Smooth Behavioral Estimation For Ramp Merging Control In Autonomous Driving

Cooperative driving behavior is essential for driving in traffic, especially for ramp merging, lane changing or navigating intersections. Autonomous vehicles should also man- age these situations by behaving cooperatively and naturally. In this paper, we enhance our previous learning-based method to efficiently estimate other vehicles' intentions and interact with them in ramp merging scenarios, without over-the-air communication between vehicles. The proposed approach inherits our previous Probabilistic grahpical Model (PGM) and distance- keeping framework. Real driving trajectories are used to learn transition models in the PGM. Thus, besides the structure of the PGM, our method does not require human-designed reward or cost functions. The PGM-based intention estimation is followed by an off-the-shelf distance-keeping model to generate proper acceleration/deceleration controls. The PGM plays a plug-in role in our self-driving framework. The new model eliminates two assumptions in the previous model: 1) a fixed merging point for all merging agents, which is hard to determine before the merging vehicles make the merge; 2) Perfect velocity measurement, which requires sophisticated perception systems. We validate the performance of our method both on real merging data and using a designed merging strategy in simulation, and show significant improvements compared with previous methods. Parameter design is also discussed by experiments. The new method is computationally efficient, and exhibits better robustness against sensing uncertainty.