Author ORCID Identifier

0009-0005-6738-471X

Document Type

Conference Paper

Disciplines

Statistics

Publication Details

Statistical and Machine Learning: Methods and Applications (SAML-25) on June 5th and 6th, 2025 at TU Dublin, Ireland.

doi:10.21427/bn74-z420

Abstract

Lane change prediction is essential for ensuring road safety and effective decision-making in autonomous vehicles (AVs). AVs will probably take several decades to penetrate new vehicle sales. As AVs and human-driven vehicles (HDVs) will coexist in traffic for the long term, AVs must understand the lane change intentions of surrounding HDVs. Lane changing is a critical manoeuvre that can cause a crash if it is performed late or if incorrect lane adjustments are made. Therefore, forecasting surrounding vehicles’ lane change intentions in advance is essential to ensure safe driving in mixed traffic environments having both AVs and HDVs. The unpredictability of human driving behavior presents challenges for timely and accurate lane change predictions, leading to the risk of collisions. This study proposes an architecture combining deep learning for high accuracy and an edge-driven approach for low latency. In this study, we introduce an artificial intelligencebased early lane change prediction scheme with a decentralized Vehicle-to-Everything (V2X) framework using microservices and edge computing. Our objective is to forecast lane changes 3 seconds in advance with high accuracy and low latency for improving real-time AV safety. Our initial system employs a Long Short-Term Memory (LSTM) model to predict HDV lane change events in an emulated highway scenario.

DOI

https://doi.org/10.21427/bn74-z420

Creative Commons License

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.

Download
Contact the Author


Share

COinS