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Early lane change prediction for automated driving systems using multi-task attention-based convolutional neural networks
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Mozaffari, Sajjad, Arnold, Eduardo, Dianati, Mehrdad and Fallah, Saber (2022) Early lane change prediction for automated driving systems using multi-task attention-based convolutional neural networks. IEEE Transactions on Intelligent Vehicles, 7 (3). pp. 758-770. doi:10.1109/TIV.2022.3161785 ISSN 2379-8858.
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Official URL: http://dx.doi.org/10.1109/TIV.2022.3161785
Abstract
Lane change (LC) is one of the safety-critical manoeuvres in highway driving according to various road accident records. Thus, reliably predicting such manoeuvre in advance is critical for the safe and comfortable operation of automated driving systems. The majority of previous studies rely on detecting a manoeuvre that has been already started, rather than predicting the manoeuvre in advance. Furthermore, most of the previous works do not estimate the key timings of the manoeuvre (e.g., crossing time), which can actually yield more useful information for the decision making in the ego vehicle. To address these shortcomings, this paper proposes a novel multi-task model to simultaneously estimate the likelihood of LC manoeuvres and the time-to-lane-change (TTLC). In both tasks, an attention-based convolutional neural network (CNN) is used as a shared feature extractor from a bird's eye view representation of the driving environment. The spatial attention used in the CNN model improves the feature extraction process by focusing on the most relevant areas of the surrounding environment. In addition, two novel curriculum learning schemes are employed to train the proposed approach. The extensive evaluation and comparative analysis of the proposed method in existing benchmark datasets show that the proposed method outperforms state-of-the-art LC prediction models, particularly considering long-term prediction performance.
| Item Type: | Journal Article | |||||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TL Motor vehicles. Aeronautics. Astronautics |
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | |||||||||
| Journal or Publication Title: | IEEE Transactions on Intelligent Vehicles | |||||||||
| Publisher: | Institute of Electrical and Electronics Engineers | |||||||||
| ISSN: | 2379-8858 | |||||||||
| Official Date: | September 2022 | |||||||||
| Dates: |
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| Volume: | 7 | |||||||||
| Number: | 3 | |||||||||
| Number of Pages: | 14 | |||||||||
| Page Range: | pp. 758-770 | |||||||||
| DOI: | 10.1109/TIV.2022.3161785 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Reuse Statement (publisher, data, author rights): | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |||||||||
| Access rights to Published version: | Restricted or Subscription Access | |||||||||
| Copyright Holders: | IEEE | |||||||||
| Date of first compliant deposit: | 30 March 2022 | |||||||||
| Date of first compliant Open Access: | 13 April 2022 | |||||||||
| RIOXX Funder/Project Grant: |
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