{"type":"ScholarlyArticle","title":"A Cognitive-Driven Trajectory Prediction Model for Autonomous Driving in Mixed Autonomy Environments","authors":[{"name":"Haicheng Liao","slug":"haicheng-liao","given":"Haicheng","family":"Liao","affiliation":"University of Macau"},{"name":"Zhenning Li","slug":"zhenning-li","given":"Zhenning","family":"Li","affiliation":"University of Macau"},{"name":"Chengyue Wang","slug":"chengyue-wang","given":"Chengyue","family":"Wang","affiliation":"University of Macau"},{"name":"Bonan Wang","slug":"bonan-wang","given":"Bonan","family":"Wang","affiliation":"University of Macau"},{"name":"Hanlin Kong","slug":"hanlin-kong","given":"Hanlin","family":"Kong","affiliation":"University of Electronic Science and Technology of China"},{"name":"Yanchen Guan","slug":"yanchen-guan","given":"Yanchen","family":"Guan","affiliation":"University of Macau"},{"name":"Guofa Li","slug":"guofa-li","given":"Guofa","family":"Li","affiliation":"Chongqing University"},{"name":"Zhiyong Cui","slug":"zhiyong-cui","given":"Zhiyong","family":"Cui","affiliation":"Beihang University"}],"abstract":"As autonomous driving technology progresses, the need for precise trajectory prediction models becomes paramount. This paper introduces an innovative model that infuses cognitive insights into trajectory prediction, focusing on perceived safety and dynamic decision-making. Distinct from traditional approaches, our model excels in analyzing interactions and behavior patterns in mixed autonomy traffic scenarios. We introduce the Macao Connected Autonomous Driving (MoCAD) dataset as part of our contributions, which adds value to its complex urban driving scenarios. Our model represents a significant leap forward, achieving marked performance improvements on several key datasets. Specifically, it surpasses existing benchmarks with gains of 16.2% on the Next Generation Simulation (NGSIM), 27.4% on the Highway Drone (HighD), and 19.8% on the MoCAD dataset. Our proposed model shows exceptional proficiency in handling corner cases, essential for real-world applications. Moreover, its robustness is evident in scenarios with missing or limited data, outperforming most of the state-of-the-art baselines. This adaptability and resilience position our model as a viable tool for real-world autonomous driving systems, heralding a new standard in vehicle trajectory prediction for enhanced safety and efficiency.","doi":"10.24963/ijcai.2024/656","url":"https://ijcai.org/proceedings/2024/656","pdf":"https://ijcai.org/proceedings/2024/0656.pdf","markdown":"https://ijcai.org/proceedings/2024/656.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/656","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Multidisciplinary Topics and Applications","track":"Main Track","number":656,"pages":{"first":5936,"last":5944},"keywords":["Multidisciplinary Topics and Applications: MTA: Transportation","Planning and Scheduling: PS: Applications","Agent-based and Multi-agent Systems: MAS: Human-agent interaction","Robotics: ROB: Motion and path planning"],"videos":[],"references":[{"position":1,"raw":"[Ajzen, 1991] Icek Ajzen. 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