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

Anticipating the motion of other road users is crucial for automated drivingsystems (ADS), as it enables safe and informed downstream decision-making andmotion planning. Unfortunately, contemporary learning-based approaches formotion prediction exhibit significant performance degradation as the predictionhorizon increases or the observation window decreases. This paper proposes anovel technique for trajectory prediction that combines a data-drivenlearning-based method with a velocity vector field (VVF) generated from anature-inspired concept, i.e., fluid flow dynamics. In this work, the vectorfield is incorporated as an additional input to a convolutional-recurrent deepneural network to help predict the most likely future trajectories given asequence of bird's eye view scene representations. The performance of theproposed model is compared with state-of-the-art methods on the HighD datasetdemonstrating that the VVF inclusion improves the prediction accuracy for bothshort and long-term (5~sec) time horizons. It is also shown that the accuracyremains consistent with decreasing observation windows which alleviates therequirement of a long history of past observations for accurate trajectoryprediction. Source codes are available at:https://github.com/Amir-Samadi/VVF-TP.

A Novel Deep Neural Network for Trajectory Prediction in Automated Vehicles Using Velocity Vector Field