Path planning is crucial for characterizing the driving ability of autonomous vehicles. The ant colony algorithm is a heuristic searching algorithm that simulates ant foraging. When used for the path planning of autonomous vehicles, this algorithm may suffer from slow convergence speed and unsmooth corners, and the solutions may fall into local extremes. An improved ant colony algorithm was proposed herein for reducing the risk of collision and improving the quality and efficiency of path planning. The heuristic function \(\eta_{ij} \left( t \right)\) and pheromone update rules of the traditional ant colony algorithm were modified. \(\eta_{ij} \left( t \right)\) was calculated based on the distance and angle from the current node to the target node. A pheromone regulatory factor C was introduced; its value decreased when the path length was greater than the average path length and increased otherwise. Simulation of the improved algorithm on 20 × 20 and 30 × 30 grid maps revealed that the path length and number of iterations decreased by an average of 9.8% and 64.3%, respectively, compared with those of the traditional ant colony algorithms. Experimental results showed that autonomous vehicles could move from the starting point to the end point along the simulated paths using the proposed algorithm. The findings of this study are of considerable significance for autonomous vehicles.
