Van Winsum

8/12/2019 Van Winsum

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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

Van Winsum

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