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Learning to Predict Lane Changes in Highway Scenarios Using Dynamic Filters on a Generic Traffic Representation
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.ORCID iD: 0000-0002-7796-1438
KTH, School of Electrical Engineering and Computer Science (EECS), Robotics, perception and learning, RPL.
2018 (English)In: IEEE Intelligent Vehicles Symposium, Proceedings, Institute of Electrical and Electronics Engineers Inc. , 2018, p. 1385-1392Conference paper, Published paper (Refereed)
Abstract [en]

In highway driving scenarios it is important for highly automated driving systems to be able to recognize and predict the intended maneuvers of other drivers in order to make robust and informed decisions. Many methods utilize the current kinematics of vehicles to make these predictions, but it is possible to examine the relations between vehicles as well to gain more information about the traffic scene and make more accurate predictions. The work presented in this paper proposes a novel method of predicting lane change maneuvers in highway scenarios using deep learning and a generic visual representation of the traffic scene. Experimental results suggest that by operating on the visual representation, the spacial relations between arbitrary vehicles can be captured by our method and used for more informed predictions without the need for explicit dynamic or driver interaction models. The proposed method is evaluated on highway driving scenarios using the Interstate-80 dataset and compared to a kinematics based prediction model, with results showing that the proposed method produces more robust predictions across the prediction horizon than the comparison model.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2018. p. 1385-1392
Keywords [en]
Deep learning, Intelligent vehicle highway systems, Kinematics, Vehicles, Accurate prediction, Comparison modeling, Driver interaction, Highly automated drivings, Lane change maneuvers, Prediction horizon, Robust predictions, Visual representations, Forecasting
National Category
Civil Engineering
Identifiers
URN: urn:nbn:se:kth:diva-247122DOI: 10.1109/IVS.2018.8500426Scopus ID: 2-s2.0-85056800536ISBN: 9781538644522 (print)OAI: oai:DiVA.org:kth-247122DiVA, id: diva2:1301892
Conference
2018 IEEE Intelligent Vehicles Symposium, IV 2018, 26 September 2018 through 30 September 2018
Note

QC 20190403

Available from: 2019-04-03 Created: 2019-04-03 Last updated: 2019-04-03Bibliographically approved

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Mänttäri, JoonatanFolkesson, JohnWard, Erik

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  • apa
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Output format
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