Lateral Ego-Vehicle Control Without Supervision Using Point Clouds
Müller F, Khan Q, Cremers D (2022)
Publication Type: Conference contribution
Publication year: 2022
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 13363 LNCS
Pages Range: 477-488
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Paris, FRA
ISBN: 9783031090363
DOI: 10.1007/978-3-031-09037-0_39
Abstract
Existing vision based supervised approaches to lateral vehicle control are capable of directly mapping RGB images to the appropriate steering commands. However, they are prone to suffering from inadequate robustness in real world scenarios due to a lack of failure cases in the training data. In this paper, a framework for training a more robust and scalable model for lateral vehicle control is proposed. The framework only requires an unlabeled sequence of RGB images. The trained model takes a point cloud as input and predicts the lateral offset to a subsequent frame from which the steering angle is inferred. The frame poses are in turn obtained from visual odometry. The point cloud is conceived by projecting dense depth maps into 3D. An arbitrary number of additional trajectories from this point cloud can be generated during training. This is to increase the robustness of the model. Online experiments conducted on a driving simulator show that the performance of our model is superior to that of a supervised model trained on the same initial data set and comparable to the same model but trained on data collected with noise injection.
Involved external institutions
Germany (DE)How to cite
APA:
Müller, F., Khan, Q., & Cremers, D. (2022). Lateral Ego-Vehicle Control Without Supervision Using Point Clouds. In Mounîm El Yacoubi, Eric Granger, Pong Chi Yuen, Umapada Pal, Nicole Vincent (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 477-488). Paris, FRA: Springer Science and Business Media Deutschland GmbH.
MLA:
Müller, Florian, Qadeer Khan, and Daniel Cremers. "Lateral Ego-Vehicle Control Without Supervision Using Point Clouds." Proceedings of the 3rd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2022, Paris, FRA Ed. Mounîm El Yacoubi, Eric Granger, Pong Chi Yuen, Umapada Pal, Nicole Vincent, Springer Science and Business Media Deutschland GmbH, 2022. 477-488.
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