The algamesdriving.jl from tmacattank

algamesdriving.jl's Introduction

Purpose

A set of tools to quickly generate and visualize autonomous driving scenarios for Algames.jl.

Installation

Pkg.add("AlgamesDriving")

Related Paper

RSS 2020

Autonomous Robots paper submission:

The experiments are available here.

Quick Start

To run a simple example of a autonomous driving see script in /examples/intro_example.jl.

Install AlgamesDriving: add AlgamesDriving#v0.1.4
Install Algames: add Algames#v0.1.6
Install Plots: add Plots
Add other dependencies: add LinearAlgebra Meshcat StaticArrays
run the script!

Starter Code

Generate Autonomous Driving Scenario

# Create Roadway
roadway_opts = MergingRoadwayOptions()
roadway = build_roadway(roadway_opts)

# Create Dynamics Model
p = 3
model = UnicycleGame(p=p)

# Create Players
players = Vector{Player}(undef, p)
# For each player specify the start and goal state
players[1] = Player(model, roadway.lane[1],
    x0=VehicleState(0.0, 0.1, 0.0, 0.3),
    xf=VehicleState(4.0, 0.1, 0.0, 0.3))
players[2] = Player(model, roadway.lane[2],
    x0=VehicleState(0.0,-0.1, 0.0, 0.3),
    xf=VehicleState(4.0,-0.1, 0.0, 0.3))
players[3] = Player(model, roadway.lane[3],
    x0=VehicleState(0.2,-0.1, 0.0, 0.3),
    xf=VehicleState(4.0, 0.1, 0.0, 0.3))

# Create Scenario
sce = Scenario(model, roadway, players)

Generate and Solve the Dynamic Game Problem

N = 20 # number of time steps
dt = 0.1 # time step
solver_opts = Options() # solver options
prob = GameProblem(N, dt, sce, solver_opts) # atonomous driving problem
@time newton_solve!(prob) # solve the problem

# Plotting vehicles' trajectories and the solver's progress
plot!(model, prob.pdtraj.pr)
plot!(prob.stats)

Visualize the Results

# Visualize Scenario
vis = Visualizer()
open(vis)
# Build environment
set_scenario!(vis, sce)
# Set camera
set_camera_birdseye!(vis, height=7)
set_env!(vis, VehicleState(2.,0.,0.,0.))
# Animate the vehicles
set_traj!(vis, model, sce, prob.pdtraj.pr)

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