Deep understanding of big geospatial data for self-driving cars

Shang, Shuo; Shen, Jianbing; Wen, Ji Rong; Kalnis, Panos

Deep understanding of big geospatial data for self-driving cars

Files

Deep understanding of big geospatial data for self-driving cars.pdf (67.81 KB)

Type

Article

Authors

Shang, Shuo
Shen, Jianbing
Wen, Ji Rong
Kalnis, Panos

KAUST Department

Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
InfoCloud Research Group

Online Publication Date

2020-07-28

Print Publication Date

2020-07

Date

2020-07-28

Submitted Date

2020-06-07

Abstract

Self-driving cars are capable of sensing environment and moving with little or no human input. Effective control of self-driving cars based on big geospatial data is one of the promising future directions of intelligent transportation. Specifically, big geospatial data understanding is helpful in acquiring travel behavior, vehicle mobility, traffic flow, nearby environment, and traffic-aware navigation. This special issue contains 10 research articles that present solid and novel research studies in the area of geospatial data analytics for self-driving applications, and 1survey article that investigates existing studies related to self-driving cars. All of the 11 papers went through at least two rounds of rigorous reviews by the guest editors and invited reviewers.

Citation

Shang, S., Shen, J., Wen, J.-R., & Kalnis, P. (2020). Deep understanding of big geospatial data for self-driving cars. Neurocomputing. doi:10.1016/j.neucom.2020.06.119