Accession Number:

AD1051064

Title:

Neuro Inspired Adaptive Perception and Control for Agile Mobility of Autonomous Vehicles in Uncertain and Hostile Environments

Descriptive Note:

Technical Report,01 Aug 2015,31 Jul 2016

Corporate Author:

Georgia Tech Research Corporation Atlanta United States

Report Date:

2017-02-08

Pagination or Media Count:

19.0

Abstract:

This final report summarizes the results of the work performed between for the period beginning August 1, 2015 and ending July 31, 2016, under the support of ARO MURI grant no. W911NF1110046. First, we continued our investigation into the semi-autonomous and autonomous vehicle control. This year, a major focus was on conducting large-scale experimental analyses with human subjects in the loop, to study the engagement of humans with semi-autonomous and autonomous driving technologies which were developed under this MURI program. We have also continued our work on improving the convergence rates of randomized, sampling-based planners, which have been recently shown to be capable for solving problems in high dimensional search spaces. We introduced three new algorithms, the PI-RRT that utilizes policy iteration updates, the DRRT that combines gradient descent with randomized sampling to increase convergence and the CL-RRT that uses closed-loop predictions for kinodynamic motion planning. We also investigated generalized label correcting GLC algorithms for kinodynamic motion planners and we found a very efficient scheme to generate, in a principled manner, the control primitives. In terms of perception, this last year we finalized the development of a new visual attention model which learns from human eye movements and continued our work on deciphering driver state and intentions beyond eye movements. Our perception work also focused on developing a SLAM-type of algorithm to support the MPPI controller described in last years report. Last but not least, we continued our work on developing credible autocoders to simplify the validation and verification of autonomous embedded systems. This past year we have focused on autocoders for semi-definite programs, a very important class of on-line optimization algorithms that recently proved their worth with the autonomous landing of a SpaceX Falcon 9 rocket on a barge in the middle of the ocean.

Subject Categories:

  • Operations Research
  • Cybernetics
  • Surface Transportation and Equipment

Distribution Statement:

APPROVED FOR PUBLIC RELEASE