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

Vehicle safety is one of the most challenging aspect of future-generationautonomous and semi-autonomous vehicles. Collision warning systems (CCWs), as aproposed solution framework, can be relied as the main structure to address theissues in this area. In this framework, information plays a very importantrole. Each vehicle has access to its own information immediately. However,another vehicle information is available through a wireless communication. Dataloss is very common issue for such communication approach. As a consequence,CCW would suffer from providing late or false detection awareness. Robustestimation of lost data is of this paper interest which its goal is toreconstruct or estimate lost network data from previous available or estimateddata as close to actual values as possible under different rate of lost. Inthis paper, we will investigate and evaluate three different algorithmsincluding constant velocity, constant acceleration and Kalman estimator forthis purpose. We make a comparison between their performance which reveals theability of them in term of accuracy and robustness for estimation andprediction based on previous samples which at the end affects the quality ofCCW in awareness generation.

Evaluation of estimation approaches on the quality and robustness of collision warning system