Work

Pedestrian Crossing Intention Prediction in the Wild: A Survey

Year: 2024

Type: article

Source: CHAIN

DOI: 10.23919/chain.2024.000008

Authors: Yancheng Ling, Zhenliang Ma

Institution: KTH Royal Institute of Technology

Language: English

Topic: Autonomous Vehicle Technology and Safety

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Sustainable cities and communities

Open Access status: closed

OpenAlex ID: W4406322787

FWCI: 0.65

Cites: 81

Cited by: 6

Related to: 10