In order to improve the reliability and safety factor of driver assistance systems, and achieve pedestrian detection with a higher precision, an improved pedestrian detection method based on a tree-structured graphical model of the human body is proposed, and it consists of an offline training part and an online detection part. First, the corresponding parent-child parts are obtained by defining the symbiotic relationship between human parts, and then the K-means algorithm is applied to the location relationship between part pairs to acquire part types via clustering. For the purpose of taking both intra-class tightness and inter-class differences into account, a hybrid particle swarm optimization algorithm is built with a two-phase fitness function via introducing MSE and DBI. It is not only effective in estimating the number of optimal cluster centers, but also in eliminating the effect of random initialization on the clustering accuracy. Then, the part type obtained using the optimized clustering method is considered as the latent variable. The pedestrian detection model is obtained through solving the latent structural SVM problem. Finally, we estimate the position of human parts and the detection bounding box on multiple scales based on solving the state equation via a dynamic programming algorithm, and obtain the final pedestrian detection result through incorporating the idea of non-maximum suppression. Experimental results indicate that the performance of the proposed algorithm is superior to those of five other pedestrian detection algorithms. In particular, on the INRIA and ETH databases, the loss rate of the proposed algorithm decreased by 8.14% and 5.05%, respectively, compared with that of the pose-original method. Experimental results show that the proposed algorithm has good performance and high accuracy and robustness.