Progress Report for the Robotic Intelligence Evaluation 

Program Year 1: Developing Test Methodology 

for Anti-Rollover Systems 

by MaryAnne Fields 

ARL-TR-3811 June 2006 

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ARL-TR-3811 June 2006 

Progress Report for the Robotic Intelligence Evaluation 

Program Year 1: Developing Test Methodology 

for Anti-Rollover Systems 

by MaryAnne Fields 

Weapons and Materials Research Directorate, ARL 

Approved for public release; distribution is unlimited. 
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14. ABSTRACT 

The Robotics Intelligence Evaluation Program (RIEP) is a joint effort by the U.S. Army Aberdeen Test Center (ATC) and U.S. 

Army Research Laboratory (ARL) to develop methodologies to evaluate robotic behavior algorithms that control the actions of 

individual robots or groups of robots acting as a team to perform a particular task. Although vehicle chassis performance will

impact the evaluation of robotic behavior algorithms, testing of intelligent robotic platforms requires more than the classic 

automotive tests. The tests and procedures required to evaluate these algorithms cross the traditional boundaries between 

operational and developmental testing and between hardware and software testing. This document is a discussion of an antirollover case study used to refine the RIEP methodology. We begin with a discussion of vehicle rollover and prevention 

systems. Most of the information for these sections comes from the automotive industry where anti-rollover technologies are 

becoming important safety features. We discuss the current measures and tests used by automotive safety engineers and some 

of the additional elements that need to be considered for robotic systems. We also discuss several simulation tools applicable to 

vehicle rollover studies. Finally, we make recommendations for virtual and physical tests of anti-rollover systems. 

15. SUBJECT TERMS

 robotic behavior; test and evaluation; test methodology; vehicle roll-over 

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iii

Contents 

List of Figures iv

1. Introduction 1

2. Vehicle Rollover 5

3. Rollover Prevention 6

4. Current Measures and Tests 7

5. Roll-Over Testing for Robotic Systems 10

6. Simulation Tools 12

7. Virtual Environment Requirements 16

8. Perception Models 17

9. Recommendations 18

10. Conclusions 21

11. References 23

Distribution List 26
iv

List of Figures 

Figure 1. The 4D-RCS robotics control architecture......................................................................3 

Figure 2. Embedding a behavior algorithm in a virtual environment.............................................4 

Figure 3. A spiral path showing the required rollover velocity. .....................................................9 

Figure 4. Hypothetical vehicles with SSF = 1.5 and SSF = 1.0 (NHTSA, 2005)...........................9 

Figure 5. Paths between point A at the upper left edge of the map and point B in the lower 

right region of the map.............................................................................................................12 

Figure 6. An OTB simulation comparing two similar vehicles executing slowly increasing 

steering maneuvers on flat ground...........................................................................................15 

Figure 7. A notional slalom course to test autonomous systems. .................................................19 

Figure 8. Three paths through a test field. ....................................................................................20 
1

1. Introduction 

The Robotics Intelligence Evaluation Program (RIEP) is a joint effort by the U.S. Army Aberdeen 

Test Center (ATC) and U.S. Army Research Laboratory (ARL) to develop methodologies to 

evaluate robotic behavior algorithms that control the actions of individual robots or groups of 

robots acting as a team to perform a particular task. Although vehicle chassis performance will 

impact the evaluation of robotic behavior algorithms, testing of intelligent robotic platforms 

requires more than the classic automotive tests. The tests and procedures required to evaluate 

these algorithms cross the traditional boundaries between operational and developmental testing 

and between hardware and software testing. Developmental testing evaluates behavior algorithms 

using performance specifications contained in the relevant system contract. However, these 

specifications define parameters such as chassis performance, sensor detection levels, and low 

level behaviors which might include maximum on-road speed, minimum obstacle height detection, 

maximum range at which system can detect a human-sized obstacle, and so on. Specifications for 

higher level behaviors will likely be less well defined. In some sense, evaluating behavior 

algorithms is similar to evaluating new tactics, techniques, and procedures (TTPs) for manned 

systems. Battlefield simulation tools are generally used to allow researchers to look at the effect of 

TTPs on combat performance measures, and these same tools are key to the evaluation of 

intelligent robotic systems. Behavior algorithm performance is affected by software design as well 

as vehicle design, but these algorithms are generally driven by on-board sensors and external 

information sources such as human operators or situational information systems that supply the 

input that determines specific actions within the behavior algorithm. 

There are four aspects of the RIEP process. First is a thorough analysis of the task addressed by 

the behavior algorithm. Once the task is understood, the second aspect of the methodology is to 

find a simulation environment that is “rich” enough to simulate the task and test the proposed 

algorithm. The third aspect of RIEP is to determine a suitable method to link the proposed 

algorithm to the simulation environment. The last aspect of the program is to design a set of 

meaningful tests and performance measures to evaluate a proposed behavior algorithm. 

The first step of the REIP process is a thorough analysis of the task addressed by the behavior 

algorithm. The emphasis is on the task, not the algorithm, since many algorithms can be 

proposed to solve a specific task. Behavior algorithms are being designed to perform tasks 

ranging from system health monitoring to battlefield missions such as area reconnaissance. The 

four-dimensional real-time control system (4D-RCS), an architecture for designing robot control 

systems, provides a convenient way to rank task complexity and to look at the resolution of 

information required by each task. Figure 1 provides a diagram of the 4D-RCS architecture. 

Each level of the architecture contains three components: an executor that accepts plans and 

information from the next higher level; a planner that directs the actions of the robot and sends 
2

tasks to the next lower level; and a world model that contains sensor information. At the servo 

level, the robot controls the actuators that move the vehicle and subsystems such as cameras on 

board the robot itself. The robot acts quickly, typically on a microsecond scale, on information 

provided by on-board sensors. The primitive level involves low-level vehicle tasks such as 

obstacle avoidance, image processing, etc. The subsystem level combines tasks into important 

activities, such as autonomous mobility or reconnaissance. At the vehicle level, the robot 

performs functions that are tactically useful on a battlefield. At the section level, these functions 

extend to include tasks that are performed by groups of robots. The 4D-RCS continues beyond 

the levels shown in figure 1; the higher levels resemble levels of military organizations. 

