UMTRI-2005-31

PRIORITIZING IMPROVEMENTS TOTRUCK DRIVER VISION

Matthew P. Reed
Daniel Blower
Michael J. Flannagan

University of Michigan
Transportation Research InstituteMay 2006

PRIORITIZING IMPROVEMENTS TO TRUCK DRIVER VISION

Matthew P. Reed
Daniel Blower
Michael J. Flannagan

The University of Michigan
Transportation Research Institute
Ann Arbor, Michigan 48109-2150
U.S.A.

May 2006

iTechnical Report Documentation Page
1. Report No.

2. Government Accession No.

3. Recipient's Catalog No.

UMTRI-2005-31
4. Title and Subtitle

5. Report Date

Prioritizing Improvements to Truck Driver Vision

May 2006
6. Performing Organization Code

7. Author(s)8. Performing Organization Report No.

Reed, Matthew P., Blower, D.F., and Flannagan, M.J.
9. Performing Organization Name and Address

10. Work Unit No. (TRAIS)

University of Michigan Transportation Research Institute2901 Baxter Rd.
Ann Arbor MI 48109

11. Contract or Grant No.

12. Sponsoring Agency Name and Address

13. Type of Report and Period Covered

International Truck and Engine Corporation
14. Sponsoring Agency Code
15. Supplementary Notes

16. Abstract

This report presents the results of a three-part study of truck driver exterior vision and its safety consequences. In part one,crash data are analyzed to document vision-related truck crash issues. About 20% of truck-initiated crashes occur inconfigurations in which limitations to truck driver vision may have been an important factor contributing to the crash.
Right-going lane changes and turns account for more than half of these crashes. On average, right-going truck-initiatedcrashes are about 4.5 times more likely than left-going crashes. Non-motorists killed in startup and right-turn crashes were
nearly all adults and tend to be older the pedestrians struck in other crash modes, suggesting that near-field truck vision
analyses should focus on adults rather than children. Over half of pedestrians involved in start-up crashes are over age 65.
An experimental study showed that driver performance in detecting lane-change conflicts was directionally consistent withthe findings from the crash data. Drivers took longer to detect conflicts on the right side of the vehicle than on the left. The
longest reaction times were observed when the target vehicle was directly to the right of the cab, suggesting that detecting a
conflict in this area is most difficult for drivers. Drivers also made more errors on the right side of the vehicle, including
several failures to detect a vehicle directly to the right of the cab. Based on these findings, a prioritized set of vision zones
was developed. The highest priority for improvements to driver vision is the area directly to the right of the truck cab. This
area represents the most likely position of a crash partner at the truck driver’s decision point in right lane-change crashes andis also the pre-crash position of many non-motorists involved in right turn and start up crashes. This report presents a new
approach to evaluating exterior vision from truck cabs. The method differs from previous approaches, e.g., SAE J1750, by
providing an aggregate score that is related to a specific crash-safety issue. The method is based on the visibility of standing
adult pedestrians, and hence addresses the specific problem of pedestrian involvement in start up and right turn crashes. The
experimental paradigm presented in Section 3 also represents a promising approach to evaluating the quality of exterior
vision provided by alternative vision systems. The time drivers require to determine if a conflict exists provides a sensitive
measure of the difficulty of the task. The parallels between the findings of the experimental study and the crash data
analysis support the validity of the experimental approach. This method could be applied to evaluate alternative mirror
systems, camera-based systems, and other technologies that might be developed to address the priorities established in thisreport.
17. Key Word

18. Distribution Statement

Truck drivers, vision, mirrors, safety
19. Security Classif. (of this report)

20. Security Classif. (of this page)

Form DOT F 1700.7 (8-72)

Reproduction of completed page authorized

21. No. of
Pages

85

22. Price

ii

ACKNOWLEDGMENTS
This research was funded by International Truck and Engine Corporation. The authors
thank Brandon Schoettle, Omer Tsimhoni, and Sheila Ebert for their assistance.

iii

iv

CONTENTS
ACKNOWLEDGMENTS ................................................................................................. iii
1.0

INTRODUCTION .................................................................................................. 1

2.0

ANALYSIS OF CRASH DATA ............................................................................ 3

3.0

EMPIRICAL TEST OF MIRROR SYSTEMS..................................................... 45

4.0

PRIORITIZATION OF ZONES FOR DRIVER VISION IMPROVEMENT ..... 63

5.0

A METHOD FOR EVALUATING NEAR-CAB VISIBILITY .......................... 67

6.0

SUMMARY AND CONCLUSIONS ................................................................... 81

7.0

REFERENCES ..................................................................................................... 85

v

1.0

INTRODUCTION

Although a view of the environment surrounding a vehicle is critical for driving,
little effort has been directed at quantifying the quantity and quality of the field of viewthat is required. A small aperture affording a view of the forward scene a few degrees
wide might be considered the minimum possible exterior view, but most vehicles provide
drivers with a much wider field of view, encompassing nearly 360 degrees around thevehicle. The total field of view is achieved by indirect vision using mirrors as well as bydirect vision. Driver head motions allow a range of vantage points and further expand
the fields of view.
Drivers of heavy trucks (10,000 lb and larger) rely to a greater extent than driversof smaller vehicles on indirect vision. Most heavy trucks provide no view through therear of the vehicle, so vision to the rear is restricted to the view provided by exterior
mirrors. Often the geometry of the truck obstructs the view of the area adjacent to thetruck behind the cab. The higher driving position typical of heavy trucks increases thesize of the zone around the vehicle within which vision is obstructed by the vehicle.
Regulatory requirements for truck driver vision are minimal. The only standard
that bears directly on driver fields of view is Federal Motor Vehicle Safety Standard(FMVSS) 111, which regulates mirror systems. Trucks over 10,000 lb are required tohave planar mirrors with an area of at least 323 cm2 on each side of the cab. Direct vision
is unregulated.
Any increase in the visibility of the exterior environment could be viewed as an
improvement in driver vision, and any decrease could be viewed as negative. But all
increases or reductions are unlikely to have the same effects on safety.

For example,

raising the hood by 10 cm might have a larger effect on safety than lowering the top of
the windshield by 10 cm. But whether either change would have a significant impact onsafety would depend on the baseline condition. Raising the hood would not matter if thepotential crash partners remained visible, but lowering the top of the windshield could
reduce the visibility of traffic control signals if the starting point were sufficiently low.
Similarly, the potential effects of changes in mirror configurations are difficult todetermine. Nearly all heavy trucks currently have mirrors that exceed the FMVSS 111

1

requirements, primarily by the addition of convex mirrors near the mandated planar
mirrors.

Recently the National Highway Traffic Safety Administration (NHTSA) has

requested information on whether fender-mounted convex mirrors should be required(NHTSA XX) and has proposed new rules that would mandate indirect vision systems toview the area behind certain straight trucks (NHTSA XX). The potential benefits of therear-view requirement are more apparent because the area immediately behind the truckis currently not viewable by the driver. The potential benefits of fender-mounted convex
mirrors, which are already present on the right front of about two-thirds of tractor-trailers,
are more difficult to determine.
This report presents the results of a study designed to address the contributions of
the quantity and quality of the field of view to truck safety. The study was conducted inthree complementary tasks.

First, an extensive analysis of crash data from several

sources examined the role of driver vision in truck-initiated crashes (Section 2 of thereport). Second, an experimental study of truck drivers’ conflict detection in a lanechange scenario was conducted (Section 3). Third, a quantitative method for evaluating
truck cab designs with respect to near-cab visibility was developed (Section 5). As partof the work, a prioritized set of vision zones around the truck were established to guide
improvements to direct and indirect vision (Section 4).

2

2.0

2.1

ANALYSIS OF CRASH DATA

Data and Analysis Methods
Two sources of crash data were used to characterize the crash environment of

medium and heavy trucks: nationally representative computerized crash files andsupplementary data collected specifically for this project for selected crash types.
Nationally Representative Crash Data — The computerized crash files used were
the Trucks Involved in Fatal Accidents (TIFA) file, which is compiled by UMTRI, andthe National Automotive Sampling System General Estimates System file (NASS GES,
referred to simply as GES hereafter), compiled by the National Highway Traffic Safety
Administration. Data files for seven years, 1994-2000, were aggregated for the analysis.
Multiple years of data were used to improve the resolution of the analysis and to produce
statistically reliable results.
The TIFA file is a survey of all medium and heavy trucks1 involved in a fatal
traffic accident in the United States. A fatal accident includes any traffic accident inwhich one or more persons are fatally injured; the fatality may occur to a truck occupant,an occupant of another vehicle, or a nonmotorist. For 1999 and 2000, TIFA is a census
file, meaning there is a record for each truck involved in a fatal crash. For 1994-1998,
trucks were sampled to reduce the number of cases processed. For those years, about 60
percent of trucks involved in a fatal crash were sampled. Weights were determined thatallow correct national estimates of population totals. The TIFA file provides the mostaccurate and detailed data on trucks involved in a fatal crash available.
The GES crash file is a nationally representative sample of police-reported
crashes, covering all vehicles involved in a traffic accident, not just trucks. GES data arecoded entirely from police-reports. GES is the product of a complex sample survey withclustering, stratification, and weighting that allows calculation of national estimates.
The GES data are used to cover nonfatal truck accidents. The GES file is known
to underestimate fatal crashes by approximately 20-30 percent. Since the TIFA data are

1

The term trucks will be used hereafter to refer to all class 3 and above vehicles.

3

known to be a substantially more accurate representation of fatal truck crash
involvement, for this analysis TIFA data are used to cover fatal crashes and GES data areused to represent nonfatal crashes. To facilitate analysis, an analytical file was built thatcombines TIFA and GES data, with TIFA data covering fatal involvements and GES
nonfatal. Care was taken to ensure that the variables combined were compatible, or
recoded to be compatible. In particular, the TIFA file includes an accident type variable
that codes the relative position and movement of the vehicles prior to the crash. The
TIFA accident type variable is modeled on the accident type variable in the GES file, sothey are fully compatible. This variable is of particular use in the present effort since it
provides critical information on how the crash occurred and, accordingly, the direction to
the conflict prior to collision.
Error!
TIFA

Fatal

InjuryProperty damage only

GES

Figure 1. Composition of analysis file: TIFA and GES, 1994-2001.

Figure 1 shows schematically the composition of the analysis data file: the nearcensus TIFA file covers fatal crash involvements while the nationally-representative GES
sample file covers all other crash severities. The shapes are not to scale; fatal crashes
account for only 1.3 percent of truck crash involvements. However, given their severity
and the additional detail the TIFA file supplies, it is important to use the most accurate
data available.

4

Supplemental Data Collection for Selected Crash Types — Supplementary data
were collected for this project to provide greater detail on the events and positions for
specific crash types. The source of these data was a review of police reports for a set ofcrashes. Such police reports were available because as part of the TIFA data collection
protocol, UMTRI acquires from the states the original crash reports on all fatal crashes
involving trucks. These police reports are currently available for 1999 and later (earlyyears have been discarded because of storage limitations), and provide a resource toexamine in some detail specific crash types.
Three crash types were identified for supplementary data collection. They are: 1)
start up crashes in which the truck starts from a stopped positions and strikes anonmotorist; 2) right turn crashes in which the truck collides with a nonmotorist while
making a right turn; and, 3) lane change/merge right2 crashes in which the truck collides
with another vehicle while merging or changing lanes to the right. In the start up crash
type, the truck starts from a stopped position and collides with a nonmotorist3, usually but
not always with the front of the truck. The right turn crashes considered here are those inwhich the truck is turning right, either at an intersection or into a driveway or alleyway,
and collides with a nonmotorist. In lane change/merge right crashes, the truck changes
lanes to the right or merges to the right and collides with another vehicle. Note that foreach crash type, the truck driver initiates the maneuver that leads to the crash.
The supplemental data collection provides more detailed information about theposition and movement of the other party—vehicle, pedestrian, or bicyclist—with respect
to the truck to determine critical vision zones around the truck. The two primary pieces ofdata collected were the position of the other party three to five seconds prior to thecollision and the position at the decision point for the truck, that is, at the point where thetruck driver initiated the maneuver. In addition, the general crash type was coded, alongwith the line of travel of the other party, the location of contact, and the point of contact
on the truck. Information on the use of mirrors prior to the crash was also collected, but
2

We will italicize crash types in the text to make the references more clear. Many of the crash types havelong names because they are very specific. It is hoped that italicizing the names will make it more clear
when we are referring to a specific crash type.
3
Nonmotorist is a somewhat awkward term that encompasses pedestrians, bicyclists, and any other person
not occupying a motorized vehicle, such as roller-bladers, children in strollers, skateboarders, and so on.

5the use of mirror was mentioned in only ten of 160 cases reviewed, and in none did thetruck driver report seeing the other party in the mirror prior to the collision.
Figure 2 shows the interface used in collecting the data. The database wasdeveloped in Microsoft Access, which provides convenient and flexible tools for enteringand reviewing data.

Figure 2. Data collection interface for supplementary crash data.

A total of 160 cases were identified for review. One case was discarded because it
had been miscoded: vehicle maneuver was coded as turning right, but the narrative on thepolice report indicated that the truck moved to the right as part of an unsuccessful evasive
maneuver. Another ten cases were considered not applicable to the intended crash type;for example, a case where the driver stops his truck to work on the brakes and it rolls
over him does not belong in the start up crash type, because there is nothing useful to belearned about the role of truck driver vision in the crash. After deleting the miscoded andnot applicable cases, 148 cases yielded usable information.

