Inferring Human Driver Intent in Partial Deployment of Connected Autonomous Vehicles: The Lane Change Case

Jonghwan Na, Hojeong Lee, Hyogon Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Co-existence between autonomous vehicles (AVs) and human-driven vehicles expected in the next few decades poses a problem for AVs to infer human drivers' intents nearby and cope with them safely and efficiently. To address this issue, we develop a light-weight deep learning model for a connected autonomous vehicle (CAV) to infer intents in a safety-critical case of lane changes made by human-driven vehicles. Through experiments with the real trajectory dataset NGSIM, we show that a simple Multi-Layer Perceptron (MLP) model can predict lane change events with high accuracy comparable with more sophisticated models. The model is intentionally designed to work with the simplest 3-vehicle topology to foster real-time execution on the resource-constrained computing platforms on AVs. Still, the model achieves 85% accuracy over 5 to 8 seconds prediction horizons so that AVs can have enough time to prepare for an upcoming lane change event.

Original languageEnglish
Title of host publication2023 IEEE 97th Vehicular Technology Conference, VTC 2023-Spring - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350311143
DOIs
Publication statusPublished - 2023
Event97th IEEE Vehicular Technology Conference, VTC 2023-Spring - Florence, Italy
Duration: 2023 Jun 202023 Jun 23

Publication series

NameIEEE Vehicular Technology Conference
Volume2023-June
ISSN (Print)1550-2252

Conference

Conference97th IEEE Vehicular Technology Conference, VTC 2023-Spring
Country/TerritoryItaly
CityFlorence
Period23/6/2023/6/23

Bibliographical note

Publisher Copyright:
© 2023 IEEE.

Keywords

  • Connected Autonomous Vehicles
  • V2X
  • deep learning
  • human driver
  • intent
  • lane change
  • partial deployment

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

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