Journal article
Context-aware trajectory prediction for autonomous driving in heterogeneous environments
Computer-aided civil and infrastructure engineering, v 39(1), pp 120-135
01 Jan 2024
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
The prediction of surrounding agent trajectories in heterogeneous traffic environments remains a challenging task for autonomous driving due to several critical issues, such as understanding social interactions among agents and the environment, handling multiclass traffic movements, and generating feasible trajectories in accordance with real-world rules, all of which hinder prediction accuracy. To address these issues, a new multimodal trajectory prediction framework based on the transformer network is presented in this study. A hierarchical-structured context-aware module, inspired by human perceptual logic, is proposed to capture contextual information within the scene. An efficient linear global attention mechanism is also proposed to reduce the computation and memory load of the transformer framework. Additionally, this study introduces a novel auxiliary loss to penalize infeasible off-road predictions. Empirical results on the Lyft l5kit data set demonstrate the state-of-the-art performance of the proposed model, which substantially enhances the accuracy and feasibility of prediction outcomes. The proposed model also possesses a unique feature, effectively dealing with missing input observations. This study underscores the importance of comprehending social interactions among agents and the environment, handling multiclass traffic movements, and generating feasible trajectories adhering to real-world rules in autonomous driving.
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Details
- Title
- Context-aware trajectory prediction for autonomous driving in heterogeneous environments
- Creators
- Zhenning Li - University of MacauZhiwei Chen - Drexel University, Civil, Architectural, and Environmental EngineeringYunjian Li - University of MacauChengzhong Xu - Univ Macau, Dept Comp & Informat Sci, State Key Lab Internet Things Smart City, Ave Univ, Taipa, Macau, Peoples R China
- Publication Details
- Computer-aided civil and infrastructure engineering, v 39(1), pp 120-135
- Publisher
- Elsevier
- Number of pages
- 16
- Grant note
- 0021/2022/ITP; 0081/2022/A2; SKL-IOTSC(UM)-2021-2023; 0123/2022/AFJ; 0015/2019/AKP / Science and Technology Development Fund of Macau SAR 2020B515130004 / GuangDong Basic and Applied Basic Research Foundation; Guangdong Basic and Applied Basic Research Foundation 2020B010164003 / Key-Area Research and Development Program of Guangdong Province
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Civil, Architectural, and Environmental Engineering
- Web of Science ID
- WOS:000945303300001
- Scopus ID
- 2-s2.0-85150437488
- Other Identifier
- 991021861177804721
UN Sustainable Development Goals (SDGs)
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Source: SDGs in the Output
InCites Highlights
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Interdisciplinary Applications
- Construction & Building Technology
- Engineering, Civil
- Transportation Science & Technology