TY - JOUR
T1 - Big Data-Driven Advancements and Future Directions in Vehicle Perception Technologies
T2 - From Autonomous Driving to Modular Buses
AU - Lin, Hongyi
AU - Liu, Yang
AU - Wang, Liang
AU - Qu, Xiaobo
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
KW - Autonomous modular bus (AMB)
KW - autonomous driving
KW - cooperative perception
KW - fusion perception
KW - in-transit docking
UR - http://www.scopus.com/inward/record.url?scp=85214922884&partnerID=8YFLogxK
U2 - 10.1109/TBDATA.2025.3527208
DO - 10.1109/TBDATA.2025.3527208
M3 - Article
AN - SCOPUS:85214922884
SN - 2332-7790
VL - 11
SP - 1568
EP - 1587
JO - IEEE Transactions on Big Data
JF - IEEE Transactions on Big Data
IS - 3
ER -