Non-Gaussian Chance-Constrained Trajectory Planning for Autonomous Vehicles under Agent Uncertainty

Understand

Agent behavior is arguably the greatest source of uncertainty in trajectory planning for autonomous vehicles.

• This problem has motivated significant amounts of work in the behavior prediction community on learning rich distributions of the future states and actions of agents.
• However, most current works on chance-constrained trajectory planning under agent or obstacle uncertainty either assume Gaussian uncertainty or linear constraints, which is limiting, or requires sampling, which can be computationally intractable to encode in an optimization problem.
• In this paper, we extend the state-of-the-art by presenting a methodology to upper-bound chance-constraints defined by polynomials and mixture models with potentially non-Gaussian components.