路面坑槽智能检测与路况评估
Intelligent Pothole Detection and Road Condition Assessment
摘要：
恶劣的道路状况是一种公害，造成乘客不适，车辆损坏和事故。在美国，在每年的42 000起交通事故死亡中，有22 000起与道路状况有关。尽管我们经常抱怨糟糕的道路，但我们无法大规模检测或报告它们。为了解决这个问题，我们开发了一个系统来检测坑洞和实时评估道路状况。我们的解决方案是一个移动应用程序，它可以从手机中的陀螺仪和加速计传感器捕获汽车运动的数据。为了使用这些传感器数据评估道路，我们训练了SVM模型，以93%的准确率对道路状况进行分类，并以92%的准确率对坑洞进行分类，这两个问题的基本比率都超过了。当用户驾驶时，模型使用传感器数据来分类道路是好还是坏，以及它是否包含坑洞。然后，分类结果用于创建数据丰富的地图，说明整个城市的道路状况。我们的系统将授权民政官员识别和修复给乘客带来不便和造成事故的损坏道路。本文详细介绍了我们的数据科学流程，包括收集真实道路上的训练数据、将嘈杂的传感器数据转换为有用的信号、训练和评估机器学习模型，以及通过实时分类应用程序将这些模型部署到生产中。本文还重点介绍了城市如何使用我们的系统众包数据，并向有需要的地区提供道路维修资源。
英文摘要：
Poor road conditions are a public nuisance, causing passenger discomfort, damage to vehicles, and accidents. In the U.S., road-related conditions are a factor in 22,000 of the 42,000 traffic fatalities each year. Although we often complain about bad roads, we have no way to detect or report them at scale. To address this issue, we developed a system to detect potholes and assess road conditions in real-time. Our solution is a mobile application that captures data on a car's movement from gyroscope and accelerometer sensors in the phone. To assess roads using this sensor data, we trained SVM models to classify road conditions with 93% accuracy and potholes with 92% accuracy, beating the base rate for both problems. As the user drives, the models use the sensor data to classify whether the road is good or bad, and whether it contains potholes. Then, the classification results are used to create data-rich maps that illustrate road conditions across the city. Our system will empower civic officials to identify and repair damaged roads which inconvenience passengers and cause accidents. This paper details our data science process for collecting training data on real roads, transforming noisy sensor data into useful signals, training and evaluating machine learning models, and deploying those models to production through a real-time classification app. It also highlights how cities can use our system to crowdsource data and deliver road repair resources to areas in need.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
