Measuring Event Based Driver Performance: implications for driving simulator scenarios TRB workshop: Standardized Descriptions of Driving Simulator Scenarios

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Measuring Event Based Driver Performance
implications for driving simulator scenariosTRB
workshop Standardized Descriptions of Driving
Simulator Scenarios
Wim van Winsum www.stsoftware.nl Tel 31 50
5778768 Fax [phone] info_at_stsoftware.nl
Washington D.C., January 9, 2005
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Overview of the presentation

• 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 Performance measures
• 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
• Creation of logical and graphical databases by a
  common source
• Illustration of TLC measurements with the
  platform software

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PART 1 Measures of Event Based Driver
Performance
1 Event Based Driver Performance measures are
usually measures that reflect the
time relation between the vehicle and an object
in the surroundings of the vehicle2 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 vehicle4 Examples are then
TLC (time-to-line crossing), TTI
(time-to- intersection) and TTC
(time-to-collision)
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Driver actions to perceived time relations

• 3 Time relations between the vehicle and other
  objects are used as safety margins by the driver

• 1 Drivers are assumed to perceive these time
  relations and use these to control their
  behaviour. Examples of these behavioural
  responses are steering corrections, braking,
  changing vehicle speed.

TTO (time-to-object)
2 These responses result in altered time
relations Drivers try to control these time
relations. The time relations are then both input
to, and output of driver actions. In that sense
time relations are measures of driver performance.
Behavioural response
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Driver are controlling and maintaining safety
margins

• Event based driver performance variables measure
  how drivers control safety margins.
• Another example of an important driver
  performance variable that reflects a safety
  margin is Time Headway (THW), although it is not
  event based.
• Measures of how drivers control their safety
  margins are then important performance variables
  that must be measured in driving simulators.
• In practice, however, driving simulators are
  often unable to provide adequate measurements of
  these important variables

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Example what is required to measure TLC in a
curve ?

• 1) TLC DLC/velocity
• 2) DLC a Rv
• Rv radius of the vehicle path (u/yawrate)
• In order to compute a, you need to know the
  coördinate points Xv, Yv and Xr, Yr as well
  as
• Rr radius of the road (distance between
  centerpoint Xr, Yr of road curve and inner lane
  boundary)
• This requires an accurate and highly detailed
  logical (mathematical) representation of the
  roadnet together with an accurate vehicle
  dynamics model

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How is TLC in a curve often measured in practice?

• 1 Because a logical representation of the road
  database is unavailable in most simulators, an
  approximation of TLC is often used that wrongly
  assumes that the vehicle will maintain the same
  lateral velocity TLC_1 (lateral
  distance)/(lateral velocity).
• This approximation gives very different results
  compared to the real TLC
• In addition, lateral distance often is computed
  with respect to the polygon edges of the
  graphical database. In the graphical database,
  road curves are often simulated as a sequence of
  straight edges that connect with a small angle.
  This results in sharp spikes in the TLC_1 signal
  that can only be removed after filtering
• Because of these factors, TLC measurements in
  driving simulators are often unreliable

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Implications for driving simulator scenarios

• 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

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PART2 Research simulator platform in the
Netherlands

• 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 same experimental scenarios can be
  played on different simulators, ranging from
  low-end to high-end
• Standardization of scenario- and database formats
• Exchange of graphical databases and scenarios
• 4 Development of tools that allow researchers to
  build databases and experimental scenarios by
  themselves

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A few design considerations

• Logical- and graphical databases must originate
  from a common source StRoadDesign database
  designer. This ensures that both types of
  databases match geometrically
• Standardization in database formats and
  rendering OpenFlighttm and OpenSceneGraph (OSG)
• All internal variables in the simulator software
  are accessible to the researcher via a scripting
  language
• Everything in the simulations is controlled by
  scripts from traffic generation to datastorage
  and feedback generation
• Complexity is reduced by using autonomous agents
  and by letting each scenario script control
  itself (switch on or off as a result of a dynamic
  condition)
• Re-use of scripts

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Graphical and logical databases generated by one
program
StRoadDesign road designer
OpenFlighttm database
Logical database
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Autonomous agents drive in a logical database

• Autonomous agents (vehicles, bicyclists,
  pedestrians) scan the immediate environment in
  the logical database
• Based on what they perceive, they apply a number
  of behavioural rules
• And perform an action that changes speed and
  lateral position
• And update their position in the logical database

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ExampleTLC measured by the platform software

1. Time histories of the following data are shown
   steering-wheel angle, yawrate, real TLC, lateral
   position, lateral velocity and approximated TLC1.
   To leftpositive. To rightnegative.
2. The real TLC (3th row) covaries with
   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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Conclusions

• Event Based Driver Performance measures (or
  safety margins) are among the most important
  dependent variables in driver behaviour research
• 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.
• An added advantage of a logical database is that
  autonomous agents (vehicles, bicyclists,
  pedestrians) can travers the road network by
  references to this database
• The logical- and the graphical database must
  originate from the same source, in order to
  ensure that logical and graphical positions of
  objects match, which is a core property of our
  design tool
• The collective use of the same road networks and
  driver performance measures by research
  institutes will enable comparability of results
  and exchange of scenarios