By Topic

Contingency Planning Over Probabilistic Obstacle Predictions for Autonomous Road Vehicles

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Jason Hardy ; Department of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY, USA ; Mark Campbell

This paper presents a novel optimization-based path planner that is capable of planning multiple contingency paths to directly account for uncertainties in the future trajectories of dynamic obstacles. This planner addresses the particular problem of probabilistic collision avoidance for autonomous road vehicles that are required to safely interact, in close proximity, with other vehicles with unknown intentions. The presented path planner utilizes an efficient spline-based trajectory representation and fast but accurate collision probability bounds to simultaneously optimize multiple continuous contingency paths in real time. These collision probability bounds are efficient enough for real-time evaluation, yet accurate enough to allow for practical close-proximity driving behaviors such as passing an obstacle vehicle in an adjacent lane. An obstacle trajectory clustering algorithm is also presented to enable the path planner to scale to multiple-obstacle scenarios. Simulation results show that the contingency planner allows for a more aggressive driving style than planning a single path without compromising the overall safety of the robot.

Published in:

IEEE Transactions on Robotics  (Volume:29 ,  Issue: 4 )