In order to solve the problem of long running time of single frame and single trajectory evaluation criteria existing in the optimal trajectory generation algorithm of driverless vehicles in urban road scenarios, a driverless vehicle trajectory planning method based on driving risk field to optimize sampling area was proposed. In this method, static and dynamic obstacle risk field models were established respectively through two-dimensional Gaussian distribution to quantify the target point of the sampling area on road. The sampling target points in the areas with low driving risks were selected by convolution to generate the optimal trajectory. The simulation results show that this optimization method only selects part of the sampling target points for trajectory generation in each planning period, which improves the running efficiency of the algorithm and makes the running time of each frame of the algorithm less than 0.1 s. The addition of driving risk field makes the sampling area of algorithm more consistent with the drivers′ behavior and habits, improves the degree of anthropomorphism of the algorithm planning results, and ensures the high driving efficiency of the driverless vehicle.