PORCA: Modeling and Planning for Autonomous Driving Among Many Pedestrians

Environmental Modeling for Autonomous Virtual Pedestrians

We present a technique for modeling large-scale urban environments for virtual human simulation. Our approach, which involves a set of hierarchical data structures, supports the efficient interaction between numerous autonomous pedestrians and their environment, including perceptual processing and path planning for the purposes of goal directed navigation. As a specific implementation of our ap...

Safe Motion Planning for Autonomous Driving

Self-driving cars have the potential to revolutionize transportation by making it cheaper, safer, and more efficient. In this thesis we describe a novel motion planning system, which translates high-level navigation goals into low-level actions for controlling a vehicle. Specifically, the motion planning system is responsible for choosing at each time step an appropriate velocity and steering a...

Real-Time Motion Planning 
 and
 Autonomous Driving

Path Planning for Autonomous Driving in Unknown Environments

We describe a practical path-planning algorithm that generates smooth paths for an autonomous vehicle operating in an unknown environment, where obstacles are detected online by the robot’s sensors. This work was motivated by and experimentally validated in the 2007 DARPA Urban Challenge, where robotic vehicles had to autonomously navigate parking lots. Our approach has two main steps. The firs...

Practical Search Techniques in Path Planning for Autonomous Driving

We describe a practical path-planning algorithm that generates smooth paths for an autonomous vehicle operating in an unknown environment, where obstacles are detected online by the robot’s sensors. This work was motivated by and experimentally validated in the 2007 DARPA Urban Challenge, where robotic vehicles had to autonomously navigate parking lots. Our approach has two main steps. The firs...