Semantic Segmentation on Automotive Radar Maps
Authors:
- 1 June 2019
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 756-763
Abstract
As radar sensors can measure an object's range and velocity with a high degree of precision, moving objects can be successfully classified, as well. Classifying stationary objects still needs a lot of research, however. In this paper, we use popular semantic segmentation networks in order to classify the vehicle's immediate infrastructure. To this end, a full 3D measurement is performed with a test vehicle equipped with four high resolution corner radar sensors. A preprocessed point cloud is transformed into various radar maps for input to a neural network. Simulations as well as real-world measurements show an overall intersection over union of 84 and 77%, respectively, as well as an overall accuracy of 95 and 90%, respectively, being a new benchmark for this young research field.Keywords
This publication has 14 references indexed in Scilit:
- Adaptions for Automotive Radar Based Occupancy Gridmaps2018
- Semantic radar grids2017
- DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFsIEEE Transactions on Pattern Analysis and Machine Intelligence, 2017
- Parking space detection from a radar based target list2017
- SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image SegmentationIEEE Transactions on Pattern Analysis and Machine Intelligence, 2017
- Potential of radar for static object classification using deep learning methods2016
- Fully convolutional networks for semantic segmentation2015
- OctoMap: an efficient probabilistic 3D mapping framework based on octreesAutonomous Robots, 2013
- Empirical evaluation of vehicular models for ego motion estimation2011
- High resolution maps from wide angle sonar2005