A probabilistic approach to planning and control in autonomous urban driving

This paper considers the problem of decision making and control for autonomous urban vehicles operating among other non-cooperating, possibly human controlled, vehicles. The difficulty in this problem stems from the fact that the behavior of the other vehicles is uncertain, and in many circumstances a collision cannot be prevented even under restrictive assumptions about the other drivers' actions. Traditional approaches that consider the worst-case actions of the other vehicles typically are inapplicable because the solutions are either too conservative or infeasible. This work proposes a new method for solving this problem using a chance constrained framework, which requires that the violation probability of all the constraints is guaranteed to be below a threshold. This formulation has the benefit of not only achieving traditional objectives such as minimization of fuel or travel time, but also hedges the nominal planned maneuver against potential crashes leading from the uncertainty in the other drivers' actions. The proposed framework is demonstrated on a passing maneuver example which exhibits a region where the passing vehicle cannot be guaranteed to prevent a collision in all circumstances.