LAUSR

Efficient intersection planning is one of the most challenging tasks for an autonomous vehicle at present. The degree of response to other traffic participants and whether the path tracking effect is good are the key aspects that determine the performance of planning algorithm for an autonomous vehicle. In order to deal with these problems, in this paper, the frame structure of POMDP (Partially Observable Markov Decision Processes) and the time distance coordinate system are combined to carry out the speed planning for the self-propelled vehicle. The speed planning designs a novel POMDP model to ensure effective prediction of behavioral intentions and decision-making at intersections with no traffic signs. Specifically, the method is considered both the driving position and the time range of arrival of the vehicle at the intersection in real-world situations. At the same time, the algorithm is projected the state information into the time distance coordinate system in the form of probability, and used the value iteration method to solve the optimal solution, so as to improve the adaptability of the algorithm to real-world scenarios. Furthermore, MPC control is added on this basis. Rational use of dynamic MPC and kinematic MPC can effectively reduce the tracking error, improve the stability of tracking, and carry out finer control when the speed frequently fluctuates.