Share Email Print

Proceedings Paper

Sensitivity analysis of an optimization-based trajectory planner for autonomous vehicles in urban environments
Author(s): Jason Hardy; Mark Campbell; Isaac Miller; Brian Schimpf
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

The local path planner implemented on Cornell's 2007 DARPA Urban Challenge entry vehicle Skynet utilizes a novel mixture of discrete and continuous path planning steps to facilitate a safe, smooth, and human-like driving behavior. The planner first solves for a feasible path through the local obstacle map using a grid based search algorithm. The resulting path is then refined using a cost-based nonlinear optimization routine with both hard and soft constraints. The behavior of this optimization is influenced by tunable weighting parameters which govern the relative cost contributions assigned to different path characteristics. This paper studies the sensitivity of the vehicle's performance to these path planner weighting parameters using a data driven simulation based on logged data from the National Qualifying Event. The performance of the path planner in both the National Qualifying Event and in the Urban Challenge is also presented and analyzed.

Paper Details

Date Published: 16 October 2008
PDF: 12 pages
Proc. SPIE 7112, Unmanned/Unattended Sensors and Sensor Networks V, 711211 (16 October 2008); doi: 10.1117/12.802599
Show Author Affiliations
Jason Hardy, Cornell Univ. (United States)
Mark Campbell, Cornell Univ. (United States)
Isaac Miller, Cornell Univ. (United States)
Brian Schimpf, Cornell Univ. (United States)

Published in SPIE Proceedings Vol. 7112:
Unmanned/Unattended Sensors and Sensor Networks V
Edward M. Carapezza, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?