{"type":"ScholarlyArticle","title":"MFTraj: Map-Free, Behavior-Driven Trajectory Prediction for Autonomous Driving","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":"Huanming Shen","slug":"huanming-shen","given":"Huanming","family":"Shen","affiliation":"University of Electronic Science and Technology of China"},{"name":"Dongping Liao","slug":"dongping-liao","given":"Dongping","family":"Liao","affiliation":"University of Macau"},{"name":"Bonan Wang","slug":"bonan-wang","given":"Bonan","family":"Wang","affiliation":"University of Macau"},{"name":"Guofa Li","slug":"guofa-li","given":"Guofa","family":"Li","affiliation":"Chongqing University"},{"name":"Chengzhong Xu","slug":"chengzhong-xu","given":"Chengzhong","family":"Xu","affiliation":"University of Macau"}],"abstract":"This paper introduces a trajectory prediction model tailored for autonomous driving, focusing on capturing complex interactions in dynamic traffic scenarios without reliance on high-definition maps. The model, termed MFTraj, harnesses historical trajectory data combined with a novel dynamic geometric graph-based behavior-aware module. At its core, an adaptive structure-aware interactive graph convolutional network captures both positional and behavioral features of road users, preserving spatial-temporal intricacies. Enhanced by a linear attention mechanism, the model achieves computational efficiency and reduced parameter overhead. Evaluations on the Argoverse, NGSIM, HighD, and MoCAD datasets underscore MFTraj's robustness and adaptability, outperforming numerous benchmarks even in data-challenged scenarios without the need for additional information such as HD maps or vectorized maps. Importantly, it maintains competitive performance even in scenarios with substantial missing data (12.5%-50%), outperforming most existing state-of-the-art models. The results and methodology suggest a significant advancement in autonomous driving trajectory prediction, paving the way for safer and efficient autonomous systems.","doi":"10.24963/ijcai.2024/657","url":"https://ijcai.org/proceedings/2024/657","pdf":"https://ijcai.org/proceedings/2024/0657.pdf","markdown":"https://ijcai.org/proceedings/2024/657.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/657","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":657,"pages":{"first":5945,"last":5953},"keywords":["Agent-based and Multi-agent Systems: MAS: Applications","Robotics: ROB: Other","Multidisciplinary Topics and Applications: MTA: Transportation","Agent-based and Multi-agent Systems: MAS: Multi-agent planning"],"videos":[],"references":[{"position":1,"raw":"[Bhattacharyya et al., 2023] Prarthana Bhattacharyya, Chengjie Huang, and Krzysztof Czarnecki. 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