6

Table 1
Cases Reviewed for Supplemental Data Collection,
TIFA 1999-2000
Crash Type

2.2

N

LCM right

40

Start up (struck nonmotorist)

38

Turn right (struck nonmotorist)

70

Total

148

Results
In this section, we discuss the results of the crash data analysis, both the mass

crash files—TIFA and GES—as well as the supplemental data collected for specific crashtypes. Figure 3 shows the flow of the crash data analysis. The crash data analysis has anumber of objectives. The initial goal is to classify truck crash involvements into crashtypes that are meaningful in terms of driver vision, that is, the direction and proximity to
the conflict. Analysis of the mass crash data in the analytic file built from TIFA and GES
is used to accomplish this step. Neither direction nor distance to the conflict is directly
coded in the crash data, but the crash configuration can provide very roughapproximations for both. Accordingly, crashes are aggregated into categories from which
inferences where the truck driver should have been looking can be drawn. Within thosecategories, crash configuration in which the truck driver would have to rely on his
mirrors are identified as “mirror-relevant.” In these crashes, the conflict with the otherparty to the collision occurred in areas around the truck where the driver would have torely on the mirrors to view. In addition, the effect of factors such as weather and light
condition are considered. Certain crash types of interest were selected to collectadditional data on the location of the other party around the truck. These cases wereselected from the fatal (TIFA) data because police reports on them are readily available.
The supplementary data are analyzed to determine critical points around the vehicle.

7

Fatal(TIFA)

Mass crash analysis

Nonfatal(GES)
Start up/nonmotorist

Case review of selectedcrash types

Right turn/nonmotorist

Lane change/merge
right

Figure 3. Analysis of mass crash data and selected crash types.

Analysis of TIFA and GES — The first step in developing the crash configuration
classification tabulated in Table 2 was to identify crashes in which the truck driverinitiated the action and in which the driver would have to rely on mirrors to view the area
in which the conflict occurred. In other words, these are crashes for which the truck
driver’s use of mirrors is of primary relevance to avoiding the crash. In each case, thetruck driver is performing the maneuver. For example, in lane change/merge rightcrashes, the truck driver was changing lanes or merging with traffic to the right of thetruck. Cases in which the other vehicle changed lanes into the truck are excluded, because
the truck driver’s mirrors are not primarily at issue. Clearly there can be cases where thetruck driver may view the impending conflict and maneuver to avoid the crash; thosecases are not included here but rather are captured in the sideswipe, same direction, otherencroaching category.
Crash configurations labeled “not truck-mirror-relevant” encompass crash types
in which the driver would not normally have to rely on the mirrors to view the area ofimpending conflict. The categories used are from a typology developed for crashavoidance applications, and provide general information about the nature of the conflictthat led to the collision. For example, rear-end, truck striking crashes includes all crashes
8

in which the truck struck another vehicle that was stopped or going slower in front of thetruck. In these crashes, clearly the conflict is in front of the truck and the response of thetruck is the primary crash avoidance mechanism, either by slowing, steering around theconflict, or both. In sideswipe, same direction, other vehicle encroaching crashes, both
vehicles are proceeding in the same direction and the other vehicle encroaches into thetruck’s lane. In these crashes, the conflict is to the side of the truck, but the other vehicle
initiated the crash by moving into the truck’s lane. The other crash types can be subjected
to a similar analysis.
In Table 2 the crashes are organized to put similar crash types together. The first
three crash types are all single vehicle, so they involve primarily the action of the truck
driver. The others are organized as logical pairs, to group complementary crash types
together, depending on the role of the truck. For example, rear-end, truck striking andrear-end, truck struck, or head-on, truck encroaching and head-on, other encroaching.

9Truck mirrorrelevant

Not truck mirror-relevant

Table 2
Classification of Truck Involvements in Crashes in
Relation to Truck Driver Vision Zones
TIFA/GES 1994-2000
Crash typeN%

No collision

109,949

4.1

Ran off road

219,762

8.2

Hit object* in road

173,708

6.5

Rear-end, truck striking

296,154

11.1

Rear-end, truck struck

198,580

7.4

Sideswipe, same direction, truck encroaching†

16,604

0.6

Sideswipe, same direction, other encroaching

193,827

7.3

Head-on, truck encroaching

3,489

0.1

Head-on other encroaching

12,652

0.5

Sideswipe, opposite direction, truck encroaching

37,522

1.4

Sideswipe, opposite direction, other encroaching

55,348

2.1

Truck turns across other’s path

143,516

5.4

Other vehicle turns across truck’s path

118,091

4.4

Both going straight, truck into other’s side

53,0882.0

Both going straight, other vehicle into truck’s side

51,466

1.9

Truck backs into other vehicle†

695

0.0

Other vehicle backs into truck

18,447

0.7

Untripped rollover

17,446

0.7

Truck, lane change/merge right into other vehicle

180,797

6.8

Truck, lane change/merge left into other vehicle

41,067

1.5

Truck turns right, other vehicle in blind spot

94,771

3.5

Truck turns left, other vehicle in blind spot

22,515

0.8

215

0.0

Truck backs into other vehicle

186,350

7.0

Other crash type411,352

15.4

Unknown crash type14,3140.5

2,671,729

100.0

Truck starts up from stop

Total

* “Objects” are typically pedestrians, bicyclists, or other nonmotorists.
† Cases are anomalous on impact location, collision, or prior move.

Overall Table 2 shows that mirror-relevant crashes account for 19.7 percent of all
truck crash involvements. That is, crashes occurring in areas where the truck driverwould have to rely on mirrors to determine if it is safe to maneuver account for almost
one out of five crashes in which trucks are involved. In all of these crashes, the truck

10

initiates the crash through a maneuver. In most of these crashes, the conflict is relatively
close to the truck, since the truck is either in a low speed maneuver, as in the start up,
backing, or turning crashes, or the relative speeds of the vehicles is low, as in the lanechange/merge crash types. Among the mirror-relevant crashes, note the dominance ofcrashes where the truck is moving to the right, particularly in lane change/merge (LCM).
LCM right crashes occur 4.4 times more frequently than LCM left crashes. Similarly,
right turn crashes are significantly more frequent than left turn crashes. There are 4.2
right turn (mirror-relevant) crashes for every mirror-relevant crash while turning left.
Clearly drivers are having much more trouble in maneuvers to the right than to the left.
Start-up crashes were very hard to identify using available variables. The number islikely to be an underestimate.
While it may be useful to compare complementary crash types, Table 3 shows thecrash types sorted from most common to least, within the mirror-relevant and other crashtypes. Among the crash types considered not mirror-relevant, rear-end, truck striking was
the most common, with 11.1 percent of all truck crash involvements. Ran off road, rearend, truck struck, and sideswipe, same direction, other encroaching, are all of similar
magnitude. The most common mirror-relevant crash type is truck backs into other vehicle
with 7.0 percent of all truck involvements. Lane change/merge right accounts for about
the same percentage, with 6.8, and is in the top quarter of all crashes. Note that the othercategory actually is the most frequent. The other category includes a wide variety ofcrashes that do not fit into any of the named categories.

11

Table 3
Ranking of Crash Types in Relation to Truck Driver Vision Zones
TIFA/GES 1994-2000

Truck mirrorrelevant

Not truck mirror-relevant

Crash TypeN%

Rear-end, truck striking

296,154

11.1

Ran off road

219,762

8.2

Rear-end, truck struck

198,580

7.4

Sideswipe, same direction, other encroaching

193,827

7.3

Hit object* in road

173,708

6.5

Truck turns across other’s path

143,516

5.4

Other vehicle turns across truck’s path

118,091

4.4

No collision

109,949

4.1

Sideswipe, opposite direction, other encroaching

55,3482.1

Both going straight, truck into other’s side

53,0882.0

Both going straight, other vehicle into truck’s side

51,466

1.9

Sideswipe, opposite direction, truck encroaching

37,522

1.4

Other vehicle backs into truck

18,447

0.7

Untripped rollover

17,446

0.7

Sideswipe, same direction, truck encroaching†

16,604

0.6

Head-on other encroaching

12,6520.5

Head-on, truck encroaching

3,489

0.1

Truck backs into other vehicle†

695

0.0

Truck backs into other vehicle

186,350

7.0

Truck, lane change/merge right into other vehicle

180,798

6.8

Truck turns right, other vehicle in blind spot

94,771

3.5

Truck, lane change/merge left into other vehicle

41,067

1.5

Truck turns left, other vehicle in blind spot

22,515

0.8

215

0.0

Other crash type411,352

15.4

Unknown crash type14,3140.5

2,671,729

100.0

Truck starts up from stop

Total

* “Objects” are typically pedestrians, bicyclists, or other nonmotorists.
† Cases are anomalous on impact location, collision, or prior move.

Table 4 shows the definitions for the mirror-relevant crash types. Whiledeveloping this set of crash types, the goal was to identify crashes that occurred in anarea where the truck driver would have to use his mirror to successfully complete a
maneuver. Each bullet point essentially corresponds to a set of code values for aparticular variable. Since crashes as they actually occur on the roads do not readily sort12

themselves into neat categories, and since crash investigators can make mistakes,
consistency was required across multiple variables to increase the probability that crashes
assigned to a particular category were roughly similar. For example, in the case of lanechange/merge right, certain codes in the accident type variable define the crash type, but
in some cases the first contact was on the left or rear of the vehicle, and where movement
prior to the crash was a left turn. Such cases might be genuine inconsistencies or thecrash itself may have been anomalous. But whatever the explanation, the crashes were
excluded to avoid contaminating the category. Note that in both the left and right turncategories, the vehicle collided with was approaching from the rear of the truck.
Table 5 shows the categories of driver vision zones that can be reasonably
inferred from the crash types and other information available in the crash files, andprovides the reasoning supporting the classification. The goal is to identify the direction
from the driver to the impending conflict. The classification was developed using thecrash types developed above, along with the more detailed accident type information andinformation about the location of the first impact on the vehicle. The table describes thelocations identified by each category.

13

Table 4
Identification of Mirror-Relevant or Indirect Driver Vision Crashes
Crash TypeDefinition

Lane change/merge right

Lane change/merge left

Right turn•Same trafficway, same direction accident
type move to right•First impact on right side•

Prior move is lane change, merge, or goingstraight•

Same trafficway, same direction accident
type move to left•

First impact on left side•

Prior move is lane change or merge•

Change trafficway, accident type is turn toright, with other vehicle approaching frombehind•First impact on right•First harmful event is collision withpedestrian or pedalcyclist•

First impact on right side•

Prior move is right turn•

Change trafficway, accident type is turn toleft, with other vehicle approaching frombehind•Prior move is left turn•Impact point on left•

Accident type is backing•

First harmful event is collision•

Prior move is stopped or starting up in
traffic lane•First harmful event is collision withpedestrian or pedalcyclist•

At intersection or intersection-relatedorLeft turn

BackingStart up14

Table 5
Driver Vision Zone Definitions
Direction toConflictDefinition

Forward

Straight in front of vehicle. For example, rear-ends where the truck is the striking
vehicle, or start-up collisions with pedestrians.

Rightforward

Conflict is in right adjacent lane, for example, striking a parked vehicle in the parking
lane. Also includes same direction sideswipes where the contact point was on the front
right corner.

Right

These are mostly crashes that occur when the vehicles are on intersecting roadways.
As an example, for the vehicle coded 87 in the diagram here, the conflict is comingfrom the right. Actually, it is to the right only at the point of impact. Prior to that (i.e.,
when the view might have been relevant to the driver), the direction would be more
like 60º. The right and left categories are used only for cases where the vehicles pre-crash were on intersecting roads.
Right rear

Direction to the other vehicle is on the right side, toward the rear of the vehicle. Mostof these cases are from the mirror-relevant group.

Right,
generalConflict was to the right of the vehicle, but it cannot be determined if it was forward,
rear, or in the middle. Many of these are same direction sideswipes. The contact
information is not detailed enough to know whether the other vehicle was to the rightfront of the truck, roughly in the middle, or toward the rear.

Rear

Directly to the rear of the truck. Includes mostly rear-end struck and truck backed into
other vehicle.

Left, rear

Consistent with right rear

Left

Consistent with right

Left forward

Consistent with right forward: these are mostly head-ons and opposite direction
sideswipes. It is assumed they are all cases of conflicts with vehicles initially in theadjacent lane. Some fraction are actually cases where the vehicle is already in thetruck’s lane, such as cases where the other vehicle used an exit ramp as an entrance
ramp. There is no way to identify such cases in the data, but it is likely the fraction isvery small.

Left, general

Consistent with right, general.

No conflict

These are cases, as far as can be determined, where driver vision to whatever it is he ishitting, is not of primary relevance to the crash. Includes untripped rollovers, forexample, and cases where the driver drove or skidded off the road and was notmaneuvering to avoid something in the road. Category also includes noncollision
events, such as fires, explosions, falling from vehicle, and so on.

Unknown

Insufficient information to deduce direction to the conflict from the configuration of
the crash and impact point the direction to the conflict. Many are coded unknown orother on accident configuration. Point of impact on the truck does not imply the vectorof the collision, so that is not enough.

15

Table 6 shows the results of aggregating truck crash involvements into thecategories described in the table above. Note that directly forward and directly to the rearare both about the same magnitude, and directly right and directly left are both about thesame magnitude. Table 6 includes all crashes, not just those the truck driver initiated bymaneuvering.
Table 6Classification of Crashes by Direction to Conflict
TIFA/GES 1994-2000
Direction toConflictN%

Forward419,569

15.7

Right forward

50,887

1.9

Right132,134

4.9

Right rear

94,8393.5

Right, general

315,557

11.8

Rear

395,725

14.8

Left, rear

23,7390.9

Left146,362

5.5

Left forward

206,649

7.7

Left, general

162,438

6.1

No conflict

319,611

12.0

Unknown

404,22015.1

2,671,729

100.0

Total

The distribution may be more readily seen in Figure 4. In this figure, the noconflict and unknown categories are dropped, and the percentages recalculated for allcases with known conflicts. The percentages at the corners on the front and rear of thetruck identify right forward and right rear, left forward and left rear. The percentages forcrash involvements in which the direction to the conflict was more or less directly to theleft or right, as at an intersection, are labeled as intersection crashes. The “general”
categories show the percentages of crash involvements in which the conflict was to theleft or right, but for which there was insufficient information to classify the direction
more precisely.

16

Figure 4. Distribution of direction to conflict, TIFA/GES 1994-2000.

