Delayed-Decision Motion Planning in the Presence of Multiple Predictions

-- Reliable automated driving technology is challenged by various sources of uncertainties, in particular, behavioral uncertainties of traffic agents. It is common for traffic agents to have intentions that are unknown to others, leaving an automated driving car to reason over multiple possible behaviors. This paper formalizes a behavior planning scheme in the presence of multiple possible futures with corresponding probabilities. We present a maximum entropy formulation and show how, under certain assumptions, this allows delayed decision-making to improve safety. The general formulation is then turned into a model predictive control formulation, which is solved as a quadratic program or a set of quadratic programs. We discuss implementation details for improving computation and verify operation in simulation and on a mobile robot. Prediction technology continues to advance, and multiple prediction outputs are now a staple of state-of-the-art prediction methods [1]-[6]. This paper examines how an autonomous driving (AD) agent can utilize multiple predictions in the behavior planning process. In the context of this work, behavior planning corresponds to the combined task of decision-making and trajectory planning. Consider the scenario depicted in Figure 1. A pedestrian walks along a road and will likely continue straight (with 80% probability), but the pedestrian is positioned close to the street, indicating that they might turn to cross the street (with 20% probability). Selecting the most probable sequence of events results in an overly aggressive and risky behavior - we assume they will not cross and are wrong 20% of the time.