Integrating Intuitive Driver Models in Autonomous Planning for Interactive Maneuvers

Given the current capabilities of autonomous vehicles, one can easily imagine autonomous vehicles being released on the road in the near future. However, it can be assumed that this transition will not be instantaneous, suggesting that autonomous vehicles will have to be capable of driving in a mixed environment, with both humans and autonomous vehicles. To guarantee smooth integration and maintain the nuanced social interactions on the road, a shared mental model must be developed. This means that the behaviors of human-driven vehicles and their typical interactions in collaborative maneuvers must be modeled and understood in an accurate and precise manner. Then, by integrating such models into autonomous planning, we can develop control frameworks that mimic this shared understanding. We present a driver modeling framework that estimates an empirical reachable set to capture typical lane changing behaviors. This method can predict driver behaviors with up to 90% accuracy and cumulative errors less than 1 m. Leveraging this driver model in an optimization-based trajectory planning framework, we can generate trajectories that are similar to those performed by humans. By using this modeling and planning framework, we can improve understanding and integration of nuanced interactions to improve collaboration between humans and autonomy.