A Lane Change Assisting Framework for Large Vehicles

  • Nanbin Zhao
  • , Xinran Wang
  • , Bohui Wang
  • , Jialu Zhang
  • , Yun Lu
  • , Rong Su

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

The trend of intelligent transportation has been leading the world for years, and human acceptance of applying Advanced Driver Assistance Systems (ADAS) in driving is increasing. Among all types of human driving, large vehicle driving is the one that requires the assistance of ADAS the most. It is imperative to use automatic driving technology to assist large vehicle driving and further integrate those successful cases into ADAS to increase safety and driving experience. According to [1] and [2], improper lane changes are one of the top 10 driver-related factors involved in large truck crashes. Due to their size and weight, large vehicles require more space and time to change lanes. To fundamentally improve the safety of their lane changes, this paper proposes a Lane Change Assisting Framework (LCAF) for large vehicles. It helps to reduce the potential risks of large vehicles in the lane-changing process by predicting the driving intention of surrounding vehicles and evaluating the safety of potential routes. By assisting the lane change decision-making of large vehicles, while not affecting their passing rate, the passive influence of their lane changes on the whole traffic flow is minimized, and the speed and capacity of the traffic system are improved.

Original languageEnglish
Title of host publication2023 IEEE 26th International Conference on Intelligent Transportation Systems, ITSC 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages48-53
Number of pages6
ISBN (Electronic)9798350399462
DOIs
StatePublished - 2023
Event26th IEEE International Conference on Intelligent Transportation Systems, ITSC 2023 - Bilbao, Spain
Duration: 24 Sep 202328 Sep 2023

Publication series

NameIEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
ISSN (Print)2153-0009
ISSN (Electronic)2153-0017

Conference

Conference26th IEEE International Conference on Intelligent Transportation Systems, ITSC 2023
Country/TerritorySpain
CityBilbao
Period24/09/2328/09/23

Keywords

  • ADAS
  • Lane change prediction
  • Public transport systems
  • Responsibility-Sensitive Safety
  • SUMO
  • Transformer

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