Tasks can involve multiple levels of the 4D-RCS structure. For instance, a reconnaissance 

mission is a section-level task that involves a team of robots that provides information about a 

designated area of the battlefield. Each robot performs independent vehicle-level behaviors that 

contribute to the overall mission. These vehicle-level tasks can be subdivided into three distinct 

subsystems: mobility, sensing, and weapon control. Autonomous mobility involves map-based 

planners and obstacle-avoidance systems. Autonomous sensing may involve several sensors and 

fusion algorithms that combine information from multiple sensor sources. Once we understand 

the task, we can identify critical elements of the environment needed to support an evaluation of 

algorithm designed to address the task. Generally, we need to consider terrain topography, 

composition, surface features, weather elements such as wind and rain, and the simulation tool. 

The world-model blocks from figure 1 provide a rough estimate of the level of detail required to 

stimulate tasks at each level of the architecture. Section-level behavior tasks depend on very 

crude information such as the location of lakes or canopy areas. At the subsystem level, tasks 

depend on detailed environmental information such as the location of ditches and individual 

trees. The primitive level requires even more detail, such as the location, shape, and composition 

of terrain features. 

The choice of the simulation tool can also be tied to the 4D-RCS structure. Effective testing of 

section-level behaviors requires a simulation tool capable of representing the position of 

individual vehicles and modeling engagements between two or more forces. Also, it may be 

desirable to model at least some of the command and control (C2) structure for the unit that 

executes the behavior algorithm. Sensor information is represented as polygonal and linear 

“map” features that become known as the vehicle moves around in the simulated world. 

Subsystems such as the autonomous mobility system require simulation tools that can model the 

position and orientation of each vehicle. Sensor models provide information about the 

immediate vicinity of the vehicle. Visual sensor performance models interact with objects 

approximately 10 to 20 inches in radius. The primitive level requires tools that can model the 

position and orientation of vehicle components. At this level, it becomes important to model the 

process of sensing the environment. 
3

Figure 1. The 4D-RCS robotics control architecture. 

Linking the behavior algorithm that is undergoing test to the virtual world can be accomplished in 

two ways. The first, a dissection approach, is to rewrite the behavior algorithm as an element of 

simulation tool used in the analysis. If this approach is carefully executed, it leads to detailed 

understanding of the algorithm undergoing test, the sensory information required to drive the 

algorithm, and the relationship between the behavior algorithm and the host robotic platform. 

This approach is useful in building a behavior algorithm for complex force-on-force models. It is 
4

also useful for modularized behavior algorithms that can be separated into component parts with 

the 4D-RCS architecture as a guide. As a module in the behavior is changed, that module of the 

simulation can be revised. Researchers can use these systems to study the response of high-level 

behaviors to different levels of performance of the sub-behaviors. To be successful, this approach 

requires close collaboration between the behavior developer and the behavior evaluator. 

The second method, a “black box” approach, is to link the behavior system embedded in the robot 

with the virtual environment. Often, this is the preferable approach since the algorithm cannot be 

modified during the process. Figure 2 shows a graphic that illustrates this approach. The behavior 

algorithm in the middle block is the system undergoing test. Information from the virtual world 

provides the behavior algorithm with simulated sensor input. Sensor models in the virtual 

environment must be capable of supplying sensor information at the level of detail required by the 

behavior algorithm. Commanding entities provide C2 information by providing orders, routes, and 

situational awareness overlays. Information also flows from the behavior algorithm to other 

entities in the exercise. Both of these connections depend on a communica-tion model that models 

the flow of information in and out of the behavior algorithms. The key to success for this approach 

is to define the interfaces between behavior algorithm and the virtual environment. 

Behavior

Algorithm

Undergoing

Test

Sensed terrain features

Sensor Performance 

Models

Sensor in the Loop

terrain elevation and 

mapped terrain features

common operating picture

commands from real 

and simulated entities

Figure 2. Embedding a behavior algorithm in a virtual environment. 
5

The last step of the RIEP is to design a set of meaningful tests to characterize behavior algorithm 

performance in battlefield environments. Test scenarios need to include environmental 

challenges that allow researchers to assess the robustness and reliability of the behavior 

algorithm undergoing test. Testing low-level behavior algorithms such as autonomous mobility, 

resembles traditional testing, i.e., the virtual environment is populated with specific features 

designed to test the response of the system. Often, these tests can be tied directly to a contract 

specification or to an operational requirements document (ORD). Higher level behaviors need to 

be tested in the context of force-on-force scenarios. ORDs and contract specifications are often 

vague about performance expectations for high-level behaviors, so it is useful to examine a series 

of force-on-force scenarios designed to find performance limitations. Early in the development 

cycle, well-designed force-on-force scenarios can help focus the development effort on 

frequently occurring issues and away from rarely occurring issues. One issue that needs to be 

addressed with high-level behaviors is the behavior undergoing test. One approach is to regard a 

test of a high-level behavior as a test of all the underlying algorithms. Since most of the 

subsystems are not yet finalized, we may need methods to isolate the performance of high-level 

algorithms from the lower level algorithms. 

The RIEP process is being refined through a series of case studies, beginning with subsystemlevel behaviors and ending with the evaluation of a mixed human-robot reconnaissance behavior. 

In this first year of the process, we have chosen to examine vehicle rollover prevention 

algorithms. Rollover prevention is critical for the safety of manned and unmanned systems. It is 

also specified by the ORD for some near-term robotic systems. 

The remainder of this document is a discussion of the progress of RIEP during the first year. We 

begin with a discussion of vehicle rollover and prevention systems. Most of the information for 

these sections comes from the automotive industry where anti-rollover technologies are becoming 

important safety features. Sections 4 and 5 discuss the current measures and tests used by 

automotive safety engineers and some of the additional elements that need to be considered for 

robotic systems. We discuss several simulation tools applicable to vehicle rollover studies. 

Finally, we make recommendations for virtual and physical tests of anti-rollover systems. 

2. Vehicle Rollover 

Although we consider primarily “car-like” robots on relatively flat surfaces in this research, it is 

useful to provide a general definition of vehicle rollover that applies to traditional wheeled and 

tracked vehicles as well as vehicles with legs, large mobile manipulators, outriggers, or other 

novel characteristics operating in a wide variety of terrains. Consider an arbitrary ground vehicle 

that has n points of contact with the ground. These n contact points form a support polygon for 

the ground vehicle. A rollover or “tip-up” occurs when the vehicle’s center of gravity (c.g.) 
6

moves far enough outside the support polygon to cause the vehicle to rotate around one of the 

polygon sides (Papadopolos & Rey, 1996). In automotive applications, cars typically roll 

laterally around either the left or right set of wheels. 

