Behaviors

DriverModel{DriveAction}

A DriverModel represents a specific driving behavior. It specifies the action taken by the agent at a given scene. The ation will be of type DriveAction. It can be interpreted as a distribution, the likelihood of taking a certain action in a given scene. The DriverModel type is an abstract type! Custom driver models should inherit from it.

action_type(::DriverModel{A}) where {A}

returns the type of the actions that are sampled from the model

set_desired_speed!(model::DriverModel, v_des::Float64)

Sets a desired speed. This method is relevant for models like IDM where the vehicle tracks a nominal speed.

reset_hidden_state!(model::DriverModel)

Resets the hidden states of the model.

reset_hidden_states!(models::Dict{I,M}) where {M<:DriverModel}

reset hidden states of all driver models in models

observe!(model::DriverModel, scene, roadway, egoid)

Observes the scene and updates the model states accordingly.

rand(model::DriverModel)
rand(rng::AbstractRNG, model::DriverModel)

Samples an action from the model.

IntelligentDriverModel <: LaneFollowingDriver

The Intelligent Driver Model. A rule based driving model that is governed by parameter settings. The output is an longitudinal acceleration.

Here, we have extended IDM to the errorable IDM. If a standard deviation parameter is specified, then the output is a longitudinal acceleration sampled from a normal distribution around the non-errorable IDM output.

Fields

• a::Float64 = NaN the predicted acceleration i.e. the output of the model
• σ::Float64 = NaN allows errorable IDM, optional stdev on top of the model, set to zero or NaN for deterministic behavior
• k_spd::Float64 = 1.0 proportional constant for speed tracking when in freeflow [s⁻¹]
• δ::Float64 = 4.0 acceleration exponent
• T::Float64 = 1.5 desired time headway [s]
• v_des::Float64 = 29.0 desired speed [m/s]
• s_min::Float64 = 5.0 minimum acceptable gap [m]
• a_max::Float64 = 3.0 maximum acceleration ability [m/s²]
• d_cmf::Float64 = 2.0 comfortable deceleration m/s²
• d_max::Float64 = 9.0 maximum deceleration m/s²

Tim2DDriver

Driver that combines longitudinal driver and lateral driver into one model.

Constructors

Tim2DDriver(;kwargs...) The keywords argument are the fields described below.

Fields

• mlon::LaneFollowingDriver = IntelligentDriverModel() Longitudinal driving model
• mlat::LateralDriverModel = ProportionalLaneTracker() Lateral driving model
• mlane::LaneChangeModel =TimLaneChanger Lane change model

PrincetonDriver <: LaneFollowingDriver

A lane following driver model that controls longitudinal speed by following a front car.

Fields

• a::Float64
• σ::Float64 = NaN optional stdev on top of the model, set to zero or NaN for deterministic behavior
• k::Float64 = 1.0 proportional constant for speed tracking [s⁻¹]
• v_des::Float64 = 29.0 desired speed [m/s]

SidewalkPedestrianModel

Walks along the sidewalk until approaching the crosswalk. Waits for the cars to pass, then crosses.

Fields

• timestep::Float64
• phase::Int = APPROACHING
• ttc_threshold::Float64 = clamp(rand(Normal(4.0, 2.5)), 1.0, Inf)
• crosswalk::Lane = Lane()
• sw_origin::Lane = Lane()
• sw_dest::Lane = Lane()
• a::PedestrianLatLonAccel = PedestrianLatLonAccel(0.0, 0.0, sw_origin) makes you turn, left/right
• σ::Float64 = NaN optional stdev on top of the model, set to zero or NaN for deterministic
• v_des_approach::Float64 = clamp(rand(Normal(1.28, 0.18)), 0.0, Inf) Based on Feliciani et al. results
• v_des_appraise::Float64 = clamp(rand(Normal(0.94, 0.21)), 0.0, Inf)
• v_des_cross::Float64 = clamp(rand(Normal(1.35, 0.18)), 0.0, Inf)
• ped_accel::Float64 = 0.30
• ped_decel::Float64 = -0.50

StaticDriver{A,P<:ContinuousMultivariateDistribution} <: DriverModel{A}

A driver model where actions are always sampled by the same distribution specified by the field distribution.

Fields

• distribution::P

MOBIL

See Treiber & Kesting, 'Modeling Lane-Changing Decisions with MOBIL'

Constructor

MOBIL(;kwargs...) The keyword arguments are the fields highlighted below.

Fields

• dir::Int = DIR_MIDDLE
• mlon::LaneFollowingDriver=IntelligentDriverModel()
• safe_decel::Float64=2.0
• politeness::Float64=0.35
• advantage_threshold::Float64=0.1

TimLaneChanger

A simple lane change behavior that changes lanes whenever the lead car is going slower than our desired speed. Lane changes are made when there is an available lane, fore/rear gaps exceed our thresholds, we are faster than a rear vehicle in the target lane, and any lead vehicle in the target lane is faster than we can currently go.

Has not been published anywhere, so first use in a paper would have to describe this. See MOBIL if you want a lane changer you can cite.

Constructors

TimLaneChanger(v_des::Float64=29.0, threshold_fore::Float64 = 50.0,threshold_lane_change_gap_fore::Float64 = 10.0, threshold_lane_change_gap_rear::Float64 = 10.0,dir::Int=DIR_MIDDLE)

Fields

• dir::Int = DIR_MIDDLE the desired lane to go to eg: left,middle (i.e. stay in same lane) or right
• v_des::Float64 = 29.0 desired velocity
• threshold_fore::Float64 = 50.0 Distance from lead vehicle
• threshold_lane_change_gap_fore::Float64 = 10.0 Space in front
• threshold_lane_change_gap_rear::Float64 = 10.0 Space rear

ProportionalLaneTracker

A controller that executes the lane change decision made by the lane change models

Constructors

ProportionalLaneTracker(;σ::Float64 = NaN,kp::Float64 = 3.0,kd::Float64 = 2.0)

Fields

• a::Float64 = NaN predicted acceleration
• σ::Float64 = NaN optional stdev on top of the model, set to zero or NaN for deterministic behavior
• kp::Float64 = 3.0 proportional constant for lane tracking
• kd::Float64 = 2.0 derivative constant for lane tracking

ProportionalSpeedTracker <: LaneFollowingDriver

Longitudinal proportional speed control.

Fields

• a::Float64 = NaN predicted acceleration
• σ::Float64 = NaN optional stdev on top of the model, set to zero or NaN for deterministic behavior
• k::Float64 = 1.0 proportional constant for speed tracking [s⁻¹]
• v_des::Float64 = 29.0 desired speed [m/s]