Interaction and Decision Making-aware Motion Planning using Branch Model Predictive Control

TL;DR

This paper introduces a motion planning framework for autonomous vehicles that accounts for human driver behavior, interaction, and decision-making, using a branch model predictive control approach to improve safety and communication.

Contribution

It presents a novel Branch Model Predictive Control method that models human multi-modal behavior, interaction, and decision processes based on neuroscience insights.

Findings

Effective handling of multi-modal human driver outcomes

Enhanced interaction modeling with reactive agents

Ability to plan assertive, communicative maneuvers

Abstract

Motion planning for autonomous vehicles sharing the road with human drivers remains challenging. The difficulty arises from three challenging aspects: human drivers are 1) multi-modal, 2) interacting with the autonomous vehicle, and 3) actively making decisions based on the current state of the traffic scene. We propose a motion planning framework based on Branch Model Predictive Control to deal with these challenges. The multi-modality is addressed by considering multiple future outcomes associated with different decisions taken by the human driver. The interactive nature of humans is considered by modeling them as reactive agents impacted by the actions of the autonomous vehicle. Finally, we consider a model developed in human neuroscience studies as a possible way of encoding the decision making process of human drivers. We present simulation results in various scenarios, showing the advantages of the proposed method and its ability to plan assertive maneuvers that convey intent to humans.