Existing smartphone-based systems for detection of road surface events such as speed-breakers, potholes, broken road patches, etc. have been developed primarily for the use in four-wheeler vehicles such as cars with perfect driving maneuvers over a road with occasional irregularities. However, our experiments on a 673 km road trail in a suburban city of India, where an overall road condition is poor, show that such event detection accuracy drops to less than 80% for speed-breakers and less than 70% for potholes, when the crowdsourcing data is collected from different vehicles such as two-wheeler (bike or scooty), three-wheeler (auto-rickshaw) or four-wheeler (car) or with different smartphones kept at different positions (in-pocket, in-dashboard, vehicle-mounted).

The aim of this work is to develop a system that detects three road events — speed-breakers, potholes and broken road patches, with improved accuracy even under varying conditions and rough roads. The proposed RoadSurP system works in two phases. The first phase runs in a smartphone and identifies the candidate signatures for road anomalies using robust auto-orientation and auto-tune thresholding algorithms to make it almost invariant of position, placement, vehicle, and smartphone type. The second phase works in a server and uses a decision tree based classifier to reduce the false-negative and false-positive instances caused due to the impact of different driving maneuvers, vehicle suspensions, etc. Finally, we apply a k-medoids clustering to geo-localize detected events from multiple trails over a map service. RoadSurP is implemented as an Android application, and tested over a 26 km road using five two-wheeler, seven three-wheeler and three four-wheeler vehicles with six different smartphone types under varying placement and position of the smartphones. After being thoroughly trained, the mean accuracy of RoadSurP is found to be 98% for speed-breakers and 92% for potholes over a smooth road (a road with occasional irregularities) and 92% for speed-breakers and 90% for potholes over the rough road. The developed application can be used as an effective crowdsourcing system for road quality monitoring.

已经开发出了基于智能手机的现有系统，用于检测路面事件，例如减速器，坑洼，路面碎裂等，主要用于四轮车辆，例如在偶尔出现不规则状况的道路上具有完美驾驶操作的汽车。 。但是，我们在印度郊区城市的673公里道路上进行的实验表明，该事件检测的准确度对于减速器而言低于80％，对于坑洼而言低于70％，而该地区的总体道路状况很差。众包数据是从不同的车辆收集的，例如两轮车（自行车或踏板车），三轮车（自动人力车）或四轮车（汽车），或将不同的智能手机保持在不同的位置（口袋，仪表板） ，车载）。

这项工作的目的是开发一种系统，该系统可以检测三个道路事件-减速器，坑洼和破碎的路面，即使在变化的条件和崎rough的道路上，其准确性也得到提高。拟议的RoadSurP系统分两个阶段工作。第一阶段在智能手机中运行，并使用强大的自动定位和自动调整阈值算法来识别道路异常的候选特征，从而使其位置，位置，车辆和智能手机类型几乎不变。第二阶段在服务器中工作，并使用基于决策树的分类器来减少由于不同驾驶行为，车辆悬架等的影响而导致的假阴性和假阳性实例。最后，我们将k-medoids聚类到通过地图服务对来自多个路径的检测到的事件进行地理定位。RoadSurP是作为Android应用程序实施的，并在26公里的道路上使用五辆两轮车，七辆三轮车和三辆四轮车在六种不同的智能手机类型和不同的智能手机放置和位置下进行了测试。经过全面培训后，发现RoadSurP的平均准确度对于速滑者为98％，在平坦道路（偶尔有不规则道路）上的坑洼处为92％，对于速滑者而言，在道路上较慢时为92％。坎坷之路。所开发的应用程序可以用作道路质量监控的有效众包系统。