The cart understands its surrounding through semantic segmentation, which is a technique in computer that classifies each pixel in an image into different categories. The vehicle can also make decisions based on the segmentic segmentation results. The cart can change its speed based on the proximity to nearby obstacles.
We deployed the ENet architecture for segmentation. ENet is design to work well in realtime applications. For more information, please visit the paper. We used the CityScape dataset for training and the python code for training and inferencing are located in the ./src/segmentation/scripts directory.