Driving on a Mobius Strip

AutomotiveDrivingModels is templated to efficiently run different types of simulations. Entities are parameterized by their:

S state, which changes over time
D defintion, which does not change over time
I id, typically an Int, which uniquely identifies that entity.

In addition to these types, the actions, environment and the driver models can also be parameterized.

This notebook demonstrates a longitudinal driving simulation where cars drive on a single straight lane that loops back on itself. The types are:

S - State1D, containing the position and speed
D - VehicleDef, containing length, width, and class
I - Int

We use a StraightRoadway as our environment and LaneFollowingDrivers that produce LaneFollowingAccels.



In [1]:

    
using AutomotiveDrivingModels
using AutoViz









    



┌ Info: Recompiling stale cache file C:\Users\Maxime\.julia\compiled\v1.0\AutoViz\w0rHu.ji for AutoViz [82aa6e0c-a491-5edf-8d4b-c16b98e4ea17]
└ @ Base loading.jl:1184
┌ Warning: Package AutoViz does not have AutomotiveDrivingModels in its dependencies:
│ - If you have AutoViz checked out for development and have
│   added AutomotiveDrivingModels as a dependency but haven't updated your primary
│   environment's manifest file, try `Pkg.resolve()`.
│ - Otherwise you may need to report an issue with AutoViz
└ Loading AutomotiveDrivingModels into AutoViz from project dependency, future warnings for AutoViz are suppressed.



In [2]:

    
road_length = 200.0 # [meters]
roadway = StraightRoadway(road_length)

scene = Scene1D()
push!(scene, Entity(State1D(10.0,  8.0), VehicleDef(), 1))
push!(scene, Entity(State1D(50.0, 12.5), VehicleDef(), 2))
push!(scene, Entity(State1D(150.0, 6.0), VehicleDef(), 3))

cam = StaticCamera(VecE2(100.0,0.0), 4.75)
render(scene, roadway, cam=cam, canvas_height=100)









    Out[2]:

We can add an overlay that displays the car id for convenience:



In [3]:

    
overlays = [TextOverlay(text=["$(veh.id)"], incameraframe=true, pos=VecE2(veh.state.s-0.7, 3)) for veh in scene]
render(scene, roadway, overlays, cam=cam, canvas_height=100)









    Out[3]:

Or we can create a new SceneOverlay which does this for us:



In [4]:

    
using Parameters
@with_kw struct CarIDOverlay <: SceneOverlay
    textparams::TextParams=TextParams()
end
function AutoViz.render!(rendermodel::RenderModel, overlay::CarIDOverlay, scene::Scene1D, roadway::StraightRoadway)
    for veh in scene
        x = veh.state.s - 0.7
        y = 3.0
        text = string(veh.id)
        add_instruction!(rendermodel, render_text, (text, x, y, overlay.textparams.size, overlay.textparams.color), incameraframe=true)
    end
    return rendermodel
end

render(scene, roadway, [CarIDOverlay()], cam=cam, canvas_height=100)









    Out[4]:

To run a simulation we need driving models that produce actions. For this we will use LaneFollowingDrivers that produce LaneFollowingAccels. We will give each car a different model, as a demonstration.



In [5]:

    
models = Dict{Int, LaneFollowingDriver}()
models[1] = StaticLaneFollowingDriver(0.0) # always produce zero acceleration
models[2] = IntelligentDriverModel(v_des=12.0) # default IDM with a desired speed of 12 m/s
models[3] = PrincetonDriver(v_des = 10.0) # default Princeton driver with a desired speed of 10m/s

nticks = 100
timestep = 0.1
rec = QueueRecord(Vehicle1D, nticks+1, timestep)
simulate!(LaneFollowingAccel, rec, scene, roadway, models, nticks)
render(rec[0], roadway, [CarIDOverlay()], cam=cam, canvas_height=100)









    Out[5]:

We can use Interact to inspect the simulation.



In [6]:

    
using Interact
@manipulate for frame_index in 1 : nframes(rec)
    render(rec[frame_index-nframes(rec)], roadway, [CarIDOverlay()], cam=cam, canvas_height=100)
end









    




    







    











    











    




  







    Out[6]:






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