awareness
Nikolai Smolyanskiy, Alexey Kamenev, Jeffrey Smith +1
We present a micro aerial vehicle (MAV) system, built with inexpensive off-the-shelf hardware, for autonomously following trails in unstructured, outdoor environments such as fores…