Sequential Neural Network Model with Spatial-Temporal Attention Mechanism for Robust Lane Detection Using Multi Continuous Image Frames

Research output: Contribution to conferencePosterScientific

47 Downloads (Pure)

Abstract

Lane detection serves as a fundamental task for automated vehicles and Advanced Driver Assistance Systems. However, current lane detection methods can not deliver the versatility of accurate, robust, and realtime compatible lane detection in real-world scenarios especially under challenging driving scenes. Available vision-based methods in the literature do not consider critical regions of the image and their spatial-temporal salience regarding the detection results, thus they deliver poor performance in peculiar difficult circumstances (e.g., serious occlusion, dazzle lighting). This study aims to introduce a novel sequential neural network model with a spatial-temporal attention mechanism that can focus on key features of lane lines and exploit salient spatial-temporal correlations among continuous image frames for the purpose of enhancing the accuracy and robustness of lane detection. Under the regular encoder-decoder structure and with the implementation using common neural network backbones, the proposed model is trained and evaluated on three large-scale opensource datasets. Extensive experiments demonstrate the strength and the robustness of the proposed model outperforming available state-of-the-art methods in various testing.
Original languageEnglish
Pages1
Number of pages1
Publication statusPublished - 2023
EventTransportation Research Board 102nd Annual Meeting 2023 - Mt Vernon Convention Center, Washington, United States
Duration: 8 Jan 202312 Jan 2023
Conference number: 102
https://www.trb.org/AnnualMeeting/AnnualMeeting.aspx

Conference

ConferenceTransportation Research Board 102nd Annual Meeting 2023
Abbreviated titleTRBAM 2023
Country/TerritoryUnited States
CityWashington
Period8/01/2312/01/23
Internet address

Fingerprint

Dive into the research topics of 'Sequential Neural Network Model with Spatial-Temporal Attention Mechanism for Robust Lane Detection Using Multi Continuous Image Frames'. Together they form a unique fingerprint.

Cite this