Again, the proportion of conflicts to the front and rear are nearly equal, as areconflicts directly to the left and right. Left forward is significantly higher than rightforward, 10.6 percent to 2.6 percent. Conflicts to the left forward in this classification
result in head-on collisions or sideswipes in which the vehicles are moving in opposite
directions. These occur primarily on roads with two-way traffic.
However, the figure also depicts the limits of attempting to determine withconfidence the direction to the conflict using the computerized record alone. For manycrash types it was not possible to classify the direction to the conflict in other than
general terms, i.e., left general or right general.
Table 7 shows distribution of vision zones by crash severity. Crashes are divided
between more serious (fatal, A-injury, or B-injury) and the less serious (C-injury or no
injury) crash involvements, and the distribution of the driver vision zone is shown for
each. Since most crash involvements include only minor or no injury, fatal, A-injury, and17

B-injury involvements are combined to accumulate sufficient sample size for meaningfulcomparisons. Crashes in which the vehicles were going in opposite directions are much
more serious than those in which they were going in the same direction, because closing
speeds prior to impact are typically much lower. Note that the left forward zone accounts
for 24.1 percent of the more serious involvements, but only 8.6 percent of the lessserious. Head-on collisions and sideswipes in which the vehicles are going in opposite
directions account for many of the involvements in the left forward zone. The right,
general zone tends to include less severe crash involvements: only 8.2 percent of serious
but 16.9 percent of less serious involvements. Many involvements assigned to the right,
general zone are same direction sideswipes, where closing speeds are low. In contrast,
crashes in which the conflict was to the left or right—these crashes typically occur atintersections where the vehicles are crossing paths—are overrepresented among serious
crash involvements. Overall, the left and right zones account for 21.7% of more serious
truck involvements and only 13.5 percent of the less serious.
Table 7
Distribution of Crash Severity by Direction to Conflict
TIFA/GES 1994-2000

Direction toConflict

Fatal, A-, and BInjury

C-injury and No Injury

N

%

N

%

Forward45,69518.8

357,646

22.1

Right forward

2,654

1.1

44,760

2.8

Right

26,56910.9101,060

6.2

Right rear

5,311

2.2

87,458

5.4

Right, general

20,036

8.2

272,930

16.9

Rear

43,33617.8

339,795

21.0

Left forward

58,47724.1

139,484

8.6

Left26,247

10.8

117,196

7.2

Left, rear

2,242

0.9

21,366

1.3

Left, general

12,3995.1

136,972

8.5

Total

242,966

100.0

1,618,667

100.0

Examination of specific crash types can shed more light on the role of drivervision in truck crashes and certain factors that affect it. Table 2 above showed significant

18

disparity in crash involvements when the truck was maneuvering to the left incomparison with maneuvers to the right. Specifically, in maneuvers where the truck
driver must use the mirrors to determine if the maneuver is safe, LCM crashes to the rightare 4.4 times more frequent than LCM crashes to the left. Crashes in which the truck was
executing a right turn at an intersection and collided with something to the truck’s rightare 4.2 times more frequent than analogous left turn crash involvements. In both crashtypes, (LCM right/left and turning left/right), the driver must use mirrors to determine if
the maneuver is safe. Moreover, the driver’s view to the right generally is more difficult,because the passenger side mirror is farther away than the driver side mirror, so the image
will be smaller and more difficult to interpret.
Both weather and light condition may affect the driver’s ability to use the mirrors
effectively. In this section, the effect of weather and light condition on two crash types
will be considered.
In rainy or wet weather, splash and spray can coat the mirror and windows,
obscuring the driver’s view. In dark conditions, the driver will be able to see less in the
mirror than in daylight. On the other hand, headlamps of other vehicles in the mirror will
be particularly salient and the driver may be able to see light reflected on the road fromthe headlamps of vehicles traveling alongside the truck. Thus, weather and light condition
both affect driver vision and may have an effect on the types of crashes that occur bymaking certain types more likely.
The presumed effect of weather with respect to mirrors is that precipitation andspray may obscure the image in the mirror, making it more difficult for the driver tojudge if the way is clear. Thus, mirror-relevant crashes may be more likely in adverse
weather. The table shows that crashes in which truck drivers rely on mirrors are actuallyrelatively less frequent in adverse weather (precipitation or fog) than in good weather.
“No adverse conditions” accounted for 89.4 percent of mirror-relevant involvements, but
only 82.0 percent of non-mirror-relevant crash involvements.

19

Table 8
Mirror-Relevant Involvements and Weather Condition
TIFA/GES 1994-2000
Mirror-relevant

Non-mirror relevant

Weather

N

%

N

%

No adverse condition

469,765

89.4

1,747,132

82.0

Precipitation

48,672

9.3

328,792

15.4

Fog

978

0.2

13,339

0.6

Other

1,220

0.2

16,904

0.8

Unknown

5,088

1.0

25,526

1.2

525,723100.0

2,131,693

100.0

Total

The data tabulated in Table 8 consider all types of mirror-relevant crashes
together and shows that they are not more likely in adverse conditions than other crashes.
However, the presumed effect of precipitation and spray on mirrors may operate
differently in different types of crashes, and may be detected by considering pairs ofcomplementary crash types. In both lane change and turns, the driver’s reliance on the
mirrors depends on the direction of the maneuver. Similarly, LCM maneuvers to theright are more difficult than LCM maneuvers to the left.
Table 9 shows how adverse weather in the form of rain, snow, or sleet changes
the relationship between maneuvers to the left and right. The numbers in the cells are theratio of right to left crash involvements for the two crash types in two conditions. Where
there are no adverse weather conditions, LCM right involvements occur 4.40 times more
frequently than LCM left. In rain, snow, or sleet conditions, where splash or spray might
obscure the mirror, one might expect an even higher ratio of right to left. In fact, the ratio
is somewhat higher, 4.86 to 4.40, which is an increase of about 10 percent. However, forthe turning crash type, the ratio of right to left is actually lower in rain, snow, or sleet, by
about 10 percent. Thus for one crash type, adverse weather seems to raise the ratio
slightly and for the other it seems to decrease it, equally slightly.

20

Table 9
Involvement Ratios of LCM and Turning Crashes by Weather Condition
TIFA/GES 1994-2000
No
adverseconditions

Rain,snow, orsleet

Ratio right to left lanechange/ merge

4.40

4.86

Ratio right to left turns

4.33

3.88

`

The interpretation of this result is not clear, but note that the effect is small.
Adverse weather accounts for only about a 10 percent change in the ratio, while the ratio
itself, which is the effect of using mirror to maneuver left and right, shows increases ofseveral hundred percent. The underlying data—from TIFA and GES—is a combinationof seven years from a near-census file and a complex, stratified sample, so calculating
confidence intervals for this result would be very difficult and indeed cost-prohibitive.
However, the effect of weather appears to be relatively small, and may not exist. Weather
is taken as a surrogate for splash and spray, and it is assumed that spray on the mirrors
would reduce the driver’s ability to use them. It seems likely, in light of the result, thateither adverse weather does not obscure the mirrors or that the spray is not sufficient tohinder their use.
Light condition may also affect mirror-relevant crashes. However, Table 10shows that the overall distribution of mirror-relevant crashes by light condition differs
only somewhat from the same distribution for other truck crash involvements. Nonmirror-relevant involvements have a somewhat lower proportion of the daylight
condition, 78.7 percent compared with 84.3 for the mirror-relevant involvements. Thisdifference is consistent with an elevated proportion of non-mirror-relevant crashes in thedark condition, 9.7 percent compared with 4.7 for mirror-relevant crashes. If anything,crashes in which the driver had direct vision of the conflict are more likely in dark
conditions than are mirror-relevant crashes.

21

Table 10
Light Condition and Mirror-Relevant Crashes
TIFA/GES 1994-2000
Light
condition

Mirror-relevant

Non-mirror-relevant

Unknown

Total

N

%

N

%

N

%

N

%

Daylight

443,058

84.3

1,677,070

78.7

10,34672.3

2,130,474

79.7

Dark/lighted

39,708

7.6

162,421

7.6

1,111

7.8

203,239

7.6

Dark

24,824

4.7

207,596

9.7

1,324

9.2

233,745

8.7

Dawn/Dusk

13,162

2.5

63,686

3.0

672

4.7

77,5192.9

Unknown

4,964

0.9

20,926

1.0

862

6.0

26,752

1.0

525,715100.0

2,131,699

100.0

14,315100.0

2,671,730

100.0

Total

However, Table 11 shows that the distribution of crash types varies by light
condition, particularly when the two pairs of complementary crash configurations are
considered. In the daylight condition, 6.6 percent of involvements are LCM right,
compared with 1.6 percent LCM left. The relative percentages are similar in dark
conditions, but in dark/lighted conditions 9.6 percent are LCM right and only 1.3 percent
are LCM left. LCM right crashes are even more over-involved in dark/lighted conditions.
A similar pattern may be observed for right turn and left turn mirror-relevant crashes.

22

Table 11
Mirror-Relevant Crash Types by Light Condition
TIFA/GES 1994-2000

Daylight

Dark/
lighted

Dark

Dawn/
Dusk

Unk.

Total

LCM, right

140,399

19,564

14,613

5,152

1,069

180,797

LCM, left

34,481

2,550

2,588

545

904

41,067Right turn

82,893

6,590

2,252

2,795

241

94,771

Left turn

20,551

741

452

447

323

22,515

Start up

175

36

4

0

0

215

Backing

164,559

10,227

4,916

4,222

2,427

186,351

1,677,070

162,421

207,596

63,68620,9262,131,699

10,346

1,111

1,324

672

862

14,3152,130,474

203,239

233,745

77,51926,752

2,671,730

Crash typeNon-mirror-relevant
Unknown
Total

Column percentages
LCM, right

6.6

9.6

6.3

6.6

4.0

6.8

LCM, left

1.6

1.3

1.1

0.7

3.4

1.5

Right turn

3.9

3.2

1.0

3.6

0.9

3.5

Left turn

1.0

0.4

0.2

0.6

1.2

0.8

Start up

0.0

0.0

0.0

0.0

0.0

0.0

Backing

7.7

5.0

2.1

5.4

9.1

7.0

Non-mirror-relevant

78.7

79.9

88.8

82.2

78.2

79.8

Unknown

0.5

0.5

0.6

0.9

3.2

0.5

100.0

100.0

100.0

100.0

100.0

100.0

Total

Table 12 displays how light condition affects the ratio of right to left for thesecrash types. It appears that lower visibility significantly increases the difficulty ofmaneuvering safely to the right, as compared with the left. There are also interesting
differences between the categories of light condition. (The dawn/dusk category includes
only about three percent of involvements.) The dark condition increases the ratio of rightto left LCM involvements by about 37 percent, compared with the daylight condition (4.1
to 5.6). This increase is expected since it is likely that darkness in general degrades drivervision, particularly when using mirrors. However, note that in dark/lighted conditions, theratio of right to left increases to 7.7, or by almost 88 percent, compared to the daylight
condition. One interpretation of these results is that darkness exacerbates the problem ofusing the right-side mirror to determine if a maneuver to the right is safe, but that there23may be some help from the other vehicle’s headlamps. However in the dark/lighted
condition, the roadside lights in essence wash out or mask the backscatter from the othervehicle’s headlamps, resulting in the significant increase in the ratio. The results are
similar for right and left turn mirror-relevant crashes, and the interpretation would also be
similar.
Table 12Involvement Ratios of LCM and Turning Crashes by Light Condition
TIFA/GES 1994-2000

Daylight

Dark/
lighted

Dark

Dawn/
Dusk

Unk.

Total

Ratio right-to-leftlane change

4.1

7.7

5.6

9.5

1.2

4.4

Ratio right-to-left
turns

4.0

8.9

5.0

6.2

0.7

4.2

Crash comparison

Supplemental Data Collection on Selected Crash Types — Three crash types wereidentified for supplemental data collection. Each crash type was “mirror-relevant,”
meaning that the conflict occurred in an area that the driver would have to use the mirror
system to see. The three crash types were LCM right, right turn involving a pedestrian orother nonmotorist, and start up involving a pedestrian or other nonmotorist. Both thestart up and right turn crash types involve low speed maneuvers, typically in urban areas,since they involve collisions with pedestrians or other nonmotorists. The LCM rightcrashes are primarily crashes on high-speed roads, usually involving two or more travel
lanes in the same direction.
LCM right crash involvements account for 6.8% of all truck involvements, which
is a significant portion of the truck crash population and sufficient justification for
selection for the more detailed case review. The start up and right turn crash
involvements selected for review both are limited to those with pedestrians and othernonmotorists and account for a much lower proportion of the truck crash problem. For all
crash severities, about 7.8 percent of truck involvements are collisions with nonmotorists.
It is difficult to estimate with confidence the proportion of involvements coveredby the nonmotorist crash types reviewed. In the crash typology developed for this project,
the start up and right turn crash types account for about 10.0 percent of24

truck/nonmotorist crash involvements, and 6.3 percent of fatal involvements, but both are
likely underestimates. About 470 nonmotorists are killed annually in collisions withtrucks, so those two crash types account for at least 30 fatalities per year. This figure is
likely an underestimate. The mass crash data files have significantly less information
about nonmotorists than they do about motor vehicles. In both the TIFA and the GES file,the crash type for most nonmotorist involvements is “hit object in road,” with thenonmotorist being the object. There is no further information about the movement of thenonmotorist, as there is for motor vehicles. So the tools available to specify nonmotorist
crash types are limited.
In addition, there has been limited research on truck/nonmotorist crashes so it isdifficult to determine a better estimate. Retting, in a study of truck/pedestrian fatal
crashes, found that 47.5 percent of truck/pedestrian occurred when the truck started froma stopped position (Retting 1993). Another 14.4 percent occurred in right turns by thetruck. Retting manually reviewed 202 fatal truck/pedestrians crash reports in four cities.
Since he reviewed crashes only from four urban areas, it is not appropriate to extend
those percentages to the whole population. Crash types are likely to be different in ruraland suburban areas. The correct answer likely lies somewhere between the Retting
finding and the proportions in the analysis here.
The typology here is about as detailed as possible, using the information in themass crash data. Given the limited information about pedestrian movements in the mass
crash files, it would be necessary to do something like what Retting did, but in anationally representative set of cases. In the scope of the current project, that is notpossible. However, in both the start up and right turn crash types, the critical points occur
in areas around the truck where the truck driver cannot see, either directly or by the use of
mirrors. Therefore, in low speed maneuvers in urban areas with nonmotorists present, theonus of collision avoidance is of necessity on the nonmotorist. Improved vision in theseareas would aid the driver in these situations, by giving him a chance of seeing thenonmotorist. Moreover, addressing the problem with nonmotorists in low speed
maneuvers would likely help in other crash types that do not involve nonmotorists butwhere the conflict is in the same area.

