A hierarchical perception decision-making framework for autonomous driving. Issue 3 (3rd July 2022)

• Record Type: Journal Article
• Authors: Zhang, Ende; Huang, Jin; Gao, Yue; Liu, Yau; Deng, Yangdong
• Is Part Of: Cyber-physical systems. Volume 8:Issue 3(2022)
• Page Start: 192
• Page End: 209
• Publication Date: 2022-07-03
• Subjects: Autonomous driving -- perception -- decision-making -- multi-modal -- hypergraph learning; Cooperating objects (Computer systems) -- Periodicals; Internet of things -- Periodicals; 006.22
• DOI: 10.1080/23335777.2021.1901147
• Languages: English
• ISSNs: 2333-5777

Abstract

ABSTRACT: Self-driving vehicles have attracted significant attention from both industry and academy. Despite the intensive research efforts on the perception model of environment-awareness, it is still challenging to attain accurate decision-making under real-world driving scenarios. Today's state-of-the-art solutions typically hinge on end-to-end DNN-based perception-control models, which provide a rather direct way of driving decision-making. However, DNN models may fail in dealing with complex driving scenarios that require relational reasoning. This paper proposes a hierarchical perception decision-making framework for autonomous driving by employing hypergraph-based reasoning, which enables fuse multi-perceptual models to integrate multimodal environmental information. The proposed framework utilises the high-order correlations behind driving behaviours, and thus allows better relational reasoning and generalisation to achieve more precise driving decisions. Our work outperforms state-of-the-art results on Udacity, Berkeley DeepDrive Video and DBNet data sets. The proposed techniques can be used to construct a unified driving decision-making framework for modular integration of autonomous driving systems.