With the development of 5G communication and mobile edge computing, edge-based automatic driving is becoming a promising solution for relieving the computing loads and improving the scheduling of autonomous vehicles. In the edge-based automatic driving scenario, the vehicles need to upload many types of sensor data to the edge node, which may cause large latency and endanger the vehicles. In this paper, we define a Vehicle-to-edge Data Transmission (VDT) problem for sensor data transmission between the vehicle and edge node of the edge-based automatic driving, considering the requirements on the accuracy of data and the real-time of transmission. To solve the VDT problem optimally, we construct a Mixed-Integer Linear Programming (MILP) formula. Furthermore, we also present the Deviation-Detection (DD) algorithm and Greedy algorithm to efficiently gain near-optimal solution of the VDT problem. We evaluate the proposed algorithms by a set of simulated automatic driving data. The experimental results show that the proposed Greedy algorithm can reduce 5%~13% communication cost over the instinct DD algorithm.