Motion Planning of Automatic Driving in Complex Traffic Scenarios With the increasing complexity, dynamicity and uncertainty of traffic, motion planning of automatic driving is getting more difficult and challenging. This paper focuses on the real-time motion planning problem of connected and automated vehicles in complex traffic scenarios. To effectively solve this problem, a general driving risk model is presented, which contains the following two essential parts: i) collision risk, i.e., the collision risk between the subject vehicle and other surrounding vehicles, pedestrians, buildings etc.; ii) non-collision risk, such as violation of traffic regulations, the difference between the actual operation state of the subject vehicle and the intention of driver, etc. In order to achieve the real time collision detection, the subject vehicle is approximated to a dot and its shape is considered by extending the dimension of obstcales considering their relative position and velocity. Then an index similar to the exponential function is defined to calculate the collision risk value, which is composed of the result of collision detection and time to collision (TTC). The index of non-collision risk is measured by the deviation from the desired states including regulations, driving manners and intentions etc. Accordingly, a motion planning algorithm by minimizing the aforementioned risk is proposed to calculate the required speed and yaw angle, considering the constrantis of vehicle dynamics. Bench tests have been carried out to demonstrate the effectiveness of the proposed motion planning algorithm. The results show that it can safely handle a variety of complex traffic scenarios, such as lane change with a front vehicle cutting in suddenly, meanwhile the traffic regulations and vehicle dynamics constraints can also be met.