Abstract: In order to prejudge driving intention of preceding car and enhance the adaptability of adaptive cruise control (ACC) against complex traffic scenarios simultaneously, a multi-objective adaptive cruise control algorithm considering multi-mode switching strategy is proposed. Based on the model predictive control (MPC) framework, it is hopeful to comprehensively coordinate various conflicting objectives such as driver's desired response, rear-end safety, and vehicular physical limits. Meanwhile, the slack variable vector is introduced to deal with non-feasible solution owing to hard constraints during online optimization. The desired response as well as constraint boundary varies with traffic scenarios, and consequently multiple ACC modes are designed by means of slightly adjusting weights of control objectives and system input, constraint boundary as well as slack relaxation. Further, we can obtain relatively suitable mode as well as smooth transition by fuzzy inference and the weighted average method. The simulations show that under emergent traffic scenarios, the multi-objective control algorithm together with multi-mode switching strategy is able to achieve good expectation during the car-following.