Object Segmentation for Autonomous Driving Using iseAuto Data

Junyi Gu, Mauro Bellone, Raivo Sell, Artjom Lind
2022 Electronics  
Object segmentation is still considered a challenging problem in autonomous driving, particularly in consideration of real-world conditions. Following this line of research, this paper approaches the problem of object segmentation using LiDAR–camera fusion and semi-supervised learning implemented in a fully convolutional neural network. Our method was tested on real-world data acquired using our custom vehicle iseAuto shuttle. The data include all weather scenarios, featuring night and rainy
more » ... ther. In this work, it is shown that with LiDAR–camera fusion, with only a few annotated scenarios and semi-supervised learning, it is possible to achieve robust performance on real-world data in a multi-class object segmentation problem. The performance of our algorithm was measured in terms of intersection over union, precision, recall, and area-under-the-curve average precision. Our network achieves 82% IoU in vehicle detection in day fair scenarios and 64% IoU in vehicle segmentation in night rain scenarios.
doi:10.3390/electronics11071119 fatcat:dv4jvexqbfdqjnwiq3qyovdtmy