An in-depth study on driving habits and personalized driving-assistance systems is conducive to the realization of vehicle safety and intelligent driving. In this paper, we present a personalized vehicle lane-change assistance system integrated with a driver-behavior identification strategy. First, the driver-behavior data-acquisition system is designed and established. Based on this, the input data of different kinds of drivers along with vehicle signals are collected under typical working conditions. The drivers are classified utilizing factor analysis and a fuzzy c-means clustering algorithm, and the identification of driver behavior is realized using a backpropagation neural network optimized by a particle swarm optimization algorithm. Then, personalized warning, planning, and control systems are designed for lane changing. The proposed personalized lane-change assistance system can provide more personalized recommendations to the drivers, increasing the potential for more widespread acceptance and use of advanced driver-assistance system. Finally, the correctness of the proposed personalized lane-change system is evaluated by conducting computer simulations and a driver-in-the-loop- simulation under various conditions. And the results show that the lane-change assistance system based on driver behavior can meet the driving needs of different drivers without sacrificing safety.