[TORCS] End-to-end learning for highway assistance driving system In the previous post (2017/07/28 - [TORCS] - End-to-end learning for autonomous driving), I trained a Convolutional neural network that produces a steering wheel angle command from a front-facing camera input image. In this post, I will show you a self-driving car that keeps the current lane position while keeping a constant distance from the closet car ahead. The input to the CNN is a (normaliz.. 더보기 [CVPR2017] Video frame interpolation via adaptive convolution The main idea of this paper is 'motion-compensated frame interpolation can be implemented by a simple convolution operation'. The following image breifly shows the author's idea. So the goal of this paper is to train fully convolutional neural network that infers a convolution kernel from the consecutive two video patches. Applying the convolution kernel (infered by the trained deep network) to .. 더보기 [CVPR2017] End-to-end learning of driving models from large-scale video dataset The goal of this paper is to learn a driving model or policy from large scale un-calibrated sources. The proposed model (learned from the large scale data sources) is generic in that it learns a predictive future motion path given the present car state. For the policy learning, the authors of the paper suggest Fully Convolutional Network (FCN)-Long Short Term Memory(LSTM) network architecture. T.. 더보기 이전 1 ··· 48 49 50 51 52 53 54 ··· 65 다음
