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Found 4 papers, 0 papers with code
Multi-task UNet architecture for end-to-end autonomous driving
We propose an end-to-end driving model that integrates a multi-task UNet (MTUNet) architecture and control algorithms in a pipeline of data flow from a front camera through this model to driving decisions.
End-to-End Deep Learning of Lane Detection and Path Prediction for Real-Time Autonomous Driving
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.
Differential Capacitance of Electric Double Layers: A Poisson-Bikerman Formula
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
Deep Learning and Control Algorithms of Direct Perception for Autonomous Driving
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.