25

The supplemental data were collected by reviewing police accident reports(PARs). UMTRI collects PARs as part of its TIFA program, and these PARs were used
for the review. Since the cases were selected from the TIFA file, all the cases involve
fatal crashes. All qualifying crashes from 1999 and 2000 (years for which TIFA cansupply PARs) were taken.
The primary purpose of the data collection was to determine the position of theother party at two points: three to five seconds before the collision and at the decision
point for the truck driver. Note that all three crash types are those that the truck driverinitiated by turning, changing lanes, or starting up. Accordingly, the positions of the otherparty prior to the maneuver and at the time when the driver decides to make the maneuverare important. Choosing three to five seconds as the position to code prior to the collision
is somewhat arbitrary, but the combination of that position and the decision point does
provide a view of the motion of the other party relative to the truck.
The interface for the data collection is shown above in Error! Reference source
not found.. The primary source for the information is the scene diagram, reporting
officer’s narrative, and any witness statements included in the police report of theaccident. These items are of course of varying quality, both within a state and between
states. Some reports included fairly detailed crash reconstructions. Other reports are very
sketchy, with little usable detail.
The positions of the other party are of necessity estimates based on available
information and deductions from the nature of the parties. An older pedestrian is assumed
to move slowly, while bicyclists move more quickly. If overtaking speed is given in an
LCM right crash, the position prior can be determined by tracking back from the impact
point on the truck to where the other vehicle was prior to the crash. For example, a 10
mph differential in travel speed implies a difference of 14.7 feet per second. If a truck is
65 feet long and the impact point was the front right quarter of the tractor, the othervehicle would have been 44 to 73 feet behind the front of the tractor three to five seconds
prior to impact. Where available, such information was taken into account, but for mostcases coding is a best estimate. Missing data is high for some crash types. For the LCM
right crash type, for example, it was not possible to estimate the prior position for 15 of
the 40 cases.
26

Table 13 shows the distribution of truck configuration for the reviewed crashtypes. The dominant truck configurations are straight trucks and tractor-semitrailers.
Straight trucks are somewhat overinvolved in these crashes: straight trucks account for28.2 percent of trucks in fatal truck crashes and tractor-semitrailers account for 59.6
percent. But both types are well represented here, with 38.5 percent and 52.7 percent
respectively. Even in the start up and turn right crash types, tractor-semitrailers accountfor almost half the involvements.
Table 13
Truck Configuration by Crash Type for Cases Reviewed
TIFA 1999-2000

Truckconfiguration

LCM right

Start up/nonmotorist

Turn right/
nonmotorist

N

%

N

%

N

%

Bobtail

2

5.0

2

5.3

4

Straight truck

10

25.0

15

39.5

Straight &trailer12.5

4Tractorsemitrailer

27

67.5

Total

40

100.0

Total
N

%

5.7

8

5.4

32

45.7

57

38.5

10.5

0

0.0

5

3.4

17

44.7

34

48.6

78

52.7

38

100.0

70

100.0

148

100.0

To code the position of the other vehicle, the area around the truck was divided
into a grid, and the other vehicle was placed in one of the cells. The position along thetruck was divided into thirds, with the front third essentially encompassing the tractor or
cab portion of a straight truck. The other vehicle was then located either in the lane to theright, the lane to the left, in front or to the rear of the truck. For start up and right turncrashes, the nonmotorist could be coded on the sidewalk or shoulder and areas were also
included for parties more than 20 feet to the right. This area was also used in the case of
LCM right crashes to locate vehicles that merged left as the truck merged right into thelane between them. It was also used in start up and right turn crashes to locate parties(usually bicyclists) that were moving on the road intersecting with the one the truck was
on initially.
Lane Change/Merge to Right — Figure 5 shows the position of the other vehicle
three to five seconds prior to the collision. The position could not be estimated for 15 of27

the 40 cases reviewed, so the rate of missing data is high. The percentages shown in thetable were calculated after excluding the unknown cases. This procedure assumes that thetrue position for the missing cases follows the same distribution as those for which theposition could be estimated. This seems a reasonable assumption, since there is no reason
to think that vehicles in some positions would be less likely to be identified than those inother positions.

Figure 5. Position of other vehicle 3-5 seconds prior to truck driver’s decision point, Lane Change/Merge to
Right Crashes, N=40, TIFA 1999-2000. Fifteen of 40 cases were excluded from the calculation ofpercentages because the position could not be determined.

The number of cases in Figure 5 is small (40), but the cases include all LCM rightfatal crash involvements for 1999 and 2000. Three more years, with potentially sixty
more cases, are available but could not be reviewed within the constraints of time andfunding. Also the additional years were subsequent to the period covered by the mass
crash data analysis. Because the number of cases reviewed is small, confidence intervals
for the proportions are relatively large. For example, the 95 percent confidence interval
for the estimate that 32 percent of the other vehicles were to the front right of the cab is±18 percent. (Adding three more years of cases would cut the confidence intervals almostin half.)

28

The size of the confidence intervals is a concern. However, many of the findings
discussed below are consistent with expectations and with experimental results. The
experiments described in Section 3 of this report identified the front right of the truck as aconcern. The fact that the results from crash analysis are consistent with the experimental
finding boosts confidence the results reported here are real, even if confidence in thestatistical sense is not high.
Over half of the vehicles for which the prior position could be estimated were
either along side the front of the truck or in front of the truck. A total of 40 percent of theother vehicles actually were along side the truck three to five seconds prior to the crash,and most of these were to the front. The vehicles coded directly to the front of the truck
were being overtaken by the truck, which sideswiped them while maneuvering back into
the lane. Only two cases were found in which the other vehicle was overtaking the truck
rapidly on the right; and there was only one case (4 percent here) in which the othervehicle changed lanes left into the same space into which the truck was moving. In mostof the LCM right crashes, the conflict is to the immediate right of the truck or slightly infront.
The position of the other vehicle at the decision point, that is the point at which
the truck initiates the maneuver, was significantly easier to determine. The position atdecision point could not be determined for only three of the 40 cases. As shown in
Figure 6, in almost half the cases (48.6 percent, ±16.1) the other vehicle was to the rightof the cab at the decision point. An additional 21.6 percent were alongside the truck
behind the cab. The cases coded in the lane next to the truck but still forward of the truckat the decision point included cases in which traffic suddenly slowed and the truck
maneuvered unsuccessfully to avoid a collision and one case of reckless driving by thetruck driver. However, the primary finding is that in these crashes the other vehicle ismost frequently along side the front right of the truck when the driver decides to change
lanes to the right.

29

Figure 6. Position of other vehicle at decision point, Lane Change/Merge to Right Crashes. TIFA 19992000, N=40. Not applicable: 10.8%. Three unknowns were excluded from percentage calculations.

The position three to five seconds prior to the crash could not be determined forhalf of the cases that were next to the cab at the decision point, so whether those vehicles
had been pacing along side the truck in its forward blind spot cannot be determined.
However, for cases along side the cab at the decision point that could be located prior tothe crash, all but one were along side the truck prior to the crash and the one exception
was just forward of the truck. None were rapidly overtaking from the rear.
Findings from the analysis of mass crash data reinforce the point. The TIFA data
includes a variable that identifies the initial point of impact on the truck. Figure 7 is basedon analysis of seven years of the TIFA file, not the case review. It shows the distributionof initial impact point on the truck in fatal crashes in which the truck was making a lanechange or merging either to the right or left. (Nonfatal crash involvements could not be
included because the GES file includes only the side of impact, not the location on theside.) The figures of the trucks are adapted from the coding manual used by FARS
analysts to record the data. Both straight trucks and tractor-semitrailers are included in
the data. It is important to remember that only the first contact point is recorded, so the
movement of the other vehicle prior to impact is not reflected. Thus, we do not know if

30the other vehicle was moving alongside the truck at a steady state or rapidly overtaking
the truck. Nevertheless, the distribution of contact points is strikingly and suggestively
different when the truck is moving left than when the truck is moving right.

Figure 7. Distribution of initial impact on the truck in two crash types. Fatal crashes only, Total N=200,
TIFA 1994-2000.

When the truck changed lanes or merged to the right, almost 48 percent of thecontact points were in the cab area, compared with only 25.5 percent of left movements.
And when moving right, an additional 13.0 percent were in the front of the truck, which
is at least consistent with the scenario of the other vehicle moving at the same speed as
the truck in the truck's forward blind zone. In total, over 60 percent of contact points on
the truck in fatal crashes in which the truck changed lanes to the right occurred on thefront of the truck, including the front plane and the right side of the cab. Proportionallyfewer of the contact points on the truck in movements to the right were to the rear of thevehicle, with only 8.9 percent in the rear fifth of the truck. Contact points were more
evenly distributed along the left of the vehicle in lane changes to the left, while in lane31changes to the right, contact occurred more frequently to the front of the vehicle. Thissuggests that, in lane changes to the left, the other vehicle more frequently was
overtaking and contacted the truck at an essentially random point. But in lane changes tothe right, about half the time the other vehicle was pacing alongside the truck to the rightof the cab.
Start Up Crashes — Start up crashes include all crashes in which the truck wasstarting from a stopped position on the roadway and collided with a pedestrian, bicyclist,
or other nonmotorist. Most often the truck was stopped at an intersection and was the leadvehicle. The pedestrian crossed in front of the vehicle and was struck by the front of thetruck as it started forward. This crash type also includes cases in which the truck was
stopped on the roadway and a pedestrian attempted to cross between the truck and trailer.
Note that start up crashes can include both pedestrians and bicyclists. Pedestrians are
typically close to the vehicle prior to the crash, but bicyclists can be some distance awaysince they typically travel much faster than people on foot.
Table 14 shows the relative motion of the other party prior to the crash. In mostcases, the other party was crossing the intended path of the truck, most frequently fromthe right to the left, but sometimes from the truck’s left to its right. In six of the 38 cases
reviewed, the other party was essentially stationary. In two of these, there had been aprior collision between the truck and another vehicle (of which the truck drivers were
unaware) and the pedestrian was the other driver inspecting damage or calling for help. In
another three instances, the pedestrian was a worker around the truck or a passenger who
had exited the truck and was retrieving possessions from behind the cab. And in one case,a pedestrian had suffered a seizure and had collapsed in front of the truck when it pulledforward.

32

Table 14
Motion of Nonmotorist Relative to Truck, Start Up Crashes
TIFA 1999-2000
Nonmotorist Motion

N%

Crossing Right to Left

16

42.1

Stationary

6

15.8

Crossing Left to Right

5

13.2

Same direction

1

2.6

Unknown

10

26.3

Total

38

100.0

Figure 8 shows the position of the other party three to five seconds prior to thecollision. The most frequent position is in front of the vehicle, typically older pedestrians
moving slowly, but it is interesting to note the diversity of positions. In the cases
immediately in front of the truck, it is likely that they were close enough to the truck to
be hidden by the hood. In all cases where it was mentioned, the truck driver said he did
not see the pedestrian prior to the collision, or did see the pedestrian (not in front of thetruck) but assumed that the pedestrian had cleared the truck’s path. For example, in one
crash the driver saw a 72-year old pedestrian attempting to cross in front. He waited until
he thought the pedestrian had cleared, but the pedestrian had fainted and was struck as thetruck pulled forward. But in most cases, the driver said that he never saw the other party,
whether pedestrian or bicyclist.4

4

The claim by the driver that he did not see the nonmotorist prior to the crash is of course self-protective.

But the drivers who did not stop immediately continued normally after the crash, i.e., betrayed no
consciousness of having just struck someone.

33

Figure 8. Position of nonmotorist 3-5 seconds prior to crash, Start Up Crashes, TIFA 1999-2000, N=38.
Other (underneath truck): 3.7%. Unknowns account for 28.9% of the 38 cases and are excluded from thepercentage calculations.

At the decision point, in over half the cases the pedestrian or bicyclist was either
to the left or right of the truck or directly in front of it (Figure 9). In almost 58 percent of
the crashes, the other party was directly in front of the vehicle. An additional 9.1 percent
were in the lane to the right in front of the truck, and 9.1 percent were to the left. The
cases coded behind the truck include two in which the pedestrian attempted to crossbetween the truck and trailer and one in which the pedestrian was a worker crushed when
the truck rolled backward. Two cases coded at the side of the truck toward the rear or on
the sidewalk toward the rear were likely suicides.

34Figure 9. Position of nonmotorist at truck decision point, Start Up Crashes, TIFA 1999-2000, N=38. Other(underneath truck): 3.0%. Position could not be coded for 13.2% of the 38 cases, and are excluded fromthe percentage calculations.

As in the LCM right crash type, the number of cases available for review is small,resulting in relatively large confidence intervals. For example, the estimate that 57.6
percent of the nonmotorists were directly in front of the truck at startup has a 95 percent
confidence interval of ±16.9 percent. On the other hand, even with that large uncertainty,
it is clear that directly in the front of the truck is the primary point of concern.
Older pedestrians are overrepresented in truck start up crashes. Older pedestrians
tend to move more slowly and take longer to clear the path of the truck. They also areless agile and less able to move out of the way of the truck when it starts unexpectedly.
Figure 10 shows the distribution of pedestrian age in fatal crashes. Only pedestrians areincluded in the figure and separate distributions are shown for the start up crash type andall other crash types. Note that the distribution of pedestrian age is significantly shifted to35the right (higher ages) in start up crashes compared with pedestrians in all other truck
crash types.
25.0
Other
Start uppercent

20.0

15.0

10.0

5.0

0.0
<10

10 <20

20 <3030 <4040 <5050 <6060 <7070 <8080 <9090 +Age categories

Figure 10. Distribution of pedestrian age in fatal start up and other truck crashes. TIFA 1994-2000.

