Simultaneous Realization of Decision, Planning and Control for Lane-Changing Behavior Using Nonlinear Model Predictive Control

Abstract

Path planning and motion control are fundamental components to realize safe and reliable autonomous driving. The discrimination of the role of these two components, however, is somewhat obscure because of strong mathematical interaction between these two components. This often results in a redundant computation in the implementation. One of attracting idea to overcome this redundancy is a simultaneous path planning and motion control (SPPMC) based on a model predictive control framework. SPPMC finds the optimal control input considering not only the vehicle dynamics but also the various constraints which reflect the physical limitations, safety constraints and so on to achieve the goal of a given behavior. In driving in the real traffic environment, decision making has also strong interaction with planning and control. This is much more emphasized in the case that several tasks are switched in some context to realize higherlevel tasks. This paper presents a basic idea to integrate decision making, path planning and motion control which is able to be executed in realtime. In particular, lane-changing behavior together with the decision of its initiation is selected as the target task. The proposed idea is based on the nonlinear model predictive control and appropriate switching of the cost function and constraints in it. As the result, the decision of the initiation, planning, and control of the lane-changing behavior are achieved by solving a single optimization problem under several constraints such as safety. The validity of the proposed method is tested by using a vehicle simulator.

Connection of nonlinear model predictive controllers for smooth task switching in autonomous driving

“…On the contrary, Figure 1b demonstrates the latter method that does not design an external prediction module but extends the state space of the MPC to describe the interaction with others and optimize its own-vehicle behavior considering the interactive prediction [17][18][19][20]. In this method, the MPC is formulated in the extended state space, including the state of the ego vehicle and others in the surrounding area.…”

“…However, in this framework, the state space has to be changed and the optimization problem reformulated according to the change in the driving task. Previous studies have often focused only on a single driving task, such as obstacle avoidance [17,20] and changing lanes [18,19].…”

MPC Builder for Autonomous Drive: Automatic Generation of MPCs for Motion Planning and Control

“…MPC performs well particularly in dynamic environments by embedding the prediction models of surrounding road users into the constraints of the optimization problem [2]. Consequently, MPC has been applied to complex driving scenarios in the presence of other road users, such as lane changing [3]- [5], obstacle avoidance [6], [7], pedestrian avoidance [8], [9], adaptive cruise control (ACC) [10], and turning and crossing at intersections [11], [12].…”