Multipolicy Decision-Making for Autonomous Driving via Changepoint-based Behavior Prediction

To operate reliably in real-world traffic, an autonomous car must evaluate the consequences of its potential actions by anticipating the uncertain intentions of other traffic participants. This paper presents an integrated behavioral inference and decision-making approach that models vehicle behavior for both our vehicle and nearby vehicles as a discrete set of closedloop policies that react to the actions of other agents. Each policy captures a distinct high-level behavior and intention, such as driving along a lane or turning at an intersection. We first employ Bayesian changepoint detection on the observed history of states of nearby cars to estimate the distribution over potential policies that each nearby car might be executing. We then sample policies from these distributions to obtain high-likelihood actions for each participating vehicle. Through closed-loop forward simulation of these samples, we can evaluate the outcomes of the interaction of our vehicle with other participants (e.g., a merging vehicle accelerates and we slow down to make room for it, or the vehicle in front of ours suddenly slows down and we decide to pass it). Based on those samples, our vehicle then executes the policy with the maximum expected reward value. Thus, our system is able to make decisions based on coupled interactions between cars in a tractable manner. This work extends our previous multipolicy system We evaluate our approach using real-world traffic-tracking data from our autonomous vehicle platform, and present decision-making results in simulation involving highway traffic scenarios.