Feature | Technical
Hayoung Kim, Dongchan Kim, and Kunsoo Huh Hanyang University
Intention Aware Motion Planning with Model Predictive Control in ABSTRACT Highway Merge Scenario
FIGURE 1: Highway merging scenario that requires other road user’s intention.
Introduction There has been an active research to solve merging problem. First, there exist methods using Model Predictive Control (MPC) [1,2,3]. Mukai et al. [1] uses MPC to solve merging problem in highway scenario. Here, constant velocity model is used to predict the surrounding vehicle. Therefore, it is not realistic to apply in complex traffic situations. MPC is also used in [2]. In this work, in order to represent uncertainty of the physical state of the surrounding vehicle, Gaussian distribution is utilized. Zhan et al. [3] combines search-based method and optimization-based method to roughly plan longitudinal trajectory. Then, perform longitudinal and lateral trajectory smoothing using MPC. Studies in [1,2,3] have a limit in that they do not consider intention and interaction of nearby vehicles in common.
FIGURE 2: Overall architecture for highway merging.
Next, there exist studies which consider intention of the surrounding vehicles and interaction with the host vehicle [4,5,6]. Milanés et al. [4] presented Fuzzy logicbased method to solve merging problem. They used physical state information which includes intention of the surrounding vehicles. However, there exists a limitation that the information used is obtained through vehicleto-infrastructure (V2I) communication. In the work [5], to model interaction with the surrounding vehicles, extended probabilistic IDM model is used. Here, trajectory prediction is performed estimating the parameters of the proposed IDM model. In the case of motion planning, the computation time is reduced by treating only longitudinal movements except the lateral movement through the road network represented by the arc length of 1-D. On the other hand, as can be seen in studies
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-toinfrastructure (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).
[1,7,8], in parallel lane merging scenarios where merge points are not defined, it is difficult to cope without considering lateral planning. Hubmann et al. [6] deals with the merging scenario in the congested traffic situations. They use Partially Observable Markov Decision Process (POMDP) to model uncertain cooperation with the surrounding vehicles. In addition, Monte Carlo sampling algorithm is used to solve the combined longitudinal and lateral optimization problem. In this study, we propose an intention aware motion planning method using finite state machine and model predictive control in highway merge scenario. The proposed behavioral planner contains “negotiate” state
26 | July 2020
VTE JULY 2020 FINAL.indd 26
13/7/20 7:14 pm
