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Found 4 papers, 2 papers with code
PredictionNet: Real-Time Joint Probabilistic Traffic Prediction for Planning, Control, and Simulation
Predicting the future motion of traffic agents is crucial for safe and efficient autonomous driving.
Displacement-Invariant Cost Computation for Efficient Stereo Matching
A common way to speed up the computation is to downsample the feature volume, but this loses high-frequency details.
On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network Approach
Despite the progress on monocular depth estimation in recent years, we show that the gap between monocular and stereo depth accuracy remains large$-$a particularly relevant result due to the prevalent reliance upon monocular cameras by vehicles that are expected to be self-driving.
Toward Low-Flying Autonomous MAV Trail Navigation using Deep Neural Networks for Environmental Awareness
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 forests.
Robotics
