autonomous_vehicles/code/baseline.py at main · chickert/autonomous_vehicles · GitHub

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import warnings

import numpy as np
import pandas as pd

import matplotlib.pyplot as plt

from structures import RingRoad
from animations import Animation

if __name__=='__main__':

    # Hide warnings about safe distance violation (still in development):
    warnings.filterwarnings("ignore", category=UserWarning)

    # Define a ring road environment:
    env = RingRoad(
        num_vehicles = 22,
        ring_length = 230.0,
        starting_noise = 4.0,
        temporal_res = 0.3,
        av_activate = 30,
        seed = 286,
    )

    # Run the simulation for set number of time steps:
    total_time = 50  # In seconds.
    total_steps = int(np.ceil(total_time/env.dt))
    env.run(steps=total_steps)

    # Plot initial state:
    fig,ax = env.plot_ring(step=0, draw_cars_to_scale=True, draw_safety_buffer=False)
    filename = "../outputs/environment_initial_state.png"
    plt.savefig(filename)
    print("Saved : {} .".format(filename))

    # Plot state at activation:
    fig,ax = env.plot_ring(step=int(np.ceil(env.av_activate/env.dt))-1, draw_cars_to_scale=True, draw_safety_buffer=False)
    filename = "../outputs/environment_before_activation.png"
    plt.savefig(filename)
    print("Saved : {} .".format(filename))

    # Plot stable state:
    fig,ax = env.plot_ring(step=env.step, draw_cars_to_scale=True, draw_safety_buffer=False)
    filename = "../outputs/environment_stable_state.png"
    plt.savefig(filename)
    print("Saved : {} .".format(filename))

    # Plot position history:
    fig,ax = env.plot_positions()
    filename = "../outputs/positions_history.png"
    plt.savefig(filename)
    print("Saved : {} .".format(filename))

    # Plot velocity history:
    fig,ax = env.plot_velocities(show_sigma=True)
    filename = "../outputs/velocities_history.png"
    plt.savefig(filename)
    print("Saved : {} .".format(filename))

    # Calculate standard deviations:
    speeds_before = env.get_vehicle_vel_table( range(0,env.av_activate) )
    speeds_after = env.get_vehicle_vel_table( range(env.av_activate, env.step) )
    df = pd.concat([
        pd.DataFrame(speeds_before.std(axis=0).to_frame(name='before AV control')),
        pd.DataFrame(speeds_after.std(axis=0).to_frame(name='after AV control')),
    ], axis=1)
    df_mean = df.mean(axis=0).to_frame(name='mean').transpose()
    df = pd.concat([df_mean,df], axis=0)
    filename = "../outputs/velocity_standard_deviations.csv"
    df.to_csv(filename, index=False)
    print("Saved : {} .".format(filename))

    print("")

    print("Mean standard deviation before: {}".format(df.loc['mean','before AV control']))
    print("Mean standard deviation after: {}".format(df.loc['mean','after AV control']))

    print("""
    To visualize the baseline as an animation,
    please run the code/animation.py script
    or see the notebooks/Animations.ipynb notebook.
    """)

    print("Done.")