Table 15 is also restricted to pedestrians involved in truck crashes and compares
the percentage of pedestrians in three age categories for start up and other truck crashes.
In fatal truck start up crashes, fully 52.2 percent of pedestrians struck are over 65 years
old, compared with only 16.5 percent of other truck/pedestrian crashes. Only 3.0 percent
are under 17. It appears that older pedestrians are much more vulnerable to this crash typethan other pedestrians. Given the fact that the truck driver is largely unable to view theimmediate vicinity of the vehicle, in low speed maneuvers the burden of avoiding acollision is largely borne by the other parties around the truck. Populations that are lessmobile are especially vulnerable.

36

Table 15
Percentage Distribution of Age of Pedestrians in Start Up
and Other Fatal Truck Crashes
TIFA 1994-2000
Crash type
Pedestrian age

Start up

Other

Total

< 17

3.0

8.2

8.0

17 to 65

43.3

74.4

73.5

> 65

52.2

16.5

17.5

Unknown

1.5

1.0

1.0

100.0

100.0

100.0

67

2,252

2,319

Total
N

Turn Right Crashes with Nonmotorists — Turn right crashes involving a truck
making a right turn and colliding with a pedestrian or other nonmotorist actually occur ina variety of configurations. Seventy cases were reviewed. Figure 11 shows the mostcommon configurations. In each diagram, the turning arrow represents the truck and thestraight arrow represents the movement of the pedestrian, bicyclist, or in one case, askater on rollerblades. The number of cases is shown for each configuration.

Figure 11. Selected right turn/nonmotorist crash configurations, TIFA 1999-2000.

37

Most right turn/nonmotorist crashes occurred when the truck attempted a rightturn and the pedestrian or nonmotorist went straight. Many of these crashes involved abicycle, either in the lane next to the truck, or riding on the sidewalk next to the truck.
Similarly, some of the crashes depicted in diagram B involved a bicycle riding along theside of the road against traffic, on the roadway onto which the truck was turning.
Relatively few of the crashes involved off-tracking to the extent that the trailer axles
encroached onto the sidewalk during the turn. Diagram D depicts one set, in which thepedestrians were stationary on the corner when struck by the trailer axles. The diagrams
depicted in Figure 11 are the primary right turn/nonmotorist configurations identified in
the case review.
While the number of crashes in which the trailer axles off-tracked up onto thecurb and sidewalk is relatively small, off-tracking even when the axles stayed in the road
was a factor in many of these accidents. For example, in one case an older woman was
crossing the road onto which the truck was turning. The driver stopped to allow the
woman to pass in front and waited until he thought she should have made it to the curb.
He checked his mirror, did not see the woman, and proceeded. But the woman had
paused in the road, and when the truck moved forward, the trailer swung into her,knocked her down, and she was struck by the rear axles.
In a similar case, but a different configuration, an older pedestrian intended tocross the street, as in diagram B in Figure 11. The truck pulled forward to make the turnand in fact swung wide to avoid off-tracking onto the curb. The pedestrian stepped out
into the street anticipating the truck clearing, but was taken by surprise as the trailer
swung toward him as the truck completed the turn. He attempted to get out of the way,
but stumbled and was struck.
Figure 12 shows the position of the nonmotorist prior to the decision point in rightturn fatal truck crashes. The position could not be determined in 12 of the 70 cases
reviewed, or 17.1 percent. Cases coded unknown on prior position are excluded whencalculating percentages. About 43 percent of the nonmotorists were on the sidewalk next
to the truck prior to the turn. Most of these are about level with the cab or somewhat infront of the cab. The cases coded more than 20 feet to the right are typically bicyclists
either on the sidewalk (3.4 percent here) or riding along the curb against traffic in the38

roadway (8.6 percent). Another 8.6 percent were on the sidewalk but behind the truck.
These cases were bicyclists riding on the sidewalk and overtaking the truck.

Figure 12. Position of nonmotorist 3-5 seconds prior to decision point, Turn Right/Nonmotorist Crashes,
TIFA 1999-2000, N=70. Twelve of 70 cases are excluded from calculation of percentages because theposition could not be coded.

About a quarter of the cases coded in the lane next to the truck were bicyclists.
The ones behind the truck were overtaking it, while the ones next to the cab or slightly
ahead were typically stopped next to the truck, waiting for the traffic signal to change andintending to proceed straight ahead. In virtually all cases, the truck driver reported notseeing the bicyclist. In one such case, a man and a woman on bicycles had stopped next
to a concrete mixer, with the man in the lead. When the light changed, the truck started
forward to turn but paused to allow the man to clear. The driver did not see the following
woman, who collided with the side of the truck as it executed the turn.

39

Figure 13 shows the position of the nonmotorist at the decision point for the truck.
This is the point at which the driver initiated the turn. A third of the nonmotorists forwhom this position could be estimated were on the sidewalk or shoulder and even withthe cab of the truck. While a few were struck when the truck off-tracked up onto thesidewalk or shoulder, in most cases the nonmotorist proceeded into the street where they
were struck. Only 14.8 percent were estimated to be just in front of the truck at turn
initiation. Almost 20 percent were in the road and slightly ahead of the truck, but likely
still in the forward blind zone.

Figure 13. Position of nonmotorist at truck driver’s decision point, Turn Right/Nonmotorist Crashes, TIFA
1999-2000, N=70. One case was not applicable (driver lost control due to a medical emergency). Sixteenof 70 cases were excluded from the calculation of percentages because the position could not be coded.

As in start up crashes, older pedestrians are significantly overrepresented in rightturn truck fatal crashes. Almost 42 percent of pedestrians struck in right turn crashes
were over 65, compared with only 17.0 percent of pedestrians struck in other crash types.
From the review of the events of specific crashes it appears that some are similar to start40

up crashes, in that the older pedestrians proceeded more slowly than other pedestrians
and the driver’s vision was blocked by the hood. But many were related to off-tracking,even when the truck stayed entirely in the road. In these crashes, the pedestrian stepped
into the street as the truck started forward, anticipating the truck’s movement. But they
were taken by surprise when the trailer axles tracked inboard of the tractor, and wereunable to move out of the way. It appears that the truck driver had no way to see thecrucial areas while making the turn and so had no idea that there was a problem until
after the event, either by sensing a thump as the wheels passed over the pedestrian orwhen alerted by others that an accident had occurred.
Table 16
Percentage Distribution of Age of Pedestrians in Right Turnand Other Fatal Truck Crashes
TIFA 1999-2000
Crash type
Pedestrian age

Right turn

Other

Total

< 17

4.7

8.1

8.0

17 to 65

53.5

73.9

73.5

> 65

41.9

17.0

17.5

Unknown

0.0

1.0

1.0

100.0

100.0

100.0

43

2,276

2,319

Total
N

2.3

Summary and Discussion
Mirror-relevant crashes account for almost 20 percent of all crash involvements.

That is, crashes in which the truck driver needed to use mirrors to maneuver safely
accounted for about one in five truck crash involvements. In most of the mirror-relevantcrash types, the crashes involved relatively low closing speeds, either because the truck
was moving slowly, as in start up and the turning crashes, or because both vehicles weremoving in the same direction on the same roadway and the truck maneuvered into theother vehicle, as in the lane change crashes. In these crashes, the conflict was close to thetruck and the driver had to rely on the truck mirrors to determine where the other vehicle
or nonmotorist was.

41

Truck crashes in which the driver must rely on mirrors to move the right are
significantly overrepresented in comparison with left moves. LCM right crashes occurred
over four times more frequently than LCM left crashes. Similarly, turn at intersection
crashes (turn right crashes) in which the conflict comes from the rear are over four times
more frequent in right turns than in left. The distance from the driver to the right-side
mirror and the relatively smaller image in the mirror makes maneuvers to the right morerisky than similar maneuvers to the left.
Given the over-representation of mirror-relevant crashes to the right, it was
hypothesized that factors that might further obscure the image would affect the relative
frequency. Darkness and weather were both considered. Weather condition was used as asurrogate for splash and spray that may coat the mirrors and obscure the image. Only arelatively small effect was found. When there was precipitation, the over-representationof right to left LCM crashes increased by about 10 percent. But in turns at intersection,
the ratio of right to left actually decreased by about 10 percent. Possibly splash and sprayon the mirrors is not a problem or if it is, it is relative small.
Light condition, however, plays a larger and consistent role. Darkness increased
the right/left ratio for LCM crashes by about 37 percent. The dark/lighted condition
further increased the relative frequency of lane changes to the right. Apparently darkness
obscured the view, and when there were streetlights, those lights washed out reflectancefrom the headlamps of the other vehicle, making it very difficult for the truck driver todetect other vehicles to the right.
Supplemental data on selected crash types reinforced the findings from theanalysis of the computerized crash files, and identified the right front as a significant
problem area. A sample of police reports on fatal crashes were reviewed for three types:
LCM right, start up, and right turn.
In LCM right crashes, the area to the right of the cab or just in front appeared to
be a significant problem. In over half of the crashes reviewed, the other party was in thatarea three to five seconds prior to the collision. And in almost half of the collisions, theother vehicle was to the front right of the truck’s cab when the truck driver initiated themove to the right. Identification of front right as major area of concern is reinforced in42the mass crash data by the findings on the initial impact point. Where the LCM was to theright, there was a concentration at the front of the vehicle with almost 61 percent of the
impacts. In contrast, where the LCM was to the left, impact point distribution was
bimodal, with about 25 percent to the front left, about 25 percent to the rear left and theremainder distributed in between.
In start up crashes where the other party was a pedestrian and was struck by thefront, it is likely that in most cases the driver’s view was obstructed by the hood. Most
drivers said they never saw the pedestrian, while some drivers said they saw thepedestrian crossing and paused until they thought the pedestrian had cleared. For mostpedestrian start up crashes, the pedestrian was either in front of or near the front of thetruck three to five seconds prior to the collision. Almost 60 percent were in front of thetruck at the decision point, and an additional 18.2 percent were either immediately to theleft or right of the front of the truck. In the remainder, the pedestrian was behind the truck
and it rolled back, or the pedestrian was attempting to cross between the truck and trailer.
Clearly the primary zone is immediately in front of the truck.
Older pedestrians were significantly overrepresented in start up crashes. Over halfof the pedestrians struck were over 65, compared with only 16.5% for other types oftruck/pedestrian fatal crashes. In many of the collisions with older pedestrians, theirshorter stature and slower foot speed were factors mentioned in the police reports. The
truck drivers who saw the pedestrian prior to the collision waited until they thought it wassafe to proceed. But without cross-view mirrors, the driver has no way of seeing the areain front of the vehicle, so the burden of crash avoidance falls largely on the pedestrians.
Right turn crashes are more complex, and involved off-tracking as well as blind
spots. When the crashes involved bicyclists stopped alongside the truck in the street, thedriver frequently stated that he could not see them. These crashes were like start up
crashes in that typically the bicyclist was waiting along side the truck, unseen by thedriver, and when the truck started forward, the bicyclist did also and was struck when thetruck turned. In most cases in which the bicyclist was initially on the sidewalk, thebicyclist attempted to cross in front of the truck without stopping. In all such cases, thetruck driver reported not having seen the bicyclist prior to making the turn.

43

There were a few cases of classic off-tracking, in which the trailer or truck rearaxles mounted the curb and encroached on the sidewalk or shoulder. But much morecommon were collisions in which the truck initiated its turn—in some cases swinging
wide so as to avoid running up on the curb—and the pedestrian stepped out into thestreet, anticipating the truck clearing out. But when the trailer swung toward them as thetrailer axles tracked inboard of the tractor path, the pedestrian was unable to get out of theway in time and was struck. Older pedestrians were also over-represented in right turncrashes, with pedestrians over 65 accounting for almost 42 percent of the involvements,
compared with 17 percent of other crash types.
In sum, the area to the right of the truck, particularly right forward, is clearlysignificantly overrepresented in certain crash types. In lane change crashes, movement to
the right is overrepresented by over four times compared to movement to the left, and the
problem is exacerbated at night and in dark/lighted conditions. In low speed maneuvers
involving pedestrians and other nonmotorists, the fact that the truck driver is largely
unable to view the immediate vicinity of the vehicle places the burden of avoiding acollision largely on the other parties around the truck. Populations that are less mobile areespecially vulnerable. The critical areas are immediately in front and to the right of thetruck.

44

3.0
3.1

EMPIRICAL TEST OF MIRROR SYSTEMSIntroduction
In this section, we describe a pilot experiment that was designed to quantify thedifficulty of a driver’s visual scanning task for various locations around a heavy truck andfor different mirror systems. The purpose of this experiment was to develop a method
and to provide preliminary data on visual difficulty.
The method was implemented in a static, parking lot setting, but it is intended to
be extendable to fully dynamic driving situations in actual traffic. Making this extension
will require further work, and presumably will require some modifications of theprocedure.
Crash data, described in Section 2 of this report, provide one source of
information about the areas in which a driver experiences visual difficulty. The right side
appears to present more of a problem than the left, and this difference appears to behigher at night than during the day. Data from the current experiment can be comparedto the findings from the crash data. This approach will allow us to develop a model ofvisual difficulty to account for the major findings in the crash data, and, more importantly
for practical considerations, make valid predictions about the efficacy of various
countermeasures, such as innovative mirror systems, to reduce problems with visualdifficulty.

3.2

Methods
An outdoor, parking lot situation was used to simulate statically the mostimportant visual circumstances that a driver would be presented with in traffic that ismoving at a common speed on a multilane road.
Subjects — Six professional truck drivers were paid to participate in the study.
All were male and ranged in age from 39 to 63, with an average age of 51.8 years.
Gender and age were not explicitly selected in determining the sample. Subjects wererecruited through e-mail messages that were sent to truck drivers in the southeast
Michigan area who were members of the Owner-Operator Independent Drivers
45

Association (OOIDA).

