Decision Making for Parallel Autonomy in Clutter

Decision Making for Parallel Autonomy in Clutter
Daniela Rus, Sertac Karaman

Motion planning is critical for future driver assistance systems and autonomous cars. Algorithms that are able to design high-performance and provably safe motion through cluttered, dynamics environments are required for cars to operate in complex urban environments. In this research effort, we will develop theories to understand motion through cluttered, dynamic environments; we will also develop algorithms that realize natural, safe motion through cluttered environments, with provable completeness and optimality guarantees. We envision these algorithms to:
      (i) understand the density of the “clutter,” for instance, as the number of other vehicles or pedestrians in the vicinity of the car
      (ii) devise a speed with which the car/robot navigate safely (indoors and outdoors), finding natural and safe paths as the clutter around the vehicle evolves          (iii) design provably-safe trajectories that get the vehicle through the clutter in a natural manner, while guaranteeing safety
      (iv) develop collision avoidance and trajectory planning solution for high speeds in dense environments
      (v) trajectory planning with rules of the road and safety guarantees.