Dynamic rollovers can be divided into two classes: tripped and untripped. Tripped rollovers are 

initiated by a mechanism such as a curb, guardrail, soft soil or road edge. Tripped rollovers are 

often preceded by a loss of vehicle control that allows the vehicle to leave the road surface. 

Untripped rollovers are initiated by vehicle maneuvers on a normal road surface. According to 

the National Automotive Sampling System Crashworthiness Data System (NASS CDS) 

maintained by the National Highway Traffic Safety Administration (NHTSA), less than 5% of 

the rollover crashes are untripped (Hertz, 1999). Despite their low frequency of occurrence, 

much of NHTSA’s research efforts focuses on developing extreme steering maneuvers to test 

untripped rollovers (Forkenbrock, Garrott, Heitz, & O’Harra, 2002; Forkenbrock, O’Harra, & 

Elasser, 2004; Howe, Garrott, Forkenbrock, Heydinger, & Lloyd, 2001). Untripped rollovers are 

directly related to controllable factors such as vehicle design and driver behavior. Tripped 

rollovers are also related to vehicle design and driver behavior, but any vehicle will roll over if it 

impacts a suitable tripping mechanism with sufficient lateral velocity. 

3. Rollover Prevention 

Unmanned systems are still in the development stage of their life cycle. Consequently, we do not 

have a specific anti-rollover system to test. However, anti-rollover systems developed for ground 

robots are likely to be similar to those developed for the automotive and commercial vehicle 

industries. These industries are very interested in developing methods to predict and prevent 

vehicle rollover. Their development efforts include “driver-based” strategies designed to reduce 

the probability of encountering a near-rollover condition and vehicle-based systems such as 

rollover warning sensors, suspension system components such as anti-roll/sway bars and vehicle 

stability control (VSC) systems designed to recover from a near-rollover condition. Each of these 

efforts is potentially applicable to unmanned systems. 

Rollovers can be prevented by training drivers to maintain vehicle control. Tire maintenance 

affects the controllability of the vehicle. Cargo loading strategies can change the c.g. affecting 

rollover propensity. Drivers can be taught emergency maneuvers that help them maintain control 

of the vehicle. Some driver education translates to the control of unmanned vehicles. Vehicle 

maintenance and loading is similar for manned and unmanned systems. For tele-operated systems, 

the operator can be trained to avoid excessive speed and steering. For autonomous systems, the 

robot planning algorithms can consider vehicle stability as a factor as it plans its path. 

In the last decade, many groups have begun to develop rollover warning systems for passenger 

and commercial vehicles. Under the Intelligent Highway Program, “smart” signs can warn 
7

trucks of impending rollover by measuring the speed of the truck. Chen and Peng (1999) have 

developed a time-to-rollover warning algorithm that can warn drivers of impending rollovers, 

based on the vehicle yaw rate. Rey and Papadopoulos (1997) have developed a warning 

algorithm applicable to forestry equipment with booms, cranes, or other manipulators. 

Automobile manufacturers have also developed rollover sensors. However, most of these 

sensors are used to determine when protective devices such as air bags should be deployed to 

protect the vehicle occupants. Warning systems can allow operators of tele-operated systems to 

correct unsafe vehicle operations. They could also provide an audio or visual rollover warning to 

troops in the vicinity of an unmanned system. The sensors and algorithms of the rollover 

warning systems can be incorporated into autonomous planning systems. 

Suspension system components such as anti-roll or sway bars control the body roll in cornering 

maneuvers. Another vehicle design technology (active rear steering) is designed to increase 

vehicle maneuverability at high speeds. Hac (2002) examined the effect of these vehicle design 

choices on rollover propensity. 

VSC systems are designed to assist drivers in maintaining steering control of their vehicles. 

These systems use yaw rate sensors to compare the actual path of a vehicle to its intended path. 

Automatic braking can be applied independently to each wheel to compensate for excessive 

oversteer or understeer. VSC indirectly prevents rollover by keeping the vehicle on the road 

surface. There are some new (2005) systems that attempt to directly prevent untripped rollover 

by incorporating roll rate and lateral acceleration sensors. Again, the control mechanism is to 

decelerate the vehicle and to reduce the sharpness of the turn. Unmanned systems can benefit 

from VSC technology. Tele-operated systems could use the technology to compensate for 

system latency, lack of driving awareness, or over-eager operators. Autonomous systems could 

use VSC technology to monitor and adjust the vehicle yaw rate; this may be particularly 

important on loose soil or ice-covered surfaces. 

4. Current Measures and Tests 

Highway rollover crashes are a major safety concern. Consequently, automotive safety engineers 

in NHTSA, the automotive industry, insurance industry, and consumers’ groups have developed 

tests designed to measure the rollover propensity of passenger and commercial vehicles. Their 

concerns as they develop these tests are similar to ours, namely, test repeatability, relevance to real 

driving issues, and crash rate predictability. Some of the tests are static measures based on vehicle 

parameters such as vehicle weight and the location of the c.g. Newer tests are dynamic maneuvers 

designed to look at vehicle performance in a series of maneuvers designed to mimic worst case 

emergency maneuvers. 
8

A common measure of rollover propensity is the static stability factor (SSF). This measure is 

derived from the physics of rigid bodies. Consider a vehicle to be a rigid body with mass m and 

width t moving along an arc of radius r at a speed v on a flat surface. Suppose that the height of 

the c.g. for this vehicle is h. The vehicle rolls on its side if the lateral forces given in the righthand side of equation 1 exceed the vertical forces given on the left-hand side. 

2

2

t mv mg = h

r

⎛ ⎞ ⎜ ⎟ ⎝ ⎠ (1) 

Rearranging equation 1 provides the definition for the SSF: 

2

2

t v =h rg (2) 

The left-hand side of equation 2, which is defined to be the SSF, depends only on measurable 

vehicle parameters: the c.g. and vehicle width. The right-hand side of the equation gives a 

practical safety formula specifying safe combinations of speed and turn radius. Points along the 

spiral path shown in figure 3 have been labeled with the rollover speeds for two different values 

of the SSF. The circled numbers indicate the turn radius of the spiral path in meters. The blue 

numbers indicate rollover speeds for vehicles with SSF = 1.0. The red numbers indicate 

rollovers for a vehicle with SSF = 1.5. Figure 4 shows a size comparison between vehicles with 

SSF = 1.0 and SSF = 1.5. 

Equation 2 applies to vehicle on flat ground. A more general equation relates the lateral 

acceleration to the SSF and the side slope angle, φ. 

