… based on an attention-aided encode-decoder, which is cascaded with the deep neural
network (DNN) to construct a fine-grained LC description model. The main contributions of this …

… to optimize the encoderdecoder CNN architecture for segmenting lane boundaries as described
in … Most of the proposed methods for lane detection use hand-crafted features in a model …

… prediction, and a single-road section adaptation. To tackle … prediction model based on a
deep encoder–decoder and a … road sections, this study proposes a scalable deep encoder–…

… a simple encode-decode deep learning model to detect lane … for the traffic regulations
depicted by the lane markings on … Mamidala et al. proposed an encoder-decoder convolution …

… Lane-level heterogeneous traffic flow prediction will become a new and important challenge
in … -based encoder–decoder model is proposed to solve the lane-level heterogeneous traffic …

… encoder-decoder architecture and the beam search algorithm. In Section III, we describe the
proposed trajectory prediction model … a typical sedan and the quarter lane width. Using the …

… into the widely used encoder-decoder framework to generate … lane encoder to represent
each lane individually. Each … In this paper, we show that our proposed lane attention model …

… a encoder-decoder network using semantic segmentation architecture. In this direction, a
hybrid model … -level information of the encoder-decoder network for lane region detection. The …

… road lane: CNN Encoder–Decoder network, CNN Encoder–… the vehicle’s security and to
protect other vehicles on the road, … a curved lane, parabola equation and circle equation models …

… -based LSTM encoder–decoder model and a road shape linearization method for predicting
the future trajectory of surrounding vehicles. When changing lanes or adjusting speed, …