路面
稳健性(进化)
卷积神经网络
计算机科学
高级驾驶员辅助系统
传感器融合
软件部署
人工智能
自动驾驶
深度学习
人工神经网络
运动规划
工程类
计算机视觉
机器学习
运输工程
机器人
生物化学
化学
土木工程
基因
操作系统
作者
Tongtiegang Zhao,Junxiang He,Jingcheng Lv,Delei Min,Yintao Wei
出处
期刊:IEEE Transactions on Intelligent Transportation Systems
[Institute of Electrical and Electronics Engineers]
日期:2023-08-01
卷期号:24 (8): 8361-8370
被引量:2
标识
DOI:10.1109/tits.2023.3264588
摘要
The prior monitoring of the road surface conditions provides valuable information to vehicle trajectory planning and active control systems. Road surface perception with vision sensors is an emerging technology that has recently gained much attention. However, there are few reports on an exhaustive technical scheme and application practice for driving assistance. This study first creates a large-scale road surface image dataset containing one million samples with detailed road friction level, material, and unevenness level annotations. A convolutional neural network (CNN) classification model constrained by a combined loss function and adapted optimization strategies is trained on the dataset. Then we propose a decision-level fusion method based on the improved Dempster-Shafer evidence theory to enhance the robustness of the classification algorithm. Finally, the developed models are deployed on the embedded hardware platform. The top-1 accuracy for classifying road surface images reaches 92.05% by the CNN model and 97.50% after fusion. Onboard experiments illustrate the advantage and superiority of the developed technical framework, which has excellent potential in real-vehicle applications for driving assistance.
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