SSF tan 2

SSF tan 1

v

rg

− = +φ

φ

 (3) 

Other static rollover propensity measures are the tilt table ratio (TTR), the side pull ratio (SPR), 

and the critical sliding velocity (CSV). In the tilt table test, the vehicle is placed on a table and 

tilted laterally until the front and rear wheels on the uphill side lift. The tangent of the 

longitudinal table angle is the TTR. The SSR is the ratio of the vehicle weight to the lateral force 

required to cause two-wheel lift. The CSV is lateral velocity required to cause two-wheel lift 

when the vehicle strikes a tripping obstacle such as a curb. It can be computed with the use of 

the vehicle mass, M, the vehicle width, T, the height of the c.g., H, and the roll moment of 

inertial about the c.g., Ixx (Heydinger et al., 1999). 

H H , 4

T

MH

2gI CSV 2

2

2

oxx

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ = + − (4) 

with 

H . 4

T I I M 2

2

oxx xx ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ = + + (5) 
9

Huang (Huang, 2002) has a detailed derivation of the CSV equation.

Figure 3. A spiral path showing the required rollover velocity. 

Figure 4. Hypothetical vehicles with SSF = 1.5 and SSF = 1.0 (NHTSA, 2005). 

Since the static measures can be collected in laboratory settings, these tests are considered very 

repeatable. However, these measures, especially the SSF, have been criticized for being too 

simplistic. The SSF and the CSV treat the vehicle as a solid block, giving no credit to the 

stabilizing influence of the suspension system. There is also some concern that manufacturers 
10

can optimize their vehicles to “score” well on the CSV without any improvement in the safety of 

the vehicle (NHTSA, 2004). Since they are static measures, they cannot be used to evaluate the 

performance of VSC systems. However, the SSF correlates well with existing accident statistics. 

Also, according to NHTSA, the static measures are the best predictors of vehicle performance 

during a potential trip. 

Dynamic rollover propensity maneuvers allow the vehicle performance to be tested in situ. 

They allow the complex interactions between the suspension system and the vehicle chassis and 

environmental factors such as road surface and cross winds. Test maneuvers can also evaluate 

the effect of VSC systems on vehicle rollover. However, the very elements that make test 

maneuvers relevant to real-world driving make repeatability difficult. Right now, there is not 

enough data to determine the relationship between driving maneuver performance and rollover 

accident statistics. 

The driving maneuvers used by automotive safety engineers include slowly increasing steer, 

j-turn, and fishhook (Howe, Garrott, & Forkenbrock, 2001). In a slowly increasing steer 

maneuver, the driver keeps the speed constant and gradually increases the steering wheel angle. 

In a related maneuver, the steering wheel angle is held constant while the speed is gradually 

increased. The primary use of these maneuvers is to characterize the responsiveness of the 

vehicle and to determine the maximum obtainable lateral accelerations. The j-turn is a sudden 

large turn similar to the maneuvers required to negotiate a cloverleaf ramp. The fishhook 

maneuver involves a large steering wheel angle change followed by a large steering wheel angle 

change in the opposite direction. It is analogous to maneuvers to avoid obstacles in the roadway. 

The use of steering and braking machines increases the repeatability of the maneuver tests. 

Performing these maneuvers on a test track is still a risk to the drivers and the vehicles. 

Roadway simulators such as the one at ATC can reduce risk to the test article and human 

participants and increase test repeatability (Connon, 2005). 

Crashworthiness studies use tests designed to deliberately induce a rollover. These include 

driving onto corkscrew ramps, sliding into tripping hazard such as posts or gravel pits, or driving 

into a hole. A common use for these tests has been to examine occupant safety. Recently, Viano 

and Parenteau (2004) have developed a series of tests to define rollover sensor requirements for 

applications such as air bag deployment. According to their research, the results of these 

instrumented repeatable tests mimic about 90% of real-world rollover situations. 

5. Roll-Over Testing for Robotic Systems 

The previous section described tests applicable to passenger vehicles driven on highway systems. 

These tests are applicable to robotic systems, but there are additional factors that need to be 
11

considered for robots. Situational awareness, communication delays, perception, and path 

planning all have a potential impact on rollover propensity. 

The robots considered in this research are either tele-operated or autonomously driven. Each of 

these control mechanisms has its own challenges with respect to evaluating rollover propensity. 

Tele-operated systems depend on the human operator to perceive and avoid navigational hazards. 

These systems require a very-high-bandwidth, low-latency, and highly reliable communications 

system. Delay in the communications system, coupled with the limited perception available to 

the operator, can lead to vehicle rollover. A study of tele-operated system accidents from early 

1990s found that 60% of the accidents were rollovers (McGovern, 1991). Generally, the 

operators had only limited awareness of the vehicle orientation and were unable to sense nearrollover conditions before they became irreversible. More recent work in the field of urban 

search and rescue (USAR) robots found that situational awareness remains an important issue 

(Burke, Murphy, Coovert, & Riddle, 2004). 

Autonomous systems use planners to process a priori information such as digital maps and 

acquired information such as sensor readings to navigate. In systems using hierarchical control 

architecture, the planner is divided into subsystems that operate at different frequencies. The 

mission planner translates the overall mission objectives into tasks and specific destinations for 

the robot; the mission plan is revised infrequently as the mission changes. The navigator does 

global path planning based on an a priori map and situational awareness information; paths are 

replanned as tactical information (such as known enemy positions) changes. The pilot does 

moment-to-moment trajectory planning. It monitors current position and pose (roll, pitch, yaw) 

using a variety of means such as an IMU (inertial measurement unit), GPS (global positioning 

system), and odometry with estimates from all, combined by a Kalman filter or something 

similar. Trajectories change frequently as the perception system acquires new information about 

the environment (Board on Army Science and Technology [BAST], 2002). 

Figure 5 shows a contour map and three of many possible paths between points A on the upper 

left edge of the map and point B in the lower right region of the map. A robotic system chooses 

the “best” path by minimizing the cost of each path. Costs include travel time, exposure to 

known enemy positions, vehicle safety, and other factors. In this example, the red path may 

minimize exposure to an enemy in the upper right area of the map, but it may not be safe for the 

robot to travel along the steep contours of the hills. Plans evolve, as the blue and green plans 

illustrate. In this illustration, the blue path incorporated additional sensor information that 

became available when the robot reached the middle of the map. 

