A Standard Set of Courses to Assess the Quality of Driving Off-Road Combat Vehicles

Author:

Green Paul1

Affiliation:

1. University of Michigan

Abstract

<div class="section abstract"><div class="htmlview paragraph">Making manned and remotely-controlled wheeled and tracked vehicles easier to drive, especially off-road, is of great interest to the U.S. Army. If vehicles are easier to drive (especially closed hatch) or if they are driven autonomously, then drivers could perform additional tasks (e.g., operating weapons or communication systems), leading to reduced crew sizes. Further, poorly driven vehicles are more likely to get stuck, roll over, or encounter mines or improvised explosive devices, whereby the vehicle can no longer perform its mission and crew member safety is jeopardized.</div><div class="htmlview paragraph">HMI technology and systems to support human drivers (e.g., autonomous driving systems, in-vehicle monitors or head-mounted displays, various control devices (including game controllers), navigation and route-planning systems) need to be evaluated, which traditionally occurs in mission-specific (and incomparable) evaluations.</div><div class="htmlview paragraph">To support the use of comparable test conditions, a set of combat-relevant driving courses was developed for usability evaluations. This set of courses has been implemented in simulation (the Detroit Arsenal TBMS/CS simulator) and in the field (Camp Grayling). The 9 courses are: (1) on-road driving, (2) slalom (to avoid obstacles), (3) mogul (to assess rollover propensity), (4) ditches (which are difficult to see), (5) minefield (where the path is narrow), (6) berm drill (a defensive maneuver), (7) urban cover (scampering between buildings), (8) urban drive (narrow alleys), and (9) formation change (e.g., line to column). The courses used at Camp Grayling are documented here and representative driving data are provided to support use of these courses by others. Improvements are also suggested.</div></div>

Publisher

SAE International

Subject

Artificial Intelligence,Mechanical Engineering,Fuel Technology,Automotive Engineering

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