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trajdata.jl
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trajdata.jl
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const NGSIM_TIMESTEP = 0.1 # [sec]
const SMOOTHING_WIDTH_POS = 0.5 # [s]
include(joinpath(@__DIR__, "trajectory_smoothing.jl"))
# from "Estimating Acceleration and Lane-Changing
# Dynamics Based on NGSIM Trajectory Data"
function symmetric_exponential_moving_average(
arr :: Vector{Float64},
T :: Float64; # smoothing width [s]
dt :: Float64 = 0.1 # sampling period [s]
)
Δ = T / dt
N = length(arr)
retval = Array{Float64}(undef, N)
for i = 1 : N
Z = 0.0
x = 0.0
D = min(round(Int, 3Δ), i-1)
if i+D > N
D = N-i
end
for k in (i-D):(i+D)
e = exp(-abs(i-k)/Δ)
Z += e
x += arr[k] * e
end
retval[i] = x / Z
end
retval
end
###############
mutable struct FilterTrajectoryResult
carid::Int
x_arr::Vector{Float64}
y_arr::Vector{Float64}
θ_arr::Vector{Float64}
v_arr::Vector{Float64}
function FilterTrajectoryResult(trajdata::NGSIMTrajdata, carid::Int)
dfstart = trajdata.car2start[carid]
N = trajdata.df[dfstart, :n_frames_in_dataset]
# these are our observations
x_arr = fill(NaN, N)
y_arr = fill(NaN, N)
θ_arr = fill(NaN, N)
v_arr = fill(NaN, N)
for i in 1 : N
x_arr[i] = trajdata.df[dfstart + i - 1, :global_x]
y_arr[i] = trajdata.df[dfstart + i - 1, :global_y]
end
# choose an initial belief
θ_arr[1] = atan(y_arr[5] - y_arr[1], x_arr[5] - x_arr[1])
v_arr[1] = trajdata.df[dfstart, :speed] #hypot(ftr.y_arr[lookahead] - y₀, ftr.x_arr[lookahead] - x₀)/ν.Δt
if v_arr[1] < 1.0 # small speed
# estimate with greater lookahead
θ_arr[1] = atan(y_arr[end] - y_arr[1], x_arr[end] - x_arr[1])
end
new(carid, x_arr, y_arr, θ_arr, v_arr)
end
end
Base.length(ftr::FilterTrajectoryResult) = length(ftr.x_arr)
function filter_trajectory!(ftr::FilterTrajectoryResult, ν::VehicleSystem = VehicleSystem())
μ = [ftr.x_arr[1], ftr.y_arr[1], ftr.θ_arr[1], ftr.v_arr[1]]
σ = 1e-1
Σ = Matrix(Diagonal([σ*0.01, σ*0.01, σ*0.1, σ]))
# assume control is centered
u = [0.0, 0.0]
z = [NaN, NaN]
for i in 2 : length(ftr)
# pull observation
z[1] = ftr.x_arr[i]
z[2] = ftr.y_arr[i]
# apply extended Kalman filter
μ, Σ = EKF(ν, μ, Σ, u, z)
# store results
ftr.x_arr[i] = μ[1]
ftr.y_arr[i] = μ[2]
ftr.θ_arr[i] = μ[3]
ftr.v_arr[i] = μ[4]
end
ftr
end
function Base.copy!(trajdata::NGSIMTrajdata, ftr::FilterTrajectoryResult)
dfstart = trajdata.car2start[ftr.carid]
N = trajdata.df[dfstart, :n_frames_in_dataset]
# copy results back to trajdata
for i in 1 : N
trajdata.df[dfstart + i - 1, :global_x] = ftr.x_arr[i]
trajdata.df[dfstart + i - 1, :global_y] = ftr.y_arr[i]
# trajdata.df[dfstart + i - 1, :speed] = ftr.v_arr[i]
if i > 1
trajdata.df[dfstart + i - 1, :speed] = hypot(ftr.x_arr[i] - ftr.x_arr[i-1], ftr.y_arr[i] - ftr.y_arr[i-1]) / NGSIM_TIMESTEP
else
trajdata.df[dfstart + i - 1, :speed] = hypot(ftr.x_arr[i+1] - ftr.x_arr[i], ftr.y_arr[i+1] - ftr.y_arr[i]) / NGSIM_TIMESTEP
end
