Recurrent Models for Lane Change Prediction and Situation Assessment

Scheel O, Nagaraja NS, Schwarz L, Navab N, Tombari F (2022)

Publication Type: Journal article

Publication year: 2022

Journal

IEEE Transactions on Intelligent Transportation Systems Institute of Electrical and Electronics Engineers (IEEE)

Book Volume: 23

Pages Range: 17284-17300

Journal Issue: 10

DOI: 10.1109/TITS.2022.3163353

Abstract

Predicting future events accurately is a task of great importance for autonomous vehicles. In this work we focus on lane change events. For this, we propose a novel attention mechanism on top of recurrent neural networks for the prediction task, which improves performance and yields more interpretable models. As critical corner cases are often not considered and reflected in traditional prediction metrics, we additionally introduce a new scenario-based evaluation scheme, which we posit be considered for further maneuver prediction works. Prediction and planning tasks often are correlated, usually sharing input representations and differing in expected outputs and their subsequent consideration. Here, we detail a supporting layer for planning tasks, which analyzes situations w.r.t. their suitability for lane changes and can serve as decision-making support for any planning algorithm. Exploitation of similarities between this task and the aforementioned prediction problem further improves performance of the prediction task, as well as labelling quality of the assessment task. Additionally, we extend our evaluation to urban scenarios, showcasing the generalizability of our proposed prediction models.

Involved external institutions

Technische Universität München (TUM) DE Germany (DE) BMW AG - Bayerische Motoren Werke / BMW Group DE Germany (DE)

How to cite

APA:

Scheel, O., Nagaraja, N.S., Schwarz, L., Navab, N., & Tombari, F. (2022). Recurrent Models for Lane Change Prediction and Situation Assessment. IEEE Transactions on Intelligent Transportation Systems, 23(10), 17284-17300. https://doi.org/10.1109/TITS.2022.3163353

MLA:

Scheel, Oliver, et al. "Recurrent Models for Lane Change Prediction and Situation Assessment." IEEE Transactions on Intelligent Transportation Systems 23.10 (2022): 17284-17300.

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