Interactive Behavior Modeling for Vulnerable Road Users With Risk-Taking Styles in Urban Scenarios: A Heterogeneous Graph Learning Approach

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

  • Zirui Li - , Beijing Institute of Technology (Author)
  • Jianwei Gong - , Beijing Institute of Technology (Author)
  • Zheyu Zhang - , Beijing Institute of Technology (Author)
  • Chao Lu - , Beijing Institute of Technology (Author)
  • Victor L. Knoop - , Delft University of Technology (Author)
  • Meng Wang - , Chair of Traffic Process Automation, TUD Dresden University of Technology (Author)

Abstract

The deep understanding of the behaviors of traffic participants is essential to guarantee the safety of automated vehicles (AV) in mixed traffic with vulnerable road users (VRUs). Precise trajectory prediction of traffic participants can provide reasonable solution space for motion planning of AV. Early works mainly focused on handcrafting the feature representation and designing complicated architectures in deep learning-based prediction models. However, these approaches overlooked the fact that different road users perceive the safety of the same interaction differently and also exhibit heterogeneous risk-taking styles. In this paper, we will develop a model for trajectory prediction based on risk-taking styles. The model accounts for the expected positions and occupancy of traffic participants in the surrounding environment. It consists of two sequential steps: risk-taking styles of multi-modal road users under interactive scenes are first clustered, and then reformulated in the heterogeneous graph model for trajectory prediction. The model is validated by the driving data collected on the urban road using a public dataset. Comparative experiments demonstrate that the proposed method can predict the trajectory of traffic participants much more accurately than the state-of-the-art methods.

Details

Original languageEnglish
Pages (from-to)1-18
Number of pages18
JournalIEEE Transactions on Intelligent Transportation Systems
Publication statusPublished - 2024
Peer-reviewedYes

External IDs

Mendeley 77143673-5e18-3ac5-8508-ec4ade4cd910

Keywords

Keywords

  • heterogeneous graph model, interactive behavior modeling, risk-taking behaviors, trajectory prediction, Vulnerable road users