trajdata.df[dfstart + i - 1, :global_heading] = ftr.θ_arr[i]
end
trajdata
end
function filter_trajectory!(trajdata::NGSIMTrajdata, carid::Int)
#=
Filters the given vehicle's trajectory using an Extended Kalman Filter
=#
ftr = FilterTrajectoryResult(trajdata, carid)
# run pre-smoothing
ftr.x_arr = symmetric_exponential_moving_average(ftr.x_arr, SMOOTHING_WIDTH_POS)
ftr.y_arr = symmetric_exponential_moving_average(ftr.y_arr, SMOOTHING_WIDTH_POS)
filter_trajectory!(ftr)
copy!(trajdata, ftr)
trajdata
end
function load_ngsim_trajdata(filepath::String; autofilter::Bool=true)
print("loading from file: ");
tdraw = NGSIMTrajdata(filepath)
if autofilter && splitext(filepath)[2] == ".txt" # txt is original
print("filtering: ");
for carid in carid_set(tdraw)
filter_trajectory!(tdraw, carid)
end
end
tdraw
end
function Base.convert(::Type{Trajdata}, tdraw::NGSIMTrajdata, roadway::Roadway)
df = tdraw.df
vehdefs = Dict{Int, VehicleDef}()
states = Array{RecordState{VehicleState, Int}}(undef, nrow(df))
frames = Array{RecordFrame}(undef, nframes(tdraw))
for (id, dfind) in tdraw.car2start
vehdefs[id] = VehicleDef(df[dfind, :class], df[dfind, :length]*METERS_PER_FOOT, df[dfind, :width]*METERS_PER_FOOT)
end
state_ind = 0
for frame in 1 : nframes(tdraw)
frame_lo = state_ind+1
for id in carsinframe(tdraw, frame)
dfind = car_df_index(tdraw, id, frame)
posG = VecSE2(df[dfind, :global_x]*METERS_PER_FOOT, df[dfind, :global_y]*METERS_PER_FOOT, df[dfind, :global_heading])
speed = df[dfind, :speed]*METERS_PER_FOOT
states[state_ind += 1] = RecordState(VehicleState(posG, roadway, speed), id)
end
frame_hi = state_ind
frames[frame] = RecordFrame(frame_lo, frame_hi)
end
Trajdata(NGSIM_TIMESTEP, frames, states, vehdefs)
end
get_corresponding_roadway(filename::String) = occursin("i101", filename) ? ROADWAY_101 : ROADWAY_80
function convert_raw_ngsim_to_trajdatas()
for filepath in NGSIM_TRAJDATA_PATHS
filename = splitdir(filepath)[2]
println("converting ", filename);
roadway = get_corresponding_roadway(filename)
tdraw = NGSIM.load_ngsim_trajdata(filepath)
trajdata = convert(Trajdata, tdraw, roadway)
outpath = joinpath(@__DIR__, "../data/trajdata_"*filename)
open(io->write(io, MIME"text/plain"(), trajdata), outpath, "w")
end
end
const TRAJDATA_PATHS = [
joinpath(@__DIR__, "../data/trajdata_i101_trajectories-0750am-0805am.txt"),
joinpath(@__DIR__, "../data/trajdata_i101_trajectories-0805am-0820am.txt"),
joinpath(@__DIR__, "../data/trajdata_i101_trajectories-0820am-0835am.txt"),
joinpath(@__DIR__, "../data/trajdata_i80_trajectories-0400-0415.txt"),
joinpath(@__DIR__, "../data/trajdata_i80_trajectories-0500-0515.txt"),
joinpath(@__DIR__, "../data/trajdata_i80_trajectories-0515-0530.txt"),
]
function load_trajdata(filepath::String)
td = open(io->read(io, MIME"text/plain"(), Trajdata), filepath, "r")
td
end
load_trajdata(i::Int) = load_trajdata(TRAJDATA_PATHS[i])
get_corresponding_roadway(i::Int) = get_corresponding_roadway(TRAJDATA_PATHS[i])