no code implementations • 16 Dec 2021 • Der-Hau Lee, Jinn-Liang Liu
The DNN is a modified UNet, a well known encoder-decoder neural network of semantic segmentation.
no code implementations • 9 Feb 2021 • Der-Hau Lee, Jinn-Liang Liu
Inspired by the UNet architecture of semantic image segmentation, we propose a lightweight UNet using depthwise separable convolutions (DSUNet) for end-to-end learning of lane detection and path prediction (PP) in autonomous driving.
no code implementations • 24 Dec 2020 • Ren-Chuen Chen, Chin-Lung Li, Jen-Hao Chen, Bob Eisenberg, Jinn-Liang Liu
The PBik theory is a generalization of the classical Poisson-Boltzmann theory to include different steric energies of different-sized ions and water similar to different electrical energies for different-charged ions.
Soft Condensed Matter
no code implementations • 26 Oct 2019 • Der-Hau Lee, Kuan-Lin Chen, Kuan-Han Liou, Chang-Lun Liu, Jinn-Liang Liu
Based on the direct perception paradigm of autonomous driving, we investigate and modify the CNNs (convolutional neural networks) AlexNet and GoogLeNet that map an input image to few perception indicators (heading angle, distances to preceding cars, and distance to road centerline) for estimating driving affordances in highway traffic.