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Detecting Unexpected Obstacles for Self-Driving Cars: Fusing Deep Learning and Geometric Modeling
To utilize the appearance and contextual cues, we propose a new deep learning-based obstacle detection framework.
Lost and Found: Detecting Small Road Hazards for Self-Driving Vehicles
The proposed approach outperforms all considered baselines in our evaluations on both pixel and object level and runs at frame rates of up to 20 Hz on 2 mega-pixel stereo imagery.
Exploiting the Power of Stereo Confidences
Before using this information, a mapping from confidence values to disparity outlier probability rate is performed based on gathered disparity statistics from labeled video data.
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