Path planning decisions can impact rollover propensity. Planners that do not incorporate system 

safety into the cost function may be more likely to roll than planners that consider vehicle 

orientation as a cost variable. Exercising the planners on a variety of realistic battlefields allows 

researchers to evaluate the rollover sensitivity of the planning process. 
12

Figure 5. Paths between point A at the upper left edge of the map and point B in the 

lower right region of the map. 

6. Simulation Tools 

Modeling and simulation programs are becoming common tools in automotive safety engineering. 

In this section, we discuss the use of simulation tools to supplement the laboratory measurements 

and field testing for rollover propensity. Three common simulation tools, PC-Crash1

, TruckSim, 

and CarSim2, are used in the rollover literature. We also discuss two tools developed specifically 

for military use: Vehicle Dynamics Mobility Server (VDMS) and One Semi-Automated Forces 

(OneSAF). VDMS is similar to the commercial automotive simulation packages used in the 

literature. OneSAF is a battlefield simulation tool. 

PC-Crash is a momentum-based simulation tool which is used in accident reconstruction. It can 

simulate scenarios involving as many as 32 entities including cars, trucks, pedestrians, and 

bicyclists. Vehicles are described by geometric properties such as size, weight and the c.g., 

suspension properties such as stiffness and tire characteristics, power train properties, and 

 1

PC-Crash is a trademark of Dr. Steffan Datentechnik Ges.m.b.H. 

2TruckSim and CarSim are trademarks of Mechanical Simulations Corporation. 
13

moments of inertia. Aspects of the simulated environment such as wind speed, obstacle 

locations, and road surface characteristics can be modified. In addition, PC-Crash has two 

different driver models to control the vehicles. Often, PC-Crash is combined with a finite 

element model (e.g., LS-DYNA3

, MaDymo4, and PAM-Crash5) to look at vehicle and occupant 

damage. 

Viano and Parenteau (2004) used PC-Crash to help develop test procedures and equipment to 

support their work on rollover sensors. This tool was used to simulate various tripped rollover 

maneuvers and to set experimental parameters such as impact speed and driving conditions for 

the field rollover tests. Since this tool is often used in court cases, there have been several 

validation studies. Cliff and Montgomery (1996) conducted some early validation studies. 

Later, Cliff and Moser (2001) compared PC-Crash results to staged vehicle crashes. Recently, 

Gopal, Baron, and Shah (2004) published some initial validation studies comparing PC-Crash 

results to controlled laboratory rollover tests. 

CarSim and TruckSim have been used for more than 20 years to study the dynamics of 

automobiles and trucks. CarSim simulates the dynamic behavior of race cars, passenger cars, 

light trucks, and utility vehicles. Typically, a vehicle is described as a 14-degree-of-freedom 

(DOF) system. The chassis has three rotational and three translational degrees of freedom, and 

each wheel can rotate on its axle and move vertically. TruckSim is a similar product designed 

for analyzing commercial trucks and articulated vehicles. New vehicle models are created with 

the CarSim-TruckSim user interfaces provided by the simulation package or by models created 

with the MATLAB6 and SIMULINK packages commonly used as design tools in the automotive 

industry. The road surface can be modified to represent many different environments, including 

off-road areas. 

Since CarSim is a multi-component model, it has been used to study the interactions of the 

suspension systems with the chassis (Sharp & Bettella, 2001). By linking the vehicle model with 

external simulation tools, other researchers have used the simulation to develop vehicle stability 

control algorithms and sensors (Eisele & Peng, 2000). Anwar (2004) used the model to develop 

a traction control algorithm. Chen and Peng have used it to investigate rollover warning systems 

for trucks. It has been linked to motion simulators and it can support human driver studies. 

Ungoren, Peng, and Tseng (2004) have also used CarSim to iteratively develop a series of worst 

case maneuvers to test vehicle stability control systems for sport utility vehicles. 

There have been some validation studies as well. Alonso provides a comparison between a 

general 14-dof vehicle model and field maneuvers (Alonso, 2005). Ungoren et al. (2004) 

compared the performance of a TruckSim Jeep Cherokee model to field data for banked turn 

 3LS-DYNA is a trademark of Livermore Software Technology Corp. 

4

MaDymo is a trademark of the Netherlands Organization for Scientific Research. 

5

PAM-Crash is a trademark of the ESI Group. 

6MATLAB and SIMULINK are registered trademarks of the MathWorks. 
14

maneuvers. Kalwauchi et al. (2005), found a favorable comparison between CarSim models and 

actual car performance on an oval test track. 

Another simulation tool to consider is Tank-Automotive and Armaments Command (TACOM)-

TACOM Research, Development, and Engineering Center (TARDEC’s) VDMS (Brudnak, 

Nunez, & Reid, 2002). VDMS represents a vehicle as a 6-DOF chassis and four vehicle corners 

that represent the wheels and suspension system components at each wheel. New vehicle models 

are created by commercial packages such as SimCreator or MATLAB or SIMULINK. VDMS 

represents several aspects of vehicle dynamics including power and traction limits on mobility 

because of terrain slope and composition, vibration effects because of surface roughness, and 

vehicle instabilities such as rollover. Vehicle control is provided by an external interface such as 

joystick for tele-operated systems or by external control algorithms for autonomous systems. 

The model operates at 200 to 500 Hz which could support interactions with vehicle attitude 

sensors and anti-rollover algorithms that must operate quickly to stabilize the vehicle. VDMS 

can be used as a stand-alone model or as part of a distributed modeling environment. VDMS is a 

component of the Modeling Architecture for Technology and Research Experience (MATREX) 

federation of models developed by the U.S. Army Research Development and Engineering 

Command. VDMS was used as the mobility thread in the distributed test environment 

demonstrations (DTE4 and DTE5). 

VDMS is part of the high-fidelity ground platform and terrain mechanics modeling (HGTM) 

science and technology objective (STO) to develop high fidelity, real-time, ground platform 

mobility and terrain models. As a part of this effort, TARDEC and the Army Corps of Engineers 

are developing tire-soil interaction models which will better represent tire slippage and sinkage 

(Richmond, Jones, Creighton, & Ahlvin, 2004). 

Another tool to consider is Virtual.Lab by LMS7 Engineering Solutions. Virtual.Lab Track 

Motion delivers powerful simulation capabilities specifically developed for track vehicle 

engineering. With Virtual.Lab Track Motion, engineers can assess the interaction of the vehicle 

with different terrain profiles to (a) study stability on a slope, in acceleration, in braking, or in lane 

changing (b) evaluate the vehicle’s handling, and (c) optimize driver and passenger comfort. The 

solution computes the loads between track links and suspension parts and on vehicle bodies. It 

also gives guidance to the spring and shock absorber properties and to the optimal location of road 

wheels, idlers, sprockets, etc. 

