{"type":"ScholarlyArticle","title":"Vision-based Intention and Trajectory Prediction in Autonomous Vehicles: A Survey","authors":[{"name":"Izzeddin Teeti","slug":"izzeddin-teeti","given":"Izzeddin","family":"Teeti","affiliation":"Visual Artificial Intelligence Laboratory , Oxford Brookes University , Oxford , UK; Autonomous Driving & Intelligent Transport , Oxford Brookes University , Oxford , UK"},{"name":"Salman Khan","slug":"salman-khan","given":"Salman","family":"Khan","affiliation":"Visual Artificial Intelligence Laboratory , Oxford Brookes University , Oxford , UK"},{"name":"Ajmal Shahbaz","slug":"ajmal-shahbaz","given":"Ajmal","family":"Shahbaz","affiliation":"Visual Artificial Intelligence Laboratory , Oxford Brookes University , Oxford , UK"},{"name":"Andrew Bradley","slug":"andrew-bradley","given":"Andrew","family":"Bradley","affiliation":"Autonomous Driving & Intelligent Transport , Oxford Brookes University , Oxford , UK"},{"name":"Fabio Cuzzolin","slug":"fabio-cuzzolin","given":"Fabio","family":"Cuzzolin","affiliation":"Visual Artificial Intelligence Laboratory , Oxford Brookes University , Oxford , UK"}],"abstract":"This survey targets intention and trajectory prediction in Autonomous Vehicles (AV), as AV companies compete to create dedicated prediction pipelines to avoid collisions. The survey starts with a formal definition of the prediction problem and highlights its challenges, to then critically compare the models proposed in the last 2-3 years in terms of how they overcome these challenges. Further, it lists the latest methodological and technical trends in the field and comments on the efficacy of different machine learning blocks in modelling various aspects of the prediction problem. It also summarises the popular datasets and metrics used to evaluate prediction models, before concluding with the possible research gaps and future directions.","doi":"10.24963/ijcai.2022/785","url":"https://ijcai.org/proceedings/2022/785","pdf":"https://ijcai.org/proceedings/2022/0785.pdf","markdown":"https://ijcai.org/proceedings/2022/785.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/785","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Survey Track","track":"Survey Track","number":785,"pages":{"first":5630,"last":5637},"keywords":["Survey Track: -","Survey Track: Robotics","Survey Track: Machine Learning"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38987299","length_sec":84,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985442","length_sec":707,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Addanki et al., 2021] Ravichandra Addanki, Peter W Battaglia, David Budden, Andreea Deac, Jonathan Godwin, Thomas Keck, Wai Lok Sibon Li, Alvaro Sanchez-Gonzalez, Jacklynn Stott, Shantanu Thakoor, et al. 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