Interactive Prediction and Decision-Making for Autonomous Vehicles: Online Active Learning With Traffic Entropy Minimization

Interacting with the surrounding road users is crucial for autonomous vehicles (AV). However, the inherent multimodality and uncertainties associated with traffic participants (TP) pose challenges in AVs’ prediction and decision-making (PnD). A primary challenge is adapting predictors trained on static offline datasets to the dynamic, diverse data streams encountered in reality. Secondly, utilizing one single forecast trajectory with the highest probability for decision-making contains potential risks as it neglects that even a small probability represents a subset of TP behaviors. Based on the existing prediction backbone, the authors propose an online learning approach incorporating pseudo-labels inferred from partial feedback as compensation for conventional methodologies, considering both the commonsense and personalization facets of driving. Drawing inspiration from the second law of thermodynamics, the authors propose to minimize microscopic traffic entropy as an additional objective in decision-making. This objective aims to reduce the chaos of traffic scenes, thus achieving more predictable future interactions and, conversely, making future decisions easier. Through real-time human-in-the-loop experiments, the authors quantifiably and comparably reveal that adopting one single trajectory without online learning in PnD is risky. However, this reliability is verified to be significantly improved by the proposed techniques, and the efficacy is further analyzed in a subsequent qualitative study. A static experiment transferring the prediction algorithm trained exclusively on Argoverse 2 to datasets including NGSIM, HighD, RounD, and NuScenes is also conducted, demonstrating that the proposed correction can effectively mitigate the gap between the datasets and real-world scenarios.

• Record URL:
  • https://doi.org/10.1109/TITS.2024.3419003
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2024, IEEE.
• Authors:
  • Zhang, Yiran
  • Lou, Shanhe
  • Hang, Peng
  • Huang, Wenhui
  • Yang, Lie
  • Lv, Chen
• Publication Date: 2024-11

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References;
• Pagination: pp 17718-17732
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 25
  • Issue Number: 11
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Algorithms; Autonomous vehicles; Decision making; Mathematical prediction; Vehicle trajectories
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01948012
• Record Type: Publication
• Files: TRIS
• Created Date: Mar 5 2025 9:02AM