Autoencoder and Active Learning to Reduce False Positive Warnings in a Slippery Road Alert System
| dc.contributor.author | Axenhamn, Philip | |
| dc.contributor.author | Greppe, Andreas | |
| dc.contributor.department | Chalmers tekniska högskola / Institutionen för data och informationsteknik | sv |
| dc.contributor.department | Chalmers University of Technology / Department of Computer Science and Engineering | en |
| dc.contributor.examiner | Damaschke, Peter | |
| dc.contributor.supervisor | Selpi, Selpi | |
| dc.date.accessioned | 2023-12-08T13:45:07Z | |
| dc.date.available | 2023-12-08T13:45:07Z | |
| dc.date.issued | 2023 | |
| dc.date.submitted | 2023 | |
| dc.description.abstract | Modern vehicles are commonly equipped with a slippery road alert (SRA) system that warns drivers of slippery roads. Current implementations of this system occasionally produce warnings when the road is not slippery. These warnings are called false positives and can harm the system’s trustworthiness. In this thesis, we propose a false positive filter capable of reducing the number of false positive alerts generated by an SRA system based on two machine learning techniques: autoencoder and active learning. The available data was completely unlabeled and contained few informative features. The analysis of this data showed that vehicles send bursts of data points forming sequences. Due to the limitations of the data, multi-variate time series were constructed with the idea that the sequential data might reveal more about the situation than a single-point measurement. Furthermore, the sequences were grouped into true- and false-positive classes based on assumptions of the causes for the alerts, such as driving on ice or a speed bump. The sequential data was used to train GRU- and LSTM-based autoencoders and classifiers to detect sequences that correspond to false positive situations such that they can be removed. The hyperparameters for the models were tuned using Optuna and the best-performing models with the most optimal hyperparameters were further evaluated. Since the data was not labeled, the actual performance of the proposed solution could not be assessed. Instead, the evaluation was based on computing the proportion of remaining assumed true positive (ATP) sequences and assumed false positive (AFP) sequences after filtering. The results show that the LSTM autoencoder could find patterns in the sequential data and was able to remove 43% of the AFP sequences while retaining 90% of the ATP sequences. The active learning approach proved to not work well with the available data. | |
| dc.identifier.coursecode | DATX05 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.12380/307425 | |
| dc.language.iso | eng | |
| dc.setspec.uppsok | Technology | |
| dc.subject | slippery road alert | |
| dc.subject | false positive | |
| dc.subject | machine learning | |
| dc.subject | unsupervised learning | |
| dc.subject | semi-supervised learning | |
| dc.subject | autoencoder | |
| dc.subject | active learning | |
| dc.subject | anomaly detection | |
| dc.title | Autoencoder and Active Learning to Reduce False Positive Warnings in a Slippery Road Alert System | |
| dc.type.degree | Examensarbete för masterexamen | sv |
| dc.type.degree | Master's Thesis | en |
| dc.type.uppsok | H | |
| local.programme | Data science and AI (MPDSC), MSc |