Big Data-Driven Advancements and Future Directions in Vehicle Perception Technologies: From Autonomous Driving to Modular Buses

Hongyi Lin, Yang Liu*, Liang Wang, Xiaobo Qu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

The rapid development of Big Data and artificial intelligence (AI) is revolutionizing the automotive and transportation industries, leading to the creation of the Autonomous Modular Bus (AMB). Designed to address the key challenges of modern public transportation systems, the AMB adopts a modular dynamic assembly approach. However, existing research on the AMB predominantly focuses on operational aspects, whereas in-transit docking remains the primary obstacle to its commercial deployment. This challenge stems from the fact that current perception accuracy in autonomous vehicles is limited to the decimeter level, with insufficient capability to manage adverse weather and complex traffic conditions. To enable AMBs to achieve full-scenario autonomous driving capabilities, this paper reviews current perception technologies from three perspectives: single-vehicle single-sensor perception, multi-sensor fusion perception, and cooperative perception. It examines the characteristics of existing perception solutions and evaluates their applicability to AMB-specific requirements. Furthermore, considering the unique challenges of in-transit docking, this paper identifies and proposes four future research directions for advancing AMB perception systems as well as general autonomous driving technologies.

Original languageEnglish
Pages (from-to)1568-1587
Number of pages20
JournalIEEE Transactions on Big Data
Volume11
Issue number3
DOIs
Publication statusPublished - 2025
Externally publishedYes

Keywords

  • Autonomous modular bus (AMB)
  • autonomous driving
  • cooperative perception
  • fusion perception
  • in-transit docking

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