Vehicle active collision avoidance includes collision avoidance by braking and by steering, however both of these two methods have their limitations. When the vehicle’s speed is high or road adhesion coefficient is small, critical braking distance is long by braking to avoid collision, and collision avoidance by steering is restricted to the vehicle driving condition on the side lane. Therefore, it is significant to establish the feasible region of active collision avoidance to choose the optimal way to avoid traffic accidents. Model predictive control (MPC), as an optimized method, not only makes the control input of current time to achieve the best, but also can achieve the optimal control input in a future time. MPC makes the current state as the initial state of optimal control, and uses a series of control input to acquire the vehicle trajectory in a future time, then takes the difference between the acquired vehicle trajectory and the desired trajectory as the optimization target, finally achieves the optimal control sequence. The path tracking controller is designed by hierarchical control structure. The upper controller includes model predictive control allocation and deceleration controller, and the lower is designed by weighted least-squares control allocation. Besides, seven order polynomial is used for path planning. In this paper, three kinds of collision avoidance measures are compared, including only by steering, by steering and yaw moment and by steering, yaw moment and braking. Applying the additional yaw moment can assist the vehicle to steer when the steering input reaches the limit value, and applying deceleration control can make the time of lane changing longer. Finally, the feasible collision avoidance region based on the braking/steering is built under the conditions that the front car is stationary, in constant speed or in constant deceleration.