no code implementations • 29 Sep 2022 • Alexander Popov, Patrik Gebhardt, Ke Chen, Ryan Oldja, Heeseok Lee, Shane Murray, Ruchi Bhargava, Nikolai Smolyanskiy
To this end, we present NVRadarNet, a deep neural network (DNN) that detects dynamic obstacles and drivable free space using automotive RADAR sensors.
no code implementations • 9 Jun 2020 • Ke Chen, Ryan Oldja, Nikolai Smolyanskiy, Stan Birchfield, Alexander Popov, David Wehr, Ibrahim Eden, Joachim Pehserl
We show that our multi-view, multi-stage, multi-class approach is able to detect and classify objects while simultaneously determining the drivable space using a single LiDAR scan as input, in challenging scenes with more than one hundred vehicles and pedestrians at a time.