Pothole in the Dark: Perceiving Pothole Profiles with Participatory Urban Vehicles.

Accessing to timely and accurate road condition information, especially about dangerous potholes is of great importance to the public and the government. In this paper, we propose a novel scheme, called $P^3$ , which utilizes smartphones placed in normal vehicles to sense and estimate the profiles of potholes on urban surface roads. In particular, a $P^3$ -enabled smartphone can actively learn the knowledge about the suspension system of the host vehicle without any human intervention and adopts a one degree-of-freedom (DOF) vibration model to infer the depth and length of pothole while the vehicle is hitting the pothole. Furthermore, $P^3$ shows the potential to derive more accurate results by aggregating individual estimates. In essence, $P^3$ is light-weighted and robust to various conditions such as poor light, bad weather, and different vehicle types. We have implemented a prototype system to prove the practical feasibility of $P^3$ . The results of extensive experiments based on real trace demonstrate the efficacy of the $P^3$ design. On average, $P^3$ can achieve low depth and length estimation error rates of 13 and 16 percent, respectively.