Ryan Oldja — accepted AI-conference papers

2023

NVRadarNet: Real-Time Radar Obstacle and Free Space Detection for Autonomous Driving

Alexander Popov, Patrik Gebhardt, Ke Chen, Ryan Oldja

ICRA 2023poster

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 drivable free space using automotive RADAR sensors. The network utilizes temporally accumulated data from multiple RADAR sensor…

Cited by 34 Source ↗Scholar ↗

2020

MVLidarNet: Real-Time Multi-Class Scene Understanding for Autonomous Driving Using Multiple Views

Ke Chen, Ryan Oldja, Nikolai Smolyanskiy, Stan Birchfield, Alexander Popov, David Wehr +2

IROS 2020poster

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-View LidarNet (MVLidarNet), a two-stage deep neural network for multi-class object detection and drivable space segmentatio…

Cited by 39 Source ↗Scholar ↗