Towards a Safe Real-Time Motion Planning Framework for Autonomous Driving Systems: An MPPI Approach

Planning safe trajectories in Autonomous Driving Systems (ADS) is a complex
problem to solve in real-time. The main challenge to solve this problem arises
from the various conditions and constraints imposed by road geometry, semantics
and traffic rules, as well as the presence of dynamic agents. Recently, Model
Predictive Path Integral (MPPI) has shown to be an effective framework for
optimal motion planning and control in robot navigation in unstructured and
highly uncertain environments. In this paper, we formulate the motion planning
problem in ADS as a nonlinear stochastic dynamic optimization problem that can
be solved using an MPPI strategy. The main technical contribution of this work
is a method to handle obstacles within the MPPI formulation safely. In this
method, obstacles are approximated by circles that can be easily integrated
into the MPPI cost formulation while considering safety margins. The proposed
MPPI framework has been efficiently implemented in our autonomous vehicle and
experimentally validated using three different primitive scenarios.
Experimental results show that generated trajectories are safe, feasible and
perfectly achieve the planning objective. The video results as well as the
open-source implementation are available at:
https://gitlab.uni.lu/360lab-public/mppi