The Effect of Biased Training Data in Self- Driving Cars
Northern Kentucky University
"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."
2021 Celebration of Student Research and Creativity presentation
Automated vehicles, Machine learning, Algorithms