Incorporating Orientations into End-to-end Driving Model for Steering Control

• URL: http://arxiv.org/abs/2103.05846v1
• Date: Wed, 10 Mar 2021 03:14:41 GMT
• Title: Incorporating Orientations into End-to-end Driving Model for Steering Control
• Authors: Peng Wan, Zhenbo Song, Jianfeng Lu
• Abstract summary: We present a novel end-to-end deep neural network model for autonomous driving. It takes monocular image sequence as input, and directly generates the steering control angle. Our dataset includes multiple driving scenarios, such as urban, country, and off-road.
• Score: 12.163394005517766
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we present a novel end-to-end deep neural network model for autonomous driving that takes monocular image sequence as input, and directly generates the steering control angle. Firstly, we model the end-to-end driving problem as a local path planning process. Inspired by the environmental representation in the classical planning algorithms(i.e. the beam curvature method), pixel-wise orientations are fed into the network to learn direction-aware features. Next, to handle the imbalanced distribution of steering values in training datasets, we propose an improvement on a cost-sensitive loss function named SteeringLoss2. Besides, we also present a new end-to-end driving dataset, which provides corresponding LiDAR and image sequences, as well as standard driving behaviors. Our dataset includes multiple driving scenarios, such as urban, country, and off-road. Numerous experiments are conducted on both public available LiVi-Set and our own dataset, and the results show that the model using our proposed methods can predict steering angle accurately.

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