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

Accurately detecting and predicting lane change (LC)processes of human-drivenvehicles can help autonomous vehicles better understand their surroundingenvironment, recognize potential safety hazards, and improve traffic safety.This paper focuses on LC processes, first developing a temporal convolutionalnetwork with an attention mechanism (TCN-ATM) model to recognize LC intention.Considering the intrinsic relationship among output variables, the Multi-taskLearning (MTL)framework is employed to simultaneously predict multiple LCvehicle status indicators. Furthermore, a unified modeling framework for LCintention recognition and driving status prediction (LC-IR-SP) is developed.The results indicate that the classification accuracy of LC intention wasimproved from 96.14% to 98.20% when incorporating the attention mechanism intothe TCN model. For LC vehicle status prediction issues, three multi-taskinglearning models are constructed based on MTL framework. The results indicatethat the MTL-LSTM model outperforms the MTL-TCN and MTL-TCN-ATM models.Compared to the corresponding single-task model, the MTL-LSTM modeldemonstrates an average decrease of 26.04% in MAE and 25.19% in RMSE.

Lane Change Intention Recognition and Vehicle Status Prediction for Autonomous Vehicles