Intention aware motion planning with model predictive control in highway merge scenario

Abstract

Human drivers navigate by continuously predicting the intent of road users and interacting with them. For safe autonomous driving, research about predicting future trajectory of vehicles and motion planning based on these predictions has drawn attention in recent years. Most of these studies, however, did not take into account driver's intentions or any interdependence with other vehicles. In order to drive safely in real complex driving situations, it is essential to plan a path based on other driver's intentions and simultaneously to estimate the intentions of other road user with different characteristics as human drivers do. We aim to tackle the above challenges on highway merge scenario where the intention of other road users should be understood. In this study, we propose an intention aware motion planning method using finite state machine and model predictive control without any vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) communications. The key idea is to design the behavioral planner that control the possible modes like human drivers do. This behavioral planner contains negotiate state which could inform my intent to other road users and estimate the other user's intention from their reaction. The model predictive controller generates an optimized trajectory for merging in terms of safety, efficiency and comfort with directly reflecting the estimated intention of the road users. In order to verify the proposed framework, the complex highway merging scenario is implemented where various road users with different intention and characteristic exist by using IDM (Intelligent Driver Model).