Uncertainty Models for TTC-Based Collision Avoidance

We address the problem of uncertainty-aware local collision avoidance within the context of time-to-collision based navigation of multiple agents. We consider two specific models that account for uncertainty in the future trajectories of interacting agents: an isotropic model which conservatively considers all possible errors, and an adversarial model that assumes the error is towards a head-on collision. We compare the two models experimentally via a number of simulation scenarios, and also provide theoretical guarantees about the collision avoidance behavior of the agents.

We are planning to release the code later this academic year.