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Measuring Event Based Driver Performance:
implications for driving simulator scenarios
TRB workshop: Standardized Descriptions of
Driving Simulator Scenarios
Wim van Winsum
www.stsoftware.nl
Tel: +31 50 5778768
Fax: +31 50 5775835
[email protected] Washington D.C., January 9, 2005
http://www.stsoftware.nl/http://www.stsoftware.nl/ -
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PART 1: Statement of the problem
1 What are Event Based Driver Performance measures
2 Why are they among the most important measures of driver performance
3 Time-to-line crossing (TLC) is discussed as an example, but the same
arguments also applies to other Event Based Driver Performancemeasures
4 It is concluded that a detailed geometrical road-network representation is
a prerequisite for measuring Event Based Driver Performance measures
PART 2: Dutch research simulator platform as an illustration
5) Creation of logical and graphical databases by a common source
6) Illustration of TLC measurements with the platform software
Overview of the presentation
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PART 1: Measures of Event Based Driver Performance
1 Event Based Driver Performance measures are usually measures thatreflect the time relation between the vehicle and an object in the
surroundings of the vehicle
2 The time relation usually exists of a prediction of the time it takes
before the object is crossed, reached or collided with
3 The reference object may be an edge line of the current driving lane,
the start of an intersection plane or the rear bumper of another vehicle
4 Examples are then TLC (time-to-line crossing), TTI (time-to-
intersection) and TTC (time-to-collision)
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3 Time relations between thevehicle and other objects are used
as safety margins by the driver
1 Drivers are assumed to perceivethese time relations and use these
to control their behaviour.
Examples of these behavioural
responses are: steering
corrections, braking, changing
vehicle speed.
Driver actions to perceived time relations
TTO(time-to-
object)
Behavioural
response
2 These responses result in altered
time relations: Drivers try to
control these time relations. The
time relations are then both inputto, and output of driver actions. In
that sense time relations are
measures of driver performance.
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1) TLC = DLC/velocity
2) DLC = * RvRv= radius of the vehicle path(u/yawrate)
In order to compute , you need to knowthe cordinate points [Xv, Yv] and [Xr, Yr]as well as
Rr= radius of the road (distance betweencenterpoint [Xr, Yr] of road curve andinner lane boundary)
This requires an accurate and highlydetailed logical (mathematical)representation of the roadnet togetherwith an accurate vehicle dynamics model
Example: what is required to measure TLC in a curve ?
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1 Because a logical representation of the road database is unavailable inmost simulators, an approximation of TLC is often used that wronglyassumes that the vehicle will maintain the same lateral velocity: TLC_1 =(lateral distance)/(lateral velocity).
2 This approximation gives very different results compared to the real TLC
3 In addition, lateral distance often is computed with respect to the polygonedges of the graphical database. In the graphical database, road curvesare often simulated as a sequence of straight edges that connect with asmall angle. This results in sharp spikes in the TLC_1 signal that can onlybe removed after filtering
4 Because of these factors, TLC measurements in driving simulators areoften unreliable
How is TLC in a curve often measured in practice?
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To compute the time-relations between the vehicle and other objects a
few things are required of driving simulator scenarios:
1 accurate path prediction of the vehicle (knowledge of the dynamics of the
vehicle)
2 accurate representation of the surroundings of the vehicle (knowledge of
the immediate environment) : distance to the object along the vehicle
path, dimensions and angles of the object, relevant properties of the
object, like radius, position or velocity
Not all simulators meet these requirements.
But if these requirements are met, then variables can be measured in a
simulator that are hard or even impossible to measure on the road
Implications for driving simulator scenarios
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We have established a research driving simulator platform with Dutch
universities (RU Groningen, TU Delft and TU Twente), traffic research
institutes (TNO Soesterberg, SWOV) and a neuropsychological clinic
(University hospital Groningen) with the following goals:
1 Common use of the same driving simulator software: the sameexperimental scenarios can be played on different simulators, ranging
from low-end to high-end
2 Standardization of scenario- and database formats
3 Exchange of graphical databases and scenarios
4 Development of tools that allow researchers to build databases andexperimental scenarios by themselves
PART2: Research simulator platform in the Netherlands
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1) Logical- and graphical databases must originate from a common source:
StRoadDesign database designer. This ensures that both types of
databases match geometrically
2) Standardization in database formats and rendering: OpenFlighttmand
OpenSceneGraph (OSG)
3) All internal variables in the simulator software are accessible to theresearcher via a scripting language
4) Everything in the simulations is controlled by scripts: from traffic
generation to datastorage and feedback generation
5) Complexity is reduced by using autonomous agents and by letting each
scenario script control itself (switch on or off as a result of a dynamiccondition)
6) Re-use of scripts
A few design considerations
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Graphical and logical databases generated by one program
StRoadDesign road designer OpenFlighttmdatabase
Logical database
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1 Autonomous agents (vehicles,
bicyclists, pedestrians) scan
the immediate environment in
the logical database
2 Based on what they perceive,
they apply a number ofbehavioural rules
3 And perform an action that
changes speed and lateral
position
4 And update their position in the
logical database
Autonomous agents drive in a logical database
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Example:TLC measured by the platform software
1) Time histories of the followingdata are shown: steering-wheel
angle, yawrate, real TLC, lateral
position, lateral velocity and
approximated TLC1. To
left=positive. To right=negative.
2) The real TLC (3th row) covarieswith steering-wheel angle (1st
row) and yawrate (3rd row): a
steering correction is made
when TLC reaches a minimum
to left (positive) or right
(negative)
3) The approximated TLC_1
covaries with lateral velocity and
has very different properties
compared to the real TLC
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1) Event Based Driver Performance measures (or safety margins) areamong the most important dependent variables in driver behaviour
research
2) Measuring these variables requires an accurate and detailed geometrical
description of the road geometry (logical database) and a vehicle
dynamics model of sufficient quality. Distances to other (road) objects are
then computed along the projected road path.
3) An added advantage of a logical database is that autonomous agents
(vehicles, bicyclists, pedestrians) can travers the road network by
references to this database
4) The logical- and the graphical database must originate from the same
source, in order to ensure that logical and graphical positions of objectsmatch, which is a core property of our design tool
5) The collective use of the same road networks and driver performance
measures by research institutes will enable comparability of results and
exchange of scenarios
Conclusions