OneSAF test bed baseline (OTB 2.0) is an entity-level battlefield simulation tool used by many 

groups in the U.S. Army. The behavior of each entity is controlled by a collection of function 

libraries that handle low-level functions such as movement, sensory processing, or weapon 

control. Ground vehicles are represented as simple bodies having length, width, and mass with 

tracked or wheeled dynamics. The baseline OTB tool does not represent vehicle rollover; 

instead, the dynamics library limits maximum possible steering angle. 

 7LMS is not an acronym. 
15

While OTB and other battlefield simulation tools have limited value in developing engineering 

tests of vehicle rollover, they are important in determining the effect of vehicle rollover on 

overall system of system performance in realistic battlefield missions. As a part of our work, we 

extended a version of OTB 2.0 to allow vehicle rollover using the simple static stability 

relationship provided in equation 3. This rollover extension required the vehicle definition files 

to be modified to include the c.g. height. The side slope of the terrain was used as an estimate of 

vehicle yaw. Once the conditions for equation 3 are satisfied, the vehicle status changes from 

healthy to mobility killed. Figure 6 shows two similar vehicles executing a slowly increasing 

steering maneuver on flat ground. The vehicle on the right is more sensitive to rollover because 

it has a higher c.g. This maneuver is somewhat contrived, but with the SSF equation, researchers 

could investigate the effect of vehicle load on overall mission performance. 

Figure 6. An OTB simulation comparing two similar vehicles executing slowly increasing steering maneuvers 

on flat ground. 

The OneSAF Objective System is expected to be released in the spring of 2006. It is expected to 

use the Standard Mobility Model Application Programming Interface (STNDMob API). 

STNDMob API is being developed to consistently represent vehicle mobility for systems in 

battlefield simulations based on the North Atlantic Treaty Organization (NATO) Reference 

Mobility Model (NRMM) (Baylot et al., 2005). STNDMob provides reasonable speed limits for 
16

vehicles based on vehicle type and the physical interaction between the vehicle and the terrain 

surface. STNDMob uses the SSF equations to determine the maximum safe speed. 

7. Virtual Environment Requirements 

In this section, we discuss the requirements that rollover modeling imposes on the virtual 

environment. We address two separate issues: simulating rollover propensity test maneuvers 

and simulating rollovers related to realistic battlefield driving. 

The virtual environment required to simulate rollover propensity test maneuvers is relatively 

simple. In its most recent studies, NHTSA performed rollover propensity test maneuvers on a 

level test pad with a known, constant coefficient of friction. The digital terrain associated with 

each of the tools described in the previous section can support these rollover tests. It is possible 

to introduce variations into the basic maneuver tests by adding hills and other large terrain 

features. It is also possible to vary the surface composition. VDMS, OTB, and OneSAF store 

trafficability indices and terrain attributes for each elevation post in the terrain database. CarSim 

and TruckSim assign a friction map to the terrain surface. 

Simulating rollovers related to realistic battlefield driving requires an analysis of future robotic 

mission profiles. In its 2002 study, the National Academy of Sciences stated that future Army 

mission profiles show that 70% to 80% of troop movement will use primary or secondary roads 

(BAST, 2002). Robotic systems are likely to have similar mission profiles requiring them to 

maneuver on and near road surfaces. 

In OneSAF and OTB terrain databases, a road segment is a piecewise linear feature consisting of 

a collection of points on the battlefield, a road width, and soil type for the road surface (PEO 

STRI, 2004). CarSim and TruckSim databases describe roads as collection of geometry files 

specifying the three-dimensional (3-D) location of the road centerline and an elevation map of 

the road surface. The description also includes a friction map of the road surface. Open flight 

databases, used by the VDMS tool, can support detailed road descriptions with 3-D road 

geometry, and roadside features such as shoulders, curbs, and signs. In their crash maneuver 

work, Gopal, Baron, and Shah (2004) replicated various road geometries, road features such as 

curbs, and various surfaces such as asphalt, gravel, or loose soil using PC-Crash. 

Robots commanded to follow the road, like automobiles on a highway, may occasionally leave 

the road surface because of obstacles in the roadway, lack of perception, or inaccuracies in the 

planning process. With a high fidelity representation of the roadway environs, researchers can 

investigate interaction with potential tripping mechanisms such as gravel shoulders, road edges, 

or curbs. 
17

In an off-road environment, vehicles encounter terrain surfaces with different textures, 

roughness, and friction properties. In addition, mobility hazards such as holes and large rocks 

are more prevalent in an off-road environment. Battlefield simulation tools such as OTB and 

OneSAF use trafficability indices to determine speed, climb, and turning rate limitations over 

particular sections of terrain for each type of vehicle. The trafficability indices are derived from 

the NATO NRMM with the use of factors such as soil strength, vegetation, and terrain slope. 

Holes, rocks, fallen logs, and other potential tripping mechanisms are not generally represented 

in these terrain databases. However, it is possible to add tripping features to OTB terrain 

databases. 

8. Perception Models 

Simulations of test track maneuvers do not require sophisticated perception models. The 

vehicles drive the prescribed course on a smooth level surface that is assumed to be free of 

obstructions. However, for a robotic system operating in realistic settings, a rich virtual 

environment and a good dynamics model are not sufficient to represent a robot’s behavior in a 

potential rollover situation. It is also necessary to model its perception of the environment. A 

suitable perception model represents the hazards that the robot “sees” with its perception system, 

the hazards it identifies, and the amount of time the perception process takes. 

Robotic systems use a variety of systems to perceive the environment. These include scanning 

laser radar systems, visible and infrared cameras, acoustic sensors, and radar systems. First 

principle physics models of these sensors require the shape, composition and density of obstacles 

in the environment, atmospheric models, and models of the sensor information collection 

process. Such models do exist, but they require information that might be difficult to collect for 

a variety of battlefield environments. 

