结合天气与时间，用模糊逻辑智能选摄像头或加速度数据分类路面
Fuzzy-Logic and Deep Learning for Environmental Condition-Aware Road Surface Classification
- 用模糊逻辑根据天气和时段动态选择摄像头或加速度数据
- 五类路面分类准确率超95%，相机与加速度数据各具优势
- 实测数据来自卡尔斯鲁厄理工学院周边道路，适合自动驾驶系统
监测道路表面状态可为车辆规划与主动控制系统提供关键信息。传统方法成本高且无系统性，需耗时测量。本文提出一种基于气象条件与路面数据的实时系统，通过手机摄像头采集卡尔斯鲁厄理工学院周边道路数据。测试了多种基于图像的深度学习算法，并将道路加速度数据转换为图像形式用于训练。对比了基于加速度与基于相机图像的方法性能。评估了AlexNet、LeNet、VGG、ResNet等模型在五类路面（沥青、损坏沥青、碎石路、损坏碎石路、铺砌路）上的表现，分类准确率超过95%。同时提出利用模糊逻辑，根据天气与时段决定使用加速度或相机图像进行路面分类。
原文摘要 · Abstract (English)
Monitoring states of road surfaces provides valuable information for the planning and controlling vehicles and active vehicle control systems. Classical road monitoring methods are expensive and unsystematic because they require time for measurements. This article proposes an real time system based on weather conditional data and road surface condition data. For this purpose, we collected data with a mobile phone camera on the roads around the campus of the Karlsruhe Institute of Technology. We tested a large number of different image-based deep learning algorithms for road classification. In addition, we used road acceleration data along with road image data for training by using them as images. We compared the performances of acceleration-based and camera image-based approaches. The performances of the simple Alexnet, LeNet, VGG, and Resnet algorithms were compared as deep learning algorithms. For road condition classification, 5 classes were considered: asphalt, damaged asphalt, gravel road, damaged gravel road, pavement road and over 95% accuracy performance was achieved. It is also proposed to use the acceleration or the camera image to classify the road surface according to the weather and the time of day using fuzzy logic.
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