Road Information Collector Using Smartphone for Measuring Road Width Based on Object and Lane Detection This paper proposed an application for Android smartphone to collect road information. That information will be extracted to acquire road width, so the drivers could use right alternate routes for their types of vehicle. Object and lane detection are both used for obtaining road width when lane boundaries are detected in road image otherwise, road width is obtained using a vanishing point method. The average error rate for road width measurement using object and lane detection is 19.71%. Meanwhile, the average error rate when there is no lane boundary is in the range of 1015%, 818%, and 1019% for various capturing sides. Reclassification of the road is done when the error rate of road width is set. Accuracy of road category reclassification is in the range of 7075% in various sides.

Abnormal Road Surface Recognition Based on Smartphone Acceleration Sensor In order to identify the abnormal road surface condition efficiently and at low cost, a road surface condition recognition method is proposed based on the vibration acceleration generated by a smartphone when the vehicle passes through the abnormal road surface. The improved Gaussian background model is used to extract the features of the abnormal pavement, and the knearest neighbor (kNN) algorithm is used to distinguish the abnormal pavement types, including pothole and bump. Comparing with the existing works, the influence of vehicles with different suspension characteristics on the detection threshold is studied in this paper, and an adaptive adjustment mechanism based on vehicle speed is proposed. After comparing the field investigation results with the algorithm recognition results, the accuracy of the proposed algorithm is rigorously evaluated. The test results show that the vehicle vibration acceleration contains the road surface condition information, which can be used to identify the abnormal road conditions. The test result shows that the accuracy of the recognition of the road surface pothole is 96.03%, and the accuracy of the road surface bump is 94.12%. The proposed road surface recognition method can be utilized to replace the special patrol vehicle for timely and lowcost road maintenance.