An Integrated Framework of Decision Making and Motion Planning for Autonomous Vehicles Considering Social Behaviors
This work addresses the challenge of safe and socially-aware autonomous driving for vehicles in traffic, though it appears incremental as it combines existing methods like game theory and potential fields.
The paper tackled the problem of decision making and motion planning for autonomous vehicles during lane changes by incorporating social behaviors of surrounding vehicles, resulting in an integrated approach that demonstrated feasibility and effectiveness in handling different social interactions in three testing scenarios.
This paper presents a novel integrated approach to deal with the decision making and motion planning for lane-change maneuvers of autonomous vehicle (AV) considering social behaviors of surrounding traffic occupants. Reflected by driving styles and intentions of surrounding vehicles, the social behaviors are taken into consideration during the modelling process. Then, the Stackelberg Game theory is applied to solve the decision-making, which is formulated as a non-cooperative game problem. Besides, potential field is adopted in the motion planning model, which uses different potential functions to describe surrounding vehicles with different behaviors and road constrains. Then, Model Predictive Control (MPC) is utilized to predict the state and trajectory of the autonomous vehicle. Finally, the decision-making and motion planning is then integrated into a constrained multi-objective optimization problem. Three testing scenarios considering different social behaviors of surrounding vehicles are carried out to validate the performance of the proposed approach. Testing results show that the integrated approach is able to address different social interactions with other traffic participants, and make proper and safe decisions and planning for autonomous vehicles, demonstrating its feasibility and effectiveness.