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Found 6 papers, 0 papers with code
Online Out-of-Domain Detection for Automated Driving
DNNs learn from training data, which means that they only achieve good accuracy within the underlying data distribution of the training data.
Improving Predictive Performance and Calibration by Weight Fusion in Semantic Segmentation
Albeit effective, only few works haveimproved the understanding and the performance of weight averaging. Here, we revisit this approach and show that a simple weight fusion (WF)strategy can lead to a significantly improved predictive performance andcalibration.
Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety
Our paper addresses both machine learning experts and safety engineers: The former ones might profit from the broad range of machine learning topics covered and discussions on limitations of recent methods.
Strategy to Increase the Safety of a DNN-based Perception for HAD Systems
Safety is one of the most important development goals for highly automated driving (HAD) systems.
Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds
Accurate detection of 3D objects is a fundamental problem in computer vision and has an enormous impact on autonomous cars, augmented/virtual reality and many applications in robotics.
Ranked #7 on
3D Multi-Object Tracking
on KITTI
Efficient Semantic Segmentation for Visual Bird's-eye View Interpretation
The ability to perform semantic segmentation in real-time capable applications with limited hardware is of great importance.
