Abstract
Currently much research on autonomous driving is based on inferring driving decisions from single-frame inputs without taking spatio-temporal correlations into account. We propose to exploit these correlations using a recurrent neural network (RNN) architecture known as Long-Short-Term Memory (LSTM) to make robust, more confident and more timely decisions. We applied current state of the art convolutional neural network architectures for semantic segmentation like FCN, ENet or SqueezeNet to image sequences. We identified three types of undesired artefacts and found that using the output of a segmenation-CNN as an input for a LSTM visibly improves the accuracy.
| Original language | English |
|---|---|
| Title of host publication | Neural Information Processing Systems (NIPS 2016) |
| Number of pages | 1 |
| Publication status | Published - 2016 |
Fields of science
- 303 Health Sciences
- 304 Medical Biotechnology
- 304003 Genetic engineering
- 305 Other Human Medicine, Health Sciences
- 101004 Biomathematics
- 101018 Statistics
- 102 Computer Sciences
- 102001 Artificial intelligence
- 102004 Bioinformatics
- 102010 Database systems
- 102015 Information systems
- 102019 Machine learning
- 106023 Molecular biology
- 106002 Biochemistry
- 106005 Bioinformatics
- 106007 Biostatistics
- 106041 Structural biology
- 301 Medical-Theoretical Sciences, Pharmacy
- 302 Clinical Medicine
JKU Focus areas
- Computation in Informatics and Mathematics
- Nano-, Bio- and Polymer-Systems: From Structure to Function
- Medical Sciences (in general)
- Health System Research
- Clinical Research on Aging