The traditional A ∗ algorithm for smart cars has many inflection points that are not smooth and prone to collision. To address these issues, a fusion of A ∗ and DWA is proposed to improve the random obstacle avoidance method. In the A ∗ optimization algorithm, by calculating the slope between nodes, key nodes are selected and redundant nodes are eliminated. The evaluation function is improved. The speed and path smoothness of the A ∗ algorithm are enhanced. The A ∗ optimization algorithm cannot avoid random obstacles and the DWA algorithm has the problems of imperfect speed and safety. To solve these problems, an optimized DWA adaptive algorithm is proposed, which considers speed and safety. Between each two optimized key nodes, an adaptive dynamic window method is used to locally avoid random dynamic-static obstacles. Therefore, smart cars can avoid obstacles autonomously, smoothly, safely and efficiently. Compared with the algorithm of A ∗ , Dijkstra, the improved A ∗ and the basic DWA fusion algorithm, experimental results show that the global path length, number of inflection points, and time are reduced by an average of 2. 9% , 36. 2% , and 24. 7% ; 2. 9% , 40. 7% , and 30. 9% ; 0. 31% , 23. 8% , and 13. 6% in three environments. It can achieve random obstacle avoidance and smooth path, which verifies the effectiveness of this algorithm.