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International Journal of Intelligent Transportation Systems Research
Published in:

26-09-2024

A Novel Deep Learning-Driven Smart System for Lane Change Decision-Making

Authors: D. Deva Hema, T. Rajeeth Jaison

Published in: International Journal of Intelligent Transportation Systems Research | Issue 3/2024

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Abstract

The article introduces a novel deep learning-driven smart system designed to accurately predict lane changes in autonomous vehicles. The system leverages a Deep Belief Network (DBN) for lane change decision-making and an enhanced Long Short-Term Memory (LSTM) model optimized by Improved Grey Wolf Optimization for trajectory prediction. The combination of these advanced techniques aims to improve the safety and efficiency of lane change maneuvers in autonomous driving. The study compares the proposed model with existing methods, demonstrating significant enhancements in accuracy, precision, and overall performance. By addressing the complexities of lane change behavior, this research contributes to the development of more reliable and intelligent transportation systems.
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Metadata
Title
A Novel Deep Learning-Driven Smart System for Lane Change Decision-Making
Authors
D. Deva Hema
T. Rajeeth Jaison
Publication date
26-09-2024
Publisher
Springer US
Published in
International Journal of Intelligent Transportation Systems Research / Issue 3/2024
Print ISSN: 1348-8503
Electronic ISSN: 1868-8659
DOI
https://doi.org/10.1007/s13177-024-00421-4

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Advanced Modeling and Interpretation for Accurate Intersection Traffic Time Prediction

Passenger-oriented Distributed Traffic Signal Control Using Dynamic Programming with Vehicle Queue Spillback and Waiting Time Constraints

CM3-VSL: Cooperative Multi-goal Multi-stage Multi-agent VSL Traffic Control

A Two-Stage Behavioral Model Considering Vehicle Motion Fluctuations for Decision-Making During Lane Changes in Diverging Areas

Autonomous Cars and Consumer Choices: A Stated Preference Approach

DA-RNN-Based Bus Arrival Time Prediction Model

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