Journal Article

Vision-based intention and trajectory prediction in autonomous vehicles: A survey

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.

Attached files

Authors

Teeti, Izzeddin
Khan, Salman
Shahbaz, Ajmal
Bradley, Andrew
Cuzzolin, Fabio

Oxford Brookes departments

School of Engineering, Computing and Mathematics

Dates

Year of publication: 2022
Date of RADAR deposit: 2022-10-19

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

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