基于CNN的自动驾驶可视性学习与控制交叉验证

Chen Sun, Lang Su, Sunsheng Gu, Jean M. Uwabeza Vianney, K. Qin, Dongpu Cao
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引用次数: 3

摘要

在过去的几十年里,由于计算能力的指数级增长和传感器成本的降低,自动驾驶吸引了大量的研究工作。作为一项对安全敏感的任务,自动驾驶需要对决策、规划和控制的场景有详细的了解。本文研究了基于卷积神经网络(CNN)的驾驶场景可视性学习方法。在虚拟环境和真实采样数据中,建立并评估了驾驶场景可视性学习的各种感知模型。我们还提出了一个条件控制模型,该模型将提取的粗驾驶能力映射到给定驾驶先验的低级控制条件。对基于CNN的感知模型和控制模型的性能、优点进行了分析,并在虚拟和真实数据上进行了交叉验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cross Validation for CNN based Affordance Learning and Control for Autonomous Driving
Autonomous driving has attracted a significant amount of research efforts over the last few decades owing to the exponential growth of computational power and reduced cost of sensors. As a safety-sensitive task, autonomous driving needs a detailed level of scene understanding of decision making, planning, and control. This paper investigates the Convolutional Neural Network (CNN) based methods for affordance learning in driving scene understanding. Various perception models are built and evaluated for driving scene affordance learning in both the virtual environment and real sampled data. We also propose a conditional control model that maps the extracted coarse set of driving affordances to low-level control condition on the given driving priors. The performance, merits of the CNN based perception models, and the control model are analyzed and cross-validated on both virtual and real data.
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