The Effect of Biased Training Data in Self- Driving Cars
| dc.contributor.author | Snowden, Olivia | |
| dc.date.accessioned | 2021-12-13T13:58:06Z | |
| dc.date.available | 2021-12-13T13:58:06Z | |
| dc.date.created | 2021 | |
| dc.description | 2021 Celebration of Student Research and Creativity presentation | en_US |
| dc.description.abstract | "Self-driving cars rely on trained machine learning algorithms to navigate the world around them. I present results from experiments that test whether biased training data effects a self- driving car’s ability to identify obstacles in the road. I wrote a machine learning algorithm and created biased and unbiased datasets to train and test the algorithm appropriately. When the algorithm was trained with a biased dataset but tested with an unbiased dataset, the accuracy of the model was poor. This concludes that bias in a self- driving car’s machine learning algorithm does impact the algorithm’s performance and hinders its ability to interpret the world around it." | en_US |
| dc.description.uri | https://youtu.be/467nFyjSOj4 | en_US |
| dc.identifier.uri | http://hdl.handle.net/11216/4201 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Northern Kentucky University | en_US |
| dc.relation.ispartofseries | Celebration of Student Research and Creativity;2021 | |
| dc.subject | Automated vehicles | en_US |
| dc.subject | Machine learning | en_US |
| dc.subject | Algorithms | en_US |
| dc.title | The Effect of Biased Training Data in Self- Driving Cars | en_US |
| dc.type | Presentation | en_US |