In this paper, we proposed a crowd-sensing idea to construct the driving environment so that the driver could have better understanding of his/her surroundings on the roadway. We assume that intelligent vehicles will embed a sensing system, which is composed of three basic modules including inter-vehicle communication, vehicle license plate verification, and distance estimation. Through the help of inter-vehicle communication, a vehicle can receive a set of IDs from its nearby vehicles. Those received IDs, with the license plate numbers of the nearby vehicles, could further improve the license plate verification function in an uncontrolled environment. Moreover, we proposed a regression method, which models the relationship between the image coordinate and the geometric distance, to estimate the front vehicle distance. Finally, by fusing the vehicle verification and distance information from nearby vehicles, the system would provide a global view to tell the driver the information of those vehicles around him and their distances. Comparing with the existing advanced driver assistance system (ADAS), this system would support a wider view of the driving environment, and provide a more comfortable and safer driving experience. To fulfill the sensing system, a license plate verification method with the help from inter-vehicle communication and a regression method for distance estimation are detailed in this paper. Based on the results, our system could verify the license plate with a high accuracy rate and provide robust distance estimation.