Another approach is to model the “end product” of the perception system. This would be the 

information the robot uses to drive, such as a world map or, in the case of a tele-operated system, 

the information displayed to the operator as s/he remotely drives the robot. Such models provide 

the probability of detection for features in the environment as a function of obstacle range, 

orientation, vehicle speed, weather conditions, and other factors. Ideally, these models would be 

built with the use of data collected by organizations building robotic driving sensors. However, 

generalized functions could be used in conjunction with simulation studies to look at the 

sensitivity of rollover propensity to driving sensor performance. 
18

9. Recommendations 

In the previous sections of this report, we analyzed the rollover problem, identified some 

potential anti-rollover systems, and discussed virtual simulation tools that could be used in 

rollover research. In this section, we make some recommendations for a rollover testing 

program. We recommend three levels of testing. First, laboratory measures such as the SSF and 

the CSV provide insight into the inherent stability of the platform. On the sub-system level, we 

need tests to verify that the anti-rollover system works as the designer intends. More 

importantly, we need to establish the rollover propensity for the robotic vehicle, especially in the 

off-road environment where we do not have historic data. 

At the subsystem level, we need to verify that the anti-rollover system triggers in time to prevent 

an accident. In the previous discussion, there were two levels of anti-rollover prevention: those 

connected with the vehicle that engage in a near-rollover condition and those connected with the 

“driver” that reduce the probability of encountering a near-rollover condition. Vehicle-based 

systems, including rollover warning sensors and differential braking systems, activate in 

response to unacceptable yaw or roll rates. The NHTSA maneuvers provide high yaw rates for 

stimulating the anti-roll system. Ramp and hill maneuvers that elevate one side of the vehicle 

could provide high roll rate stimulation. Note that these maneuver tests use steering machines 

and auto-pilots to ensure that vehicles drive the same path. Maneuvers along pre-determined 

paths do not provide useful information for “driver”-based systems such as terrain adaptive 

planning algorithms. Driver-based systems adjust the vehicle path to reduce the probability of 

encountering a near-rollover condition. Handling tests, such as the slalom maneuver shown in 

figure 7, in which the vehicle picks its own path are more suitable for testing these systems. 

Performing the vehicle-based and driving-based maneuvers on a test track increases the 

repeatability of these tests. 

It is essential to protect vehicles and personnel during testing. Vehicle-in-the-loop systems such 

as ATC’s Roadway Simulator offer a safe alternative to testing on tracks for wheeled vehicles 

that weigh more than 3000 lb. However, the roadway simulator must be supported by detailed 

vehicle models. Testing autonomous systems on the roadway simulator may be possible. Right 

now, an autopilot drives vehicle on the roadway simulator. For autonomous systems, that 

autopilot needs to be replaced with the robotic control system. The most challenging task will be 

providing artificial sensor information for the robotic sensing system. 

Simulation tools are useful for testing vehicle-based and driver-based systems. As we pointed 

out in our earlier discussions, the automotive industry extensively employs simulation tools such 

as CarSim and PC-Crash to reduce the time and cost of its safety testing programs. These tools 

require detailed models of each vehicle and each anti-rollover system to be tested. These models 

can be built by the tester or supplied by the developer. Linking driving algorithms to simulation 

tools allows researchers to examine driver-based anti-rollover systems. For slalom tests, 
19

simulation tools can help determine maneuver speeds and the spacing and width of the gates. By 

monitoring the driving algorithm during simulated test maneuvers, testers can examine the 

planning process. 

Figure 7. A conceptual slalom course to test autonomous systems. 

Focusing on the subsystem level of testing may demonstrate that an anti-rollover algorithm 

functions properly on improved road surfaces, but it does not fully address performance in the 

complex off-road environment. Actual system-level performance is a combination of vehicle 

properties, driver skill, and the complexity of the environment. While this is certainly equally 

true for manned systems operating on roads, we cannot make the assumption that the surface is 

supposed to be flat and smooth and that there are “representative” maneuvers that can be tested. 

Rollover propensity, a measure of the probability that a system will roll during expected 

operating conditions, provides system-level performance information that testers can use to rank 

performance levels for various systems and that evaluators could use to construct abstract models 

for large-scale force-on-force models. 

There are no formalized testing procedures for measuring rollover propensity in the off-road 

environment. We propose using simulation tools to provide an initial measurement of off-road 

rollover propensity. The first task is to collect rollover statistics from vehicles maneuvering in 

terrain patches from typical off-road environments. Figure 8 illustrates three paths from the left 

side to the right side of a representative test patch. The contours of the test patch are shown in 

brown, and obstacles such as bushes or rocks are shown in green. The path that each vehicle 

drives is constrained by the waypoints, shown as large red circles in the figure. The exact path 

and the speed of travel are controlled by the planning algorithms or, in the case of a tele-operated 

system, by the operator. The top robot path is incomplete; the robot rolls over halfway along its 

intended path. 

We need a simulation tool that has a detailed model of the vehicle undergoing test, a 

representation of the driver algorithm, and rich battlefield environment. Among the 14-DOF 

dynamics models, VDMS is the best choice because it can be easily linked with a distributed test 

environment. Also, it will continue to be improved as Cold Regions Research and Engineering 
20

Laboratory (CRREL) and TARDEC’s tire-soil interaction research matures. The distributed test 

environment is the most efficient way to include the driver algorithms. The simulation tools 

must be able to represent a variety of off-road environments containing tripping features such as 

rocks, ditches, and fallen logs. Variable friction coefficients and deformable surfaces will allow 

vehicles to slide into tripping features and sink into the terrain. A key model in the simulation 

tool is a model of the robotic perception system. Using a distributed simulation tool allows us to 

incorporate robotic perception models developed by other test centers or the system designer. 

Figure 8. Three paths through a test field. 

Simulation tools allow us to study vehicle rollover during combat scenarios in depth. By 

analyzing typical vehicles in battlefield scenarios, we can identify conditions and behaviors that 

lead to rollover. We can derive candidate rollover tests that are analogous to the NHTSA 

maneuvers, from instances of rollover and near rollover in the simulated vignettes that are 

relevant to actual vehicle use in battlefield scenarios. Potential tests can be simulated in a variety 

of battlefield environments to examine sensitivity and repeatability. 

For specific robotic systems, simulation studies provide performance data in environments not 

addressed by physical tests. We can also identify minimum conditions to consistently cause 

rollover. The tester uses this information to set speed profiles, turn, and other factors so that 

physical tests provide usable information with the fewest possible runs. 
21

10. Conclusions 

The anti-rollover case study is the first of a series of case studies used to refine the RIEP process 

for developing tests and methodologies to evaluate robotic behavior algorithms. This particular 

case study enabled us to apply our current four-step RIEP process to a familiar problem for 

which much of the testing methodology had been previously developed for manned systems. In 

the first step, we conducted a thorough analysis of vehicle rollover using the extensive body of 

rollover literature from the transportation safety community. Vehicle rollover is an important 

topic in the transportation industry, so there are several papers discussing rollover mechanisms, 

prevention systems, and rollover testing programs. 

