There were many good collision avoidance papers published last year. One trend that I saw already when I was preparing my Paris Game AI Conference presentation last year was that the next step in the human like collision avoidance will come from inspecting motion capture data.
One of my favorites from last year was A Velocity-Based Approach for Simulating Human Collision Avoidance by Karamouzas & Overmars. Technically their solution is very close to the sampling based RVO, but there is one very important difference; quoting the paper:
Our analysis, though, focuses on the predicted time to collision between interacting participants and the deviation from their desired velocities, whereas they studied the effect that the minimum predicted distance has on the participants’ accelerations.
In practice it means that they did bunch of measurements with real people and noticed that the velocity sampling range depends on the predicted time of impact.
That is, if the agent things it will hit something 3 seconds in the future, it is likely to adjust the speed and angle just a tiny amount, but if the collision is imminent, the agent may adjust the velocity a lot. The plot at the top of the post shows how the sampling range changes based on the predicted time of impact.
This is tiny detail, but very important one. The resulting animations (accessible via the link above) look pretty good too.