Abstract
In this paper, we want to strengthen an autonomous vehicle’s lane-change ability with limited lane changes performed by the autonomous system. In other words, our task is bootstrapping the predictability of lane-change feasibility for the autonomous vehicle. Unfortunately, autonomous lane changes happen much less frequently in autonomous runs than in manual-driving runs. Augmented runs serve well in terms of data augmentation: the number of samples generated from augmented runs in a single one is comparable with that of samples retrieved from real runs in a month. In this paper, we formulate the Lane-Change Feasibility Prediction problem and also propose a data-driven learning approach to solve it. Experimental results are also presented to show the effectiveness of learned lane-change patterns for the decision making.
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Acknowledgement
This research was supported by HUST Independent Innovation Research Fund (2021XXJS096), Sichuan University Interdisciplinary Innovation Research Fund (RD-03-202108), IPRAI Key Lab Fund (6142113220309), and the Key Lab of Image Processing and Intelligent Control, Ministry of Education, China.
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Xiang, X. (2023). Bootstrapping Autonomous Lane Changes with Self-supervised Augmented Runs. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol 13804. Springer, Cham. https://doi.org/10.1007/978-3-031-25069-9_9
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