The second step of the RIEP process is to find a simulation environment that is “rich” enough to 

simulate the task and test the proposed algorithm. Again, the automotive industry is an 

important source of information about the use of simulation tools to study the rollover issue. In 

this report, we discussed some of the commercial and Government vehicle dynamics simulation 

packages. However, the automotive industry’s primary interest is in rollovers on or near 

roadways. Modeling rollover in the off-road environment requires additional models for 

complex terrain surfaces and the robotic perception process. VDMS, a Government simulation 

tool, may be more suitable for the off-road environment. It uses a common terrain database 

format so that a variety of terrains can be easily investigated. VDMS also incorporates complex 

tire-soil interactions using on-going research models from TARDEC and CRREL. Unlike the 

commercial products, VDMS is already compatible with other simulation tools used in 

distributed simulation exercises. 

In the third step of RIEP, we link a proposed rollover prevention system to the simulation 

environment. Currently, we do not have a proposed system, but several researchers have used 

simulation environments in their development process by linking a MATLAB rollover 

prevention algorithm to one of the vehicle dynamics simulation packages. It is possible to link 

an actual rollover prevention system to a simulation tool if vehicle attitude information such as 

roll or pitch is provided. This information must be transmitted from the simulated environment 

to the rollover prevention system. 

The last step of RIEP is to design a set of meaningful tests and performance measures to evaluate 

a proposed rollover prevention system. In general, most of the tests designed to evaluate the 

rollover risk of passenger cars apply to unmanned vehicles. Laboratory measures such as the 

SSF and the CSV establish vehicle characteristics. Test track maneuvers can verify that an antirollover system functions in a benign environment. A useful metric to consider is rollover 

propensity which measures the likelihood that a system will roll in a single-vehicle accident. 

Unlike the transportation industry which has years of accident statistics, we do not have a lot of 

data on rollover accidents for robotic systems operating in the off-road environment. We 
22

recommend a series of simulation studies to examine rollover propensity in the off-road 

environment. 

Even this case study has uncovered differences between manned and unmanned system testing. 

Current approaches to rollover testing for manned systems concentrate on the contribution of the 

vehicle. For autonomous unmanned systems, the algorithms “driving” the vehicles must be 

tested as well. Maneuvers that require the robot to plan its path through a region test both the 

driver and the vehicle. Here again, simulated runs can supplement the information we can 

collect from physical test runs. 
23

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26

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 MANEUVER AMMUNITION SYSTEMS 

 ATTN S BARRIERES 

 BLDG 354 

 PICATINNY ARSENAL NJ 07806-5000 

 1 CDR US ARMY TRADOC ANALYSIS CTR 

 ATTN ATRC WBA J GALLOWAY 

 WHITE SANDS MISSILE RANGE NM 88002 
27

NO. OF 

COPIES ORGANIZATION

 1 CDR USAAMC 

 DEPUTY G3 CURRENT OPERATIONS 

 ATTN N BIAMON 

 5001 EISENHOWER AVE 

 ALEXANDRIA VA 22333-0001 

 2 CDR US TACOM-ARDEC 

 ATTN AMSTA AR TD J HEDDERICK 

 B MACHEK 

 PICATINNY ARSENAL NJ 07806-5000 

 1 CDR USAARDEC 

 ATTN AMSRD AAR AE COL P JANKER 

 BLDG 94 

 PICATINNY, NJ 07806-5000 

 1 PEO SOLDIER 

 ATTN C TAMEZ 

 5901 PUTNAM ROAD 

 BLDG 328 

 FT BELVOIR VA 22060-5422 

 ABERDEEN PROVING GROUND

 1 DIRECTOR 

 US ARMY RSCH LABORATORY 

 ATTN AMSRD ARL CI OK (TECH LIB) 

 BLDG 4600 

 2 CDR US ATEC 

 ATTN AMSTE CD B SIMMONS 

 AMSTE CD M R COZBY 

 RYAN BLDG 

 2 DIR US AMSAA 

 ATTN AMSRD AMS SC P MELICK/ 

 J PUSEY 

 APG MD 21005-5067 

 1 CDR US ATC 

 ATTN CSTE AEC COL ROONEY 

 BLDG 400 

 2 DIRECTOR 

 US ARMY RSCH LABORATORY 

 ATTN AMSRD ARL WM J SMITH/ 

 P BAKER 

 BLDG 4600 

 2 DIRECTOR 

 US ARMY RSCH LABORATORY 

 ATTN AMSRD ARL WM B M ZOLTOSKI/ 

 J MORRIS 

 BLDG 4600 

NO. OF 

COPIES ORGANIZATION

 2 DIRECTOR 

 US ARMY RSCH LABORATORY 

 ATTN AMSRD ARL WM BA D LYONS 

 AMSRD ARL WM BD B FORCH 

 BLDG 4600 

 1 DIRECTOR 

 US ARMY RSCH LABORATORY 

 ATTN AMSRD ARL WM BC P PLOSTINS 

 BLDG 390 

 25 DIRECTOR 

 US ARMY RSCH LABORATORY 

 ATTN AMSRD ARL WM BF S WILKERSON/ 

 M FIELDS (20 CYS) 

 H EDGE G HAAS 

 W OBERLE R PEARSON 

 BLDG 390 

 4 DIRECTOR 

 US ARMY RSCH LABORATORY 

 ATTN AMSRD ARL WM MB L BURTON 

 AMSRD ARL WM BA VANLANDINGHAM 

 AMSRD ARL WM MC M MAHER 

 AMSRD ARL WM MD E CHIN 

 AMSRD ARL WM M S MCKNIGHT 

 BLDG 4600 

 3 DIRECTOR 

 US ARMY RSCH LABORATORY 

 ATTN AMSRD ARL WM T B BURNS 

 AMSRD ARL WM TC R COATES 

 AMSRD ARL WM TB R SKAGGS 

 BLDG 309 

 1 DIRECTOR 

 US ARMY RSCH LABORATORY 

 ATTN AMSRD ARL WM TD T BJERKE 

 BLDG 4600 

 1 DIRECTOR 

 US ARMY RSCH LABORATORY 

 ATTN AMSRD ARL WM TE B RINGERS 

 BLDG 1116A