By self-report, the subjects ranged in years of professional

driving experience from 5 to 45, with an average of 17.2 years.
Vehicles — Two vehicles were used: a tractor-trailer and a passenger car. The
tractor-trailer consisted of an International tractor and an UMTRI box trailer. As shownin Error! Reference source not found., the entire combination was 19.5 m (64 ft) long.
The trailer itself was 13.7 m (45 ft) long. The passenger car was a 1993 Nissan Altima.
Mirrors and Fields of View — Three mirrors were mounted on each side of the
tractor: a flat (west coast) mirror, a window-mounted convex mirror, and a fendermounted convex mirror. All mirrors were left in place throughout the experiment, butblack covers were placed over one or the other of the convex mirrors during blocks oftrials when they were not to be used. The mirrors were aimed using the guidelines
developed by Liberty Mutual (1998). The convex mirrors were aimed for a typicaldriver’s eye position and were not reaimed for individual drivers. This proved to besatisfactory for all drivers, primarily because of the wide fields of view of those mirrors.
The west coast mirrors were reaimed for each driver. The visibility of the passenger car
in the fields of view of the three mirrors is characterized in Table 17, and the fields ofview are shown in Figure 14.

46

Table 17
Visibility of the Passenger Car in the Three Rearview Mirrors, Right Side
(empty cell = no visibility, x = partial visibility, xx = full visibility)
Distance aft of
Front Bumper(m)MirrorFlat (westcoast)

Window
ConvexFender convex

0

x

xx

1

x

xx

3

x

xx

5

x

xx

10

xx

xx

15

x

xx

xx

20

xx

xx

xx

25

xx

xx

xx

30

xx

xx

xx

35

xx

xx

xx

Field Setup — The study was conducted in a flat, open area of the UMTRIparking lot. The setup is diagrammed in Figure 14. The tractor-trailer was parked at one
end of the area, so that the passenger car could be placed in a variety of positions
alongside or behind it. Using visual reference marks that were not visible from the cab of
the tractor-trailer, the car could be placed at 0, 1, 3, 5, 10, 15, 20, 25, 30, or 35 m behind
the tractor-trailer (measured from the front of the tractor to the front of the car). The car
could be one lane to the left, one lane to the right, or two lanes to the right of the tractortrailer. Lanes were 3.7 m (12 ft) wide. The parking lot was equipped with fixed lighting,but that lighting was turned off for the night condition.

47

0
1

3

5

10

15

20

25

30

35

Figure 14. The field setup, showing the position of the tractor-trailer and the potential positions of thepassenger car one lane to the left (similar positions could be occupied in the first or second lane to the rightof the tractor-trailer). Approximate mirror fields of view are shown for the planar mirrors (green), doormounted convex (blue) and fender-mounted convex (orange) on the ground plane (solid lines) and 1.2-m
plane (dotted lines). The colored bars to the right show the approximate visibility of the target vehicle ineach of the right-side mirrors as a function of distance aft of the front bumper. For example, the vehicle
was partially visible in the planar mirror at the 10-m mark and fully visible at the 20-m mark.

48

Procedure — Each subject participated individually. For each subject, there were
two sessions, each about two hours long. The first was always a day session and thesecond was a night session. At the beginning of the first session, each subject filled out abrief questionnaire about his professional driving experience and use of rearview mirrors
on heavy trucks. He was also given a vision test. He was then seated in the tractor-trailer
and the west coast mirrors were aimed.
The procedure was the same for the night and day sessions.

For the night

sessions, all fixed lighting was turned off before the session began. During each session,
8 blocks of trials were run. Each trial began with the driver looking at a designated point
inside the cab. This allowed the passenger car to be moved into position without the
subject seeing it. During nighttime sessions, the car was moved into position with theheadlamps off so that the subject could not sense the position of the car by direct orreflected light from the headlamps. The headlamps were turned on after the car was inposition. So that the headlamps would not make it possible to discern the location of the
car after they were turned on, different visual fixation points were used for the day andnight sessions. During daytime sessions, the subject looked at a small indicator lamp on
the lower part of the instrument panel, below the steering wheel. During nighttime
sessions, the subject looked at a position on either the right or left interior of the cab(opposite the side on which the car might be positioned for that trial). At the end of eachsession, the subject completed a short debriefing questionnaire. At the beginning of eachtrial, an experimenter in the rear of the cab warned the subject that a trial was about tobegin.
The experimenter then initiated a trial by pressing a button on a computer that
kept track of trials and collected responses from the subject. The computer than either
turned on the indicator light (during daytime sessions) or made an audible signal (during
nighttime sessions). These signals indicated to the subject that he should look to apredesignated side of the truck (right or left), and, using any combination of direct andmirror fields of view, determine whether the car was present on that side in a location thatwould be in conflict with a lane change to that side. He was to make that determination
as quickly as possible and indicate his decision by pushing one of two buttons on a small
hand-held box: one marked “Go” (indicating that the car was either not present, or was49

present in a position that did not conflict with a lane change), and one marked “No go”(indicating that the car was present and in a position that conflicted with a lane change).
The computer recorded which button was pushed, and the time between the signal to thesubject (the indicator light or audible signal) and the button push.
During each block, the car, if present, was always on the same side of the truck.
The subject was therefore always aware of which side to look to on each trial. During
each of the first 6 blocks, the car, if present, was always either one lane to the left or rightof the truck. The first six blocks each consisted of 15 trials, including 5 on which the car
was not present and 10 on which the car was present at each of the longitudinal positions(0, 1, 3, 5, 10, 15, 20, 25, 30, or 35 m). The seventh and eighth blocks each consisted of
12 trials, including 4 on which the car was not present and 8 on which the car was present
at each of four longitudinal positions (5, 15, 25, or 35 m) one or two lanes to the right of
the truck. On trials when the car was not present, it was positioned in the blind zone
behind the truck so that it would not be visible to the subject in any direct or mirror fieldof view. Within each block, the order of trials was randomized so that the subject could
not predict whether the car would be present, or in what position it might appear.
It was left to the subject’s judgment to decide whether the car was in a position
that would be in conflict with a lane change. This was true for both longitudinal position(in all blocks of trials) and lane position (in the last two blocks of each session, during
which the car might be either one or two lanes to the right).
The availability of mirrors was changed between blocks of trials, and remained
fixed within each block of trials. For each session, the first six blocks of trials included
the following three sets of available mirrors, once on each side: (1) flat, window-mounted
convex, and fender-mounted convex; (2) flat and window-mounted convex; (3) flat andfender-mounted convex. For each session, the seventh and eighth blocks included thefollowing two sets of mirrors: (1) flat, window-mounted convex, and fender-mounted
convex; (2) flat and window-mounted convex.

50

3.2

Results and Discussion
Results are reported in terms of response type and then reaction time. Finally, we

discuss the relationship between response type (coded as correct or incorrect) andreaction time.
Proportion “go” responses — The proportion of “go” responses is shown in
Figure 15 as a function of distance behind the front of the tractor-trailer, for the left andright sides. There were very few go responses for positions within the first 20 m, as
might be expected because there was overlap between the vehicles for all positions backto 15 m, and there was so little clearance at 20 m (see Figure 15) that many people would
consider “no go” to be the correct response for that position as well. There are a few go
responses at very short distances, but only on the right side. All go responses at shortdistances can be considered mistakes, because the passenger car was clearly in conflictwith a lane change by the tractor-trailer when it was in those positions.
The same data are shown in Figure 16 broken down by night and day. The
increase in go responses with distance is shifted to higher distances at night, as if drivers
were more conservative at night, perhaps because the task was more difficult in the more
limited viewing conditions of nighttime. Figure 16 shows that the go responses at thevery short distances occur primarily at night.

Figure 15. Proportion “go” responses by side and distance of the target passenger car.

51

Figure 16. Proportion “go” responses by light condition and distance of the target passenger car. Right andleft sides combined.

Figure 17 shows the data for the right side only to emphasize the effects on thatside. It is clear that the go responses at very short distances are primarily limited to thenight condition on the right side. That combination of conditions could be expected to be
the most difficult, given that direct views out the window are more limited on the rightside, and that partial visibility of the passenger car, near the edge of the window-mounted
convex mirror, may be less useful in the limited viewing conditions of nighttime.
Figure 18 shows a breakdown of responses by side and time of day
simultaneously. This view of the data makes it clear that the go responses at shortdistances are in fact primarily limited to the right side at night. This corresponds well tothe crash data, which indicate that crashes are overrepresented for the same combinationof conditions. Figure 18 also illustrates that the tendency to shift go responses to higher
distances that is evident in Figure 15 and Figure 16 is mostly restricted to the
combination of right side and night. Because this could be considered conservative
behavior, it would not be expected to lead to more crashes if it were happening in actual
driving, but it is also consistent with the hypothesis that there is an interaction of the
difficulties that drivers experience in seeing to the right and the reduction in visibility
caused by night conditions, such that the combination of right side and night isparticularly difficult.

52

Figure 17. Proportion “go” responses by light condition and distance of the target passenger car. Right-sidedata only.

Figure 18. Proportion go responses by distance for each combination of side and light condition.

Although it was left to the subjects to decide which positions of the passenger car
were in conflict with a lane change by the tractor-trailer, some patterns in the data are
best illustrated by imposing a rule that allows us to classify responses as right or wrong.
First, it is reasonably clear that all go responses at distances less than 20 m should beconsidered errors. A distance of 20 m is more ambiguous, but certainly lane change
maneuvers in that configuration would be risky by many drivers. Error! Reference
source not found. shows responses coded as error rates, using the assumption that no go
is the correct response at all distances of 20 m or less, and that go is correct at all
distances of 25 m or more. Using that assumption, the error rates at 25 m are particularly
high, and it might be argued that the criterion for the no go response should be pushed53

back to 30 m. However, the patterns of errors highlighted by this analysis, and which wewill relate to reaction time data below, would not be critically different if the criterion
were shifted to 30 m.
Figure 19 illustrates and highlights several aspects of the subjects’ responses thatcan be considered errors. First, there are errors on the right side at night at shortdistances. Second, there are many nominal errors when the passenger car is just behindthe end of the trailer, a circumstance that perhaps should be viewed mainly as anindication of the ambiguity of those situations. Third, whatever distance is used as thecriterion for when there is enough clearance to allow a lane change, there will be aparticularly large number of trials on the right at night that can be considered errors in aconservative direction.

Figure 19. Responses recoded as error rates, based on 25 m as the criterion for adequate separation for alane change.

Figure 20 shows the error rates in Figure 19 referenced to the condition with thefewest nominal errors (the left side in the daytime). This emphasizes the relative
difficulty of the various conditions, showing the previously noted pattern in which theright side at night is especially difficult.

54

Figure 20. Error rates referenced to the left-day condition.

Figure 21 shows a summary of responses from the blocks of trials in which thepassenger car could appear either one or two lanes to the right of the tractor-trailer. In
those blocks, subjects had to decide not only whether the passenger car was clear of therear end of the trailer, but which lane it was in. For the most part, they could decide thelane location reliably. The proportions of cases in which the car was two lanes to theright but subjects indicated no go may be attributable to conservative decision making, toavoid a possible conflict with a car that might itself change lanes, rather than to amisperception of which lane the car was in. Lane location and distance to the rear clearlyinteract in the final decisions made by these subjects, but the extent to which this patternreflects their intended strategy (or a normative strategy) and the extent to which it reflects
any errors in their perceptions of the location of the passenger car are difficult to separate
in these data.

55

Figure 21. Proportion “go” responses by lane and distance of the target passenger car, for trials in theblocks using two lanes to the right of the truck (R indicates one lane to the right, RR indicates two lanes tothe right).

Reaction Time — Figure 22 shows reaction times for each distance on the left andright sides. The times were mostly between 1 and 2 seconds—on the slow side but notout of line with decisions that drivers may make in real traffic when anticipating a lanechange. Reaction times are consistently higher on the right side, and the difference isparticularly large for the very short distances, at which there were a substantial number oftrials that can be considered errors. There also appears to be a rise in reaction times when
the passenger car is near the end of the trailer (about 20 m). The extra time is presumably
associated with the need to make finer discriminations, or perhaps to take into accountstrategic considerations, when the clearance with the end of the trailer is marginal.
Figure 23 shows the reaction time data further broken down by time of day. As
might be expected on the basis of the results for response type and errors, the highreaction times for short distances on the right side are particularly high at night.
Interestingly, there is not a general increase of reaction times at night. Rather, theincrease at night appears to be limited to the short distances on the right. However,interpretations of the overall effect of night versus day have to be considered tentative,
given that in the design of this study all subjects completed the day condition before the56night condition. Practice effects could therefore be expected to cause a general
improvement in the night condition.
Figure 24 shows reaction times referenced to the condition that had the shortest
reaction times (and the lowest error rates, as shown in Figure 20). This highlights therelative difficulty of the various conditions. As with the error rates, it indicates that the
combination of the right side and nighttime is particularly difficult. The right side during
the day is also difficult by the reaction time measure. At least in terms of reaction time,
the left side at night is not much more difficult than during the day.

Figure 22. Reaction time by side and distance of the target passenger car.

Figure 23. Reaction time by side, light condition, and distance of the target passenger car.

57

Figure 24. Reaction times referenced to the left-day condition.

Figure 25 shows the relationship between reaction times and error rates (as shownin Figure 19) for all combinations of side and distance. There is a clear positive
relationship, with the conditions that lead to more errors also requiring the longest
reaction times. This relationship makes interpretation of both the reaction time and error
rate results relatively straightforward, because both measures lead to similar conclusions.
The opposite relationship, a tradeoff between reaction time and error rate, is sometimes
observed and is usually considered an indication of relatively complex strategic effects on
the part of subjects.
Figure 26 shows the relationship between reaction time and error rates for thethree different mirror conditions. The best performance, in terms of both speed andaccuracy (at least as quantified here) was for the innovative condition in which there wasa fender-mounted convex mirror, but no window-mounted convex mirror. Althoughthese pilot data cannot be considered conclusive, the favorable results for this condition
suggest that the possibility of substituting a fender-mounted convex mirror for thecurrently standard window-mounted convex mirror should be further investigated.

58

Figure 25. The relationship between reaction time and error rate for the 20 combinations of side (L/R) anddistance. Conditions in which the passenger car was absent are not included.

59

Figure 26. Speed and accuracy by mirror condition. For right, night, and distances of 5 m and lower.

