To solve the slow convergence, poor stability, and proneness to fall into local extremes of traditional path planning algorithms, this study proposes a vehicle path planning method based on a gradient statistical mutation quantum genetic algorithm. Firstly, based on the dynamic adjustment of the rotation angle step by the chromosome fitness value, the idea of gradient descent is introduced to improve the adjustment strategy of the quantum rotation gate. According to the statistical characteristics of chromosome variation trend, a mutation operator based on gradient statistics is designed to realize mutation operation, and an adaptive mutation strategy based on Qubit probability density is put forward. Then the vehicle path planning model is built with the shortest path as the index. Finally, the effectiveness of the improved algorithm in vehicle path planning is verified by simulation experiments. Compared with other optimization algorithms, the proposed algorithm has a shorter path and better search stability to avoid the algorithm from falling into the local optimum.