• Driver State

Overview#

The driver state provides a generic mechanism to induce imperfection into car-following and lane change models. Although errors may enter the driving process at many stages [see Figure 1], SUMO only applies errors at the perception stage, see below for details.

Figure 1: Errors in the driving process.

Practically, errors are added to the input quantities of the car-following model's input parameters of spacing and speed difference. For an integration in contributed car-following models, the implementation in the standard model can be adopted (see MSCFModel_Krauss::stopSpeed() and MSCFModel_Krauss::followSpeed()). Currently (SUMO 1.8.0) it is only implemented for the standard Krauss and IDM.

Equipping a Vehicle with a Driver State#

To apply the imperfect driving functionality for a vehicle it is equipped with a Driver State Device, see the description of equipment procedures (and use =driverstate). The minimal definition required to equip one vehicle with a Driver State has the following form:

<routes>
    ...
    <vehicle id="v0" route="route0" depart="0">
        <param key="has.driverstate.device" value="true"/>
    </vehicle>
    ....
</routes>

In this case all parameters (see below) of the driver state are set to their default values. The following table gives the full list of possible parameters for the Driver State Device. Each of these parameters must be specified as a child element of the form <param key=<PARAMETER NAME> value=<PARAMETER VALUE> of the appropriate demand definition element (e.g. <vehicle ... />, <vType ... />, or <flow ... />). See Modeling of Perception Errors for details of the error dynamics.

Supported carFollowModels#

The following models support the driverstate device:

• Krauss
• IDM
• CACC

Modeling of Perception Errors#

An underlying Ornstein-Uhlenbeck process drives the errors which are applied to the inputs of the car-following model perception. The characteristic time scale and driving noise intensity of the process are determined by the driver state awareness, which is meant to function as an interface between the traffic situation and the driver state dynamics. We have

• errorTimeScale = errorTimeScaleCoefficient*awareness(t)
• errorNoiseIntensity = errorNoiseIntensityCoefficient*(1.-awareness(t))

The error's state error(t) at time t is scaled and added to input parameters of the car-following model as follows

• perceivedSpeedDifference = trueSpeedDifference + speedDifferenceError(t), where speedDifferenceError(t) = speedDifferenceErrorCoefficient*headway(t)*error(t)
• perceivedHeadway = trueHeadway + headwayError(t), where headwayError(t) = headwayErrorCoefficient*headway(t)*error(t)

Note that the state error(t) of the error process is not directly scaled with the awareness, which only controls the errors indirectly by affecting the processes parameters. Further, the scale of the perception error is assumed to grow linearly with the distance to the perceived object.

Finally, the driver state induces an update of the input to the car-following model only if the perceived values have changed to a sufficient degree. The conditions for updating the car-following input are:

• headway: |perceivedHeadway - expectedHeadway| > headwayChangePerceptionThreshold*trueGap*(1.0-awareness)
• speed difference: |perceivedSpeedDifference - expectedSpeedDifference| > speedDifferenceChangePerceptionThreshold*trueGap*(1.0-awareness)

Here, the expected quantities are

• expectedHeadway = lastRecognizedHeadway - expectedSpeedDifference*elapsedTimeSinceLastRecognition
• expectedSpeedDifference = lastRecognizedSpeedDifference