端到端网络中的多模态时空信息用于汽车转向预测

M. Abou-Hussein, Stefan H. Müller-Weinfurtner, J. Boedecker
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引用次数: 5

摘要

我们使用车载摄像头的视觉输入数据研究端到端转向问题。对空间、时空和多模态模型进行了实证比较,从两个评价点评估每个概念的表现。首先,模型在预测和模仿现实驾驶员的行为方面有多接近,其次,预测的转向命令的平稳性。后者是一个新提出的度量标准。基于我们的结果,我们提出了一个新的循环多模态模型。建议的模型已经在BMW记录的定制数据集以及Udacity提供的公共数据集上进行了测试。结果显示,它比以前发布的分数要好。在此基础上,提出了一种包含校正框架的偏离车道驾驶转向校正概念。实证结果表明,我们的建议具有良好的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multimodal Spatio-Temporal Information in End-to-End Networks for Automotive Steering Prediction
We study the end-to-end steering problem using visual input data from an onboard vehicle camera. An empirical comparison between spatial, spatio-temporal and multimodal models is performed assessing each concept’s performance from two points of evaluation. First, how close the model is in predicting and imitating a real-life driver’s behavior, second, the smoothness of the predicted steering command. The latter is a newly proposed metric. Building on our results, we propose a new recurrent multimodal model. The suggested model has been tested on a custom dataset recorded by BMW, as well as the public dataset provided by Udacity. Results show that it outperforms previously released scores. Further, a steering correction concept from off-lane driving through the inclusion of correction frames is presented. We show that our suggestion leads to promising results empirically.
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