Figure 27 shows a histogram of responses to a question about the overall value of
fender-mounted mirrors. This group of drivers was generally positive about thesemirrors.

Figure 27. Ratings of the value of fender-mounted mirrors by the six subjects (10 is better).

3.4

Summary and Conclusions

The empirical results for driver visual performance are consistent with the crashdata described in Section 2. There appears to be converging evidence for a visibility
60

problem on the right side, toward the front of tractor-trailers. Preliminary results forthree different mirror systems are not conclusive, but suggest that the best visualperformance may result from an innovative system in which a fender-mounted convex isused to replace, rather than supplement, the currently standard window-mounted convex
mirror.

61

62

4.0

PRIORITIZATION OF ZONES FOR DRIVER VISION IMPROVEMENT

4.1

Zone Definition and Rationale

One objective of this study was to determine which aspects of driver vision most required
improvement. Based on the crash data results presented in Section 2, and the supporting
findings from the pilot study of driver performance presented in Section 3, a set of fourplan-view zones has been developed and prioritized. Figure 28 presents these zones,
which lie forward, rearward, and to the right of the truck.

Figure 28. Prioritized zones for driver vision improvement. The highest priority zone is indicated withnumeral 1.

Priority 1 (highest): The area immediately to the right of the cab, extending to thefar side of the adjacent lane and 5 m rearward from the front bumper.
The crash analysis presented in Section 2 shows that the area immediately to the right ofthe cab is the area of highest concern. This is the most common location for contact withanother vehicle during truck-initiated lane-change/merge crashes. This zone also
encompasses the most common initial (pre-maneuver) positions for non-motorists struck
during start-up and right-turn crashes. In the pilot study (Section 3), drivers took thelongest to detect lane-change conflicts in this area. The geometric analysis of direct andindirect fields of view for an exemplar cab showed that a small vehicle in this location
could be completely obstructed from direct view and not visible with door-mounted
mirrors. The driver’s only view of objects in this area was provided by the fendermounted convex mirror.
Priority 2 (second highest): The area immediately to the right of the truck,
extending to the far side of the adjacent lane, beginning 5 m from the front bumperand extending to a line 5-m rearward of the back of the truck (including trailer).
Right-going, truck-initiated lane-change/merge crashes are approximately 4.5 times more63

likely than left-going crashes. Even after accounting for the substantial percentage ofthese crashes in which the struck vehicle is in zone 1 immediately prior to the truckinitiated maneuver, the area adjacent to the right side of the truck and extending one carlength behind the back of the truck remains a high priority. Although vehicles that are
centered in the adjacent lane in this zone are usually partially or wholly visible in thedoor-mounted planar mirror, the door-mounted convex mirror is the only means mostdrivers have to view the entirety of the zone. The difference in response times between
the left and right sides in the area rearward of the cab (see section 3) suggests that the
combination of mirrors available on the right side is less effective than the mirrors on theleft side.
Priority 3 (third highest): The area immediately behind the truck.

The crashanalysis in Section 2 shows that backing crashes are the most common type of truckinitiated crash. The data used in this analysis were limited to crashes that occur on a
public motorway, so the actual number of backing crashes would probably beconsiderably higher if crashes that occurred on private property (parking lots, loading
docks, shipping terminals, etc.) were included. Backing crashes have recently beenaddressed by NHTSA in a Notice of Proposed Rulemaking that outlines changes to
FMVSS 111 that would require an indirect vision system covering the area immediately
behind certain straight trucks (NHTSA 2005). NHTSA estimated that about 13 on-road
fatalities each year are caused by backing crashes, whereas approximately 66 off-road
fatalities in backing crashes occur during the same time period. Because backing crashes
tend to occur at low speeds, the fatalities are mostly non-motorists.
Priority 4 (fourth highest): The area immediately in front and to the right-front of
the truck, extending through the truck’s lane across the right adjacent lane andextending 5 m forward of the front bumper. Start-up and right-turn crashes accountfor perhaps 40 to 60 on-road non-motorist fatalities in the U.S. each year. In most ofthese cases, the pedestrian or cyclist is in front of or to the right front of the cab
immediately prior to the event (i.e., in zones 1 or 4). Crashes into non-motorists initiallylocated in zone 4 are those most likely affected by direct vision obstructions, since few
U.S. trucks have mirrors covering this zone. Methods to assess visibility in zones 1 and
4 are the subject of section 5.

64

4.2

Discussion

As noted above, these zones are intended to prioritize improvements to driver vision
above the current de-facto standard, i.e., door-mounted planar and convex mirrors on
both sides of the cab. The lack of inclusion of a particular area (say, the left-front of thecab) in these zones should not be interpreted as implying either that the driver vision in
those areas would not benefit from improvement, nor that decrements in vision in thoseareas would be acceptable. For example, expanding the width of the direct-visionobstruction posed by the driver-side A-pillar would not be desirable, but the current
analysis is not able to estimate the safety effect of such a change.
The vision zones described above are defined only in plan view. Because the potential
crash partners in these areas typically have a vertical extent of a meter or more,suggesting that the primary plane of interest may not be the ground. Section 5 addresses
the issue of the relevant target height.
The vision zones described above span only areas relatively close to the vehicle. Somecrashes due to limitations of driver direct indirect vision involve partners who are outsideof the priority zones when the truck driver begins the maneuver, but the importance ofdriver vision in such crashes is difficult to quantify from crash data. Hence, the data do
not provide a good justification for making A-pillar obstructions (or direct-visionobstructions due to mirrors) a high priority, except as they affect visibility of targets
located within 5 m of the vehicle.
The vision zones prioritization implicitly addresses the relative importance ofimprovements in direct and indirect vision. The top three priority areas are areas withminimal or no direct visibility, meaning that improvements to driver vision in these areas
will come through improvements in indirect vision systems. Vision zone 4, directly infront of the truck, may be addressed on some vehicles through improvements in direct
vision (changes in the hood design, for example) but other trucks will require an indirect
vision component (e.g., a front cross-view mirror) to improve driver vision in this zone.
The highest priority zone directly to the right of the cab is already addressed on the
majority of trucks by a convex mirror mounted on the hood or fender near the front of thetruck. However, even with a mirror in this location, drivers’ reaction times with targets
in zone 1 are still longer than for targets in the equivalent location on the left side of thetruck. This suggests that vision improvement above that provided by the fender-mounted
convex mirror should be examined for this zone.
65

66

5.0
5.1

A METHOD FOR EVALUATING NEAR-CAB VISIBILITY

Introduction
The crash data analysis in Section 2 demonstrated that crashes due to limitationsof driver vision occur most frequently in the areas directly behind the vehicle, along theright side of the vehicle, particularly adjacent to the cab, and immediately forward of thecab. The experimental study reported in Section 3 demonstrated that the time requiredfor drivers to detect a lane-change conflict was greatest immediately to the right of thecab. Section 4 presented a set of zones, ordered by priority, in which improvements indriver vision could yield safety improvements.
Driver vision can be improved in a variety of ways. The preferred approachshould always be to provide direct vision of the area of interest. If direct vision is not
feasible, then a high-quality view of the area should be provided by an indirect visionsystem. The quality of the view afforded by an indirect vision system is difficult toquantify meaningfully, but the methods used in the pilot study provide a potential
performance-based approach. That is, the relative amount of time required to determine
the presence or absence of a conflict might be a useful measure of the relative quality ofthe view. In the pilot study, the target was a vehicle, but a pedestrian or pedal cyclist
might provide a more rigorous test of an indirect vision system, particularly for targets
relatively far rearward along the vehicle or close to the cab.
Two standardized methods are currently available for comparing the driver vision
provided by alternative cab designs. SAE J1750 presents two methods for generating
graphical depictions of the visual field by raytracing from an eye point, usually thecentroid of the J941 cyclopean eyellipse. The polar plot depicts vision obstructions dueto the cab, mirrors, and other vehicle structures in angle (azimuth, elevation) space. The
horizontal projection plot shows the visible and obstructed areas on a horizontal plane,
often the ground plane. Figure 29 shows a sample horizontal projection plot from J1750.

67

Figure 29. Sample horizontal projection plot from SAE J1750.

The J1750 visibility plots, and other graphical depictions of driver visibility, arevaluable for comparing among vehicles. However, the SAE Recommended Practices do
not provide guidance on the importance of improvements in the size of the visible field.
Intuitively, the importance of a particular increase or decrease in the field of view
depends on the area that is obstructed or revealed. A decrement in visibility in one area
may have no meaningful safety effect, whereas a decrement in another area may have an
important effect.
This report section presents a calculation procedure for evaluating of the relative
safety of alternative truck cab designs with respect to near-cab visibility. The method isbased on the following observations:•

The primary safety problem in the area affected by obscuration caused by thehood and cab greenhouse, i.e., the area directly in front and to the right of thetruck, is crashes with non-motorists (pedestrians and pedal-cyclists).

•

Most of the non-motorists struck by trucks are adults.

68•Start-up and right-turn crashes occur at low speeds.

A method for evaluating cab obstruction is therefore proposed that evaluates thepercentage of the standing adult pedestrian population that is visible at a range of target
points near the cab. The goal of the evaluation method is to provide a meaningful relative
measure of the direct and indirect fields of view in the area near the cab. An ideal
measure will be•scaled such that a score that is twice as high represents approximately twice asmuch risk;•

calculated in such a manner that credit is not given for additional visibility
beyond that required to address the problem; and•selected such that attention to the measure will tend to produce safer designs.

The method presented here evaluates the percentage of the adult population that would be
obscured from a representative driver eye point at a large number of sample points on the
ground plane near the cab. Summing obscured fractions across the sample points gives aquantitative measure of the vision obstruction posed by the cab.
5.2

Evaluation Method

1.

Obtain a computer model of the vehicle to be evaluated.
Although it would be technically feasible to perform these measurements andcalculations with an actual vehicle, the procedure is intended to be performed using
computer-aided design (CAD) software. A CAD model of the vehicle that includes all
relevant vision obstructions is needed. For the current purposes, that includes the hoodand the “greenhouse,” i.e., the outlines of the window openings on the cab. All mirrors
should be included, since they provide indirect vision and also obstruct direct vision.
Figure 30 shows the minimum information based on measurements of an example truck
cab. The CAD data must include an appropriate ground plane for the vehicle. Sincesome cabs are used in multiple configurations with different grounds, separate analyses
will need to be performed for each condition.

69

Figure 30. Computer model of vision obstructions for the example vehicle, including window perimeters
and the hood. The viewing point (representative driver eye point) is shown with a black dot.

2.

Establish a viewing point.
These calculations are performed from a single viewing point. Unlike somevision analyses intended to assess a worst case (e.g., the mirror viewing tasks for schoolbuses in FMVSS 111, which use a small-female eye location), the intent of the current
procedure can be readily fulfilled using an average eye location. The recommended
approach is to use the centroid of an appropriately positioned cyclopean driver eyellipse
for each vehicle, which is an estimate of the average driver eye location on the driverlateral centerline. The SAE J941 Class-B eyellipse (SAE 2005) or a newer eyellipse(Reed 2005) could be used.

To preserve the comparability of the analyses acrossvehicles, the same procedure should be used to locate the viewing point in each vehicle.
2.

Establish a grid of sample points.
Figure 31 shows the layout of the sample points. Beginning at the front bumperand vehicle centerline, a grid is constructed with 0.5 m pitch. The grid wraps around thesides of the vehicle to the back of the cab. The sample points are at the centers of the 0.5x-0.5-m squares defined by the grid. The grid is 10 m wide and 10 m long, with the70

center of the grid at the projection of the front bumper onto the ground at the lateral
centerline of the vehicle. The 10-m width was chosen to approximately span the width of
three lanes (3 x 3.7 m = 11.1 m). The fore-aft length is more arbitrary, but extends
approximately one car length in front of the bumper and rearward past the back of the cab
for most trucks. (See section 5.4 for more discussion of the choice of sampling points.)

Figure 31. Laying out sample points (plan view). Each intersection point on the grid is a sample point.

3.

Compute the minimum view height at each sample point.
This procedure determines the minimum height that is visible at each sample

point. Determine the point on a vertical line through the sampling point such that a linefrom the viewing point intersecting any lower point on the vertical line would alsointersect the cab. Figure 32 shows the calculation schematically.

71

Figure 32. Establishing the minimum view height (schematic in side view).

Figure 33 shows the results of calculating minimum view heights on the grid forthe example cab geometry. For the areas in front of the hood, the lines indicate theminimum height of an object that would be visible at the corresponding ground-planelocation. Sampling points at which a vertical line is completely obscured area shownwith red vertical lines.

The A-pillars create noteworthy three-dimensional effects.

Because the A-pillar slopes rearward in this geometry, there are sampling points within
the ground-plane A-pillar obscuration at which an object of a sufficient height is visible.
In the direction of the right A-pillar, sufficiently tall objects are visible across most of thesampling grid.

72

Figure 33. Illustration of direct view heights on sampling points. A vertical red line indicates that a verticalline through the sampling point is completely obscured. A black line indicates that the view is partiallyobscured and shows the minimum height that is visible. The obstruction perimeters on the ground plane areshown for the windows and windshield (red lines) and hood (cyan line).

4.

Compute the fraction of the pedestrian population visible at the view height.
The pedestrian population is modeled as a 50/50 male/female population having

the distribution given by the 1990 National Health and Nutrition Examination Survey,
known as NHANES III (NCHS 1994). Male stature is modeled as a normal distribution5
with mean 1755 mm and standard deviation 74.2 m. The female stature distribution hasmean 1618 mm and standard deviation 68.7 m. An adjustment of 25 mm is added toaccount for shoes, and a multiplier of 0.82 is used to estimate shoulder height fromstature. The result is the adult pedestrian viewing height distribution shown in Figure 34.
To simplify the calculations, the distribution is approximated using a linear function
between the 1st and 99th percentiles:

5

The mean and standard deviation have been selected to obtain a good fit in the tails of an approximating
normal distribution. The actual mean and standard deviation are somewhat different.

