English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Path tracking system plays a key technology in autonomous driving. The systemshould be driven accurately along the lane and be careful not to cause anyinconvenience to passengers. To address such tasks, this paper proposes hybridtracker based optimal path tracking system. By applying a deep learning basedlane detection algorithm and a designated fast lane fitting algorithm, thispaper developed a lane processing algorithm that shows a match rate with actuallanes with minimal computational cost. In addition, three modified pathtracking algorithms were designed using the GPS based path or the vision basedpath. In the driving system, a match rate for the correct ideal path does notnecessarily represent driving stability. This paper proposes hybrid trackerbased optimal path tracking system by applying the concept of an observer thatselects the optimal tracker appropriately in complex road environments. Thedriving stability has been studied in complex road environments such asstraight road with multiple 3-way junctions, roundabouts, intersections, andtunnels. Consequently, the proposed system experimentally showed the highperformance with consistent driving comfort by maintaining the vehicle withinthe lanes accurately even in the presence of high complexity of roadconditions. Code will be available in https://github.com/DGIST-ARTIV.

Hybrid tracker based optimal path tracking system for complex road environments for autonomous driving