73

F(Z) =
Z < 1.19 m

1.19 m ≤ Z ≤ 1.54 m 
Z > 1.54 m
0(z – 1.19)/(1.19 – 1.54)
1[1]where Z is the viewed height and F(Z) is the fraction of the pedestrian population that is
obscured. Under the approximation, the adult pedestrian population is fully visible forviewing heights at or below 1.19 m, and fully obscured for viewing heights above
1.54 m.

Figure 34. Fraction of adult pedestrian population that is visible as a function ofview height from the ground.

Figure 35 shows the obscured fraction as a set of vertical lines at the sampling
points. A vertical line 1 m tall indicates that the population is fully obscured at that point.
No vertical line indicates that the population is fully visible (or that the sampling point isunder the vehicle, and hence not included).

74

Figure 35. Depiction of the fraction of the population that is obscured for direct vision for the example
vehicle geometry. A vertical red line (all are 1-m tall) indicates that the population is completely obscured
and a vertical black line indicates fractional obscuration (height is fractional obscuration times one meter).
The obscuration boundaries at the 1.2 m plane are shown. Pedestrian obscuration calculations were not
performed for mirrors.

Figure 35 shows that most of the obscuration on the sample grid is due to thegreenhouse rather than the hood for the example vehicle. In some circumstances, it might
be valuable to evaluate only the hood or the A-pillars. Figure 36 shows the results ofconsidering only obscuration due to the hood in the area forward of the bumper.

75

Figure 36. Fraction of the pedestrian population obscured by the hood. A vertical black line indicates
fractional obscuration (height is fractional obscuration times one meter). The obscuration boundaries at the
1.2 m plane for the hood (cyan) and greenhouse (red) are shown.

5.

Compute an Aggregate Obscuration Score
For comparing across cab designs, a single score may be desirable. One approachis to multiply the fractional pedestrian population obscurations by the plan-view area
represented by each sample point (0.25 m2 in the current example) and sum across the
sample points. A lower score is better, indicating less obscuration. Using this approach,the aggregate score for the analysis in Figure 36 (hood obscuration only) is 3.2 m2.
Lowering the front hood points by 0.2 m improves the score to 1.0 m2. Figure 37 shows
the analysis with the lowered hood points. In this case, the obscuration caused by thebottom of the windshield becomes more important on the right side of the cab than thehood obscuration.

76

Figure 37. Fraction of the pedestrian population obscured by the hood, using the example cab geometry
with front hood points lowered by 0.2 m. A vertical black line indicates fractional obscuration (height isfractional obscuration times one meter). The obscuration boundaries at the 1.2 m plane for the hood (cyan)
and greenhouse (red) are shown.

5.3

Discussion
This method provides a numerical evaluation of cab obscuration that is based on

detection of adult pedestrians near the cab. This method is designed specifically toaddress start-up and right-turn crashes with nonmotorists. As shown in Section 3, thenonmotorist involved these types of fatal crashes is usually directly in front of or to theright-front of the truck at the time the truck driver begins the maneuver.

Hence,

improving driver vision in these areas may reduce the incidence of these crashes.
Some parameters of the method have been set somewhat arbitrarily. For example,
the 0.5-m pitch on the sampling grid corresponds roughly to the plan-view volume
occupied by a standing pedestrian, but a finer grid would produce smoother measures ofthe effects of changes in cab geometry and would reduce the likelihood of missing theeffect of a potentially important geometric feature. Of course, sampling at more points
increases the effort required to perform an assessment. The overall grid size (10 m x 10m) is also somewhat arbitrary. A larger grid would provide a better gage of pillar

77obscuration, and a grid that extended further to the rear might be valuable for assessing
mirror fields of view.

Regardless of the sampling points used, comparable values canonly be obtained across vehicles designs if the same grid is used.
The practicality of the procedure depends substantially on the time required toevaluate the view height at a plan view location. The sample calculations used in thisreport were performed in Mathematica using highly abstract vehicle geometry, and thecalculations could be performed essentially instantaneously.

However, for use withexisting CAD geometry it may be more feasible to program the calculations as CAD
macros. For most analyses, it would not be necessary to evaluate the view height at all of
the sample points in the grid. For example, the results in Figure 36 could be obtained byevaluating only 40 sample points.
The proposed procedure is somewhat analogous to computing the obscuration
area on a plane 1.2 m above the ground plane. The area within the 1.2-m obscuration
boundary will be roughly proportional to the score obtained by the proposed procedure.
For complex cab geometry, computing the projected area may be more difficult thancomputing the view height at sampling points on a grid. Further investigation of therelative merits of the two approaches should be conducted.
The example calculations focused exclusively on direct vision, but indirect visionassessments could be made using the same method. In this case, the three-dimensional
mirror fields of view would be included in the calculations of visible area. Viewing an
area with a mirror is less desirable than a direct view of the area, and hence it would bedesirable to “penalize” mirrors when comparing designs that combine direct and indirectview of an area. The appropriate penalty should be related to the performance of peopleusing the mirror system to detect targets of interest, e.g., vehicles and nonmotorists.
Intuitively, performance might be expected to be a function of the curvature of the mirror(convex mirrors providing worse performance than planar mirrors) and the distance fromthe driver to the mirror.

Other factors, such as the size of the mirror and weather

conditions, might also be important.
The experimental study presented in Section 3 provides one approach tocomparing detection performance with direct view and convex mirrors.

78

For example,

the drivers took approximately twice as long to detect a vehicle directly to the right of thecab using a convex mirror as they did on the left side of the vehicle using predominantly
direct vision. This suggests that, as a starting point, vision near the cab using a convex
mirror should be discounted 50% relative to direct vision. So, if the entire pedestrian
population were obscured for direct vision at a particular sample point, but fully visibleusing a convex mirror, the score would be 0.5. Further experimental work could quantify
the relative value of direct and indirect vision (particularly using convex mirrors) for
detecting pedestrians using the reaction-time measure.
The proposed method does not take into account the effects of head movements or
binocular vision, both of which can alter the effective obscuration, particularly for theleft-side A-pillar and mirror. However, since the goal is to produce a relative measurethat is useful across vehicles, a single representative viewing point is probably sufficient.
To take into account the effects of binocular vision and head movement, the analysis
could be performed for each sample point using two eye locations, with the better value
used for the aggregate calculation. For example, if the pedestrian population would becompletely obscured at a sample point from the right eyellipse centroid, but visible fromthe left eyellipse centroid, the population could be considered to be completely visible atthat point.
The method could be extended to cover all areas around the vehicle, but themeaning of the analysis would be strongly dependent on the appropriateness of thepenalty used for indirect vision systems. Anecdotally, a convex mirror has a “sweet spot”
corresponding to a distance from the mirror at which a pedestrian is most readily
detected. If the pedestrian is closer, the distortion creates problems with identification. If
the pedestrian is further from the mirror, the distortion and minification may make thepedestrian difficult to detect.

An appropriate performance-based scoring system isneeded that would appropriately account for the differences in quality of field of view fordifferent indirect vision systems. This will be particular important for evaluating thepotential benefits of camera based systems over mirrors and for comparing alternative
camera locations, display characteristics, etc.

As noted above, the reaction-timeparadigm, using direct-vision or planar-mirror detection time as a control, may be a good
metric for establishing the relative quality of view provided by different systems.
79

80

6.0
6.1

SUMMARY AND CONCLUSIONS

Primary Findings
About 20 percent of crashes initiated by trucks occur in configurations in which

limitations to truck driver vision may have been an important factor contributing to thecrash. Right-going lane changes and turns account for more than half of these crashes.
In contrast, lane changes and turns to the left account for only about 12 percent. Data on
crash contact points also show more frequent contacts on the right side of the truck thanon the left. The locations of contact points in lane-change/merge scenarios show a strong
bias toward the front of the vehicle (adjacent to the cab) for right-going crashes, whereas
left-going crashes are more evenly distributed. On average, right-going truck-initiatedcrashes are about 4.5 times more likely than left-going crashes.
Using an in-depth analysis of police accident reports (PARs), the pre-crashpositions of the crash partners were determined for a subset of potentially vision-related
crashes.

The analysis further implicates vision along the right side of the truck, andparticularly directly adjacent to the cab, in lane-change/merge crashes into other vehicles.
Data from the PAR analysis showed that nonmotorists killed in start up and right turncrashes were usually in front of or to the right of the cab immediately prior to the crash.
Nonmotorists killed in start up and right turn crashes with trucks were nearly all adults
and tend to be older than pedestrians struck in other crash modes. Over half ofpedestrians involved in start up crashes are over age 65.
The experimental study presented in Section 3 showed that driver performance indetecting lane-change conflicts was directionally consistent with the findings from thecrash data. Drivers took longer to detect conflicts on the right side of the vehicle than on
the left. The longest reaction times were observed when the target vehicle was directly tothe right of the cab, suggesting that detecting a conflict in this area is most difficult fordrivers. Drivers also made more errors on the right side of the vehicle, including several
failures to detect a vehicle directly to the right of the cab.
Based on these findings, a prioritized set of vision zones was developed (Section
4). The highest priority for improvements to driver vision is the area directly to the right81

of the truck cab. This area represents the most likely position of a crash partner at thetruck driver’s decision point in right lane-change crashes and is also the pre-crashposition of many nonmotorists involved in right turn and start up crashes.

The entire

area on the right side of the truck, and the area to the rear of the truck, also meritadditional attention, based on the relatively large percentage of crashes in which truck
drivers fail to detect conflicts in these areas. An additional priority zone was establisheddirectly in front of the vehicle to address start up crashes involving nonmotorists.
Although these crashes occur at low speeds, the vulnerability of the nonmotorists makesthese crashes particularly lethal.
6.2

Evaluating Direct and Indirect Exterior Vision from Truck Cabs
Section 5 presents a new approach to evaluating exterior vision from truck cabs.

The method differs from previous approaches, e.g., SAE J1750, by providing an
aggregate score that is related to a specific crash-safety issue. The method is based on
the visibility of standing adult pedestrians, and hence addresses the specific problem ofpedestrian involvement in start up and right turn crashes. Quantitative comparisons
between vehicle designs with respect to exterior vision can be conducted using the new
method.
The experimental paradigm presented in Section 3 also represents a promising
approach to evaluating the quality of exterior vision provided by alternative visionsystems. The time drivers require to determine if a conflict exists provides a sensitive
measure of the difficulty of the task.

The parallels between the findings of theexperimental study and the crash data analysis support the validity of the experimental
approach. This method could be applied to evaluate alternative mirror systems, camerabased systems, and other technologies that might be developed to address the priorities
established in this report.
6.3

Future Research
Given the scope of the problem documented by the crash data analysis,

considerably more work is needed to address truck driver vision. Additional analysis ofcrash data would help to quantify the problem more completely and with greater
precision. A relatively small effort would be required to double or triple the number of82

PARs analyzed to address the pre-crash configurations in LCM, right turn, and start up
crashes. Adding more data to these analyses would narrow the confidence bounds andprovide a more complete picture of the vision improvement needs near the cab.
Data from the Large Truck Crash Causation Study (LTCCS) have become
available this year.

The LTCCS dataset includes highly detailed information on anationally representative sample of 963 crashes involving heavy trucks. The data aremuch richer than those in GES or TIFA, and include considerably more detail than isfound in typical PARs. Although the total number of cases is small, these data are
expected to be valuable for addressing vision-related questions. For example, the LTCCS
contains specific information on mirror configurations that is lacking from other datasets.
In addition, each crash investigation is documented with numerous photographs,
including the interior of the cab from the driver’s point of view. A study of vision-related
factors in the LTCCS should be conducted to complement the analyses presented in thecurrent report.
The experimental investigation reported in Section 3 used only six subjects but
nonetheless yielded some valuable results. More experimental work should be conductedalong similar lines with the dual objectives of (1) developing a robust experimental
methodology for quantifying the quality of the view provided by an indirect visionsystem, and (2) evaluating the performance of alternative indirect vision systems that
may help to address the substantial driver vision issues documented in this report.
Elements that should be included in future studies include:•

alternative targets, including motorcyclists, pedestrians, and pedalcyclists; and•alternative indirect vision systems, including various combinations of thecurrent planar and convex mirrors, aspheric convex mirrors, and camera-based
systems.

Older drivers will be an important cohort in future studies because of cognitive andperceptual differences with younger drivers and because they represent an increasing
percentage of truck drivers.
The parking-lot experimental approach also should be validated in a dynamic
setting. One naturalistic approach would be to examine the time that drivers use to make83

right-going and left-going lane-change judgments in traffic. Consistency between theright/left ratios on-road and in the static situation would support the validity of the static
approach.
On-road data collection would also be valuable to understand more completely
how drivers use their mirrors. Many trucks are currently equipped with five, six, or more
mirrors.

How do drivers use these mirrors, many of which have partially redundant

fields of view? It is possible that having fewer but more-optimal mirrors would provide
improved performance by reducing scanning demands.

In-vehicle studies withalternative mirror configurations could address this issue.
Experimental studies should also be conducted to address the detection ofpedestrians with direct vision. The analysis method presented in Section 5 assumes thatthe pedestrian must be fully visible above the shoulders to be seen by the driver. A
reaction-time approach to detection might be a useful way to quantify the amount of apedestrian that must be visible to provide reliable detection. As with the other conflictdetection studies, consideration of background contrast and target position will be
important.
The CAD-based vision evaluation method presented in Section 5 should beapplied to a number of truck cabs to determine its feasibility and utility. Determining
whether the calculations are best performed in CAD software or in an external program
will be an important first step. More research is needed to establish an appropriate
method for balancing A-pillar and hood obstruction. The current method, based solely
on pedestrian obscuration, does not take into account the distance from the truck. Any
pedestrian obscuration directly in front of the hood is problematic, but pedestrians
obstructed by the A-pillar are not directly in danger. Any relative motion of the truck andpedestrian that increased the likelihood of conflict may also increase the likelihood thatthe pedestrian will become visible to the truck driver. Further analytical studies of truck
motions in right-turn scenarios may provide a means to quantify the relative importanceof hood and A-pillar obstructions for pedestrian protection. Combined with further
analysis of crash data, these analytic studies might yield an appropriate metric foraddressing the contribution of A-pillar and mirror obscuration to crashes with othervehicles.
84

7.0

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