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module_7.py
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module_7.py
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#!/usr/bin/env python3
# This work is licensed under the terms of the MIT license.
# For a copy, see <https://opensource.org/licenses/MIT>.
"""
CARLA waypoint follower assessment client script.
A controller assessment to follow a given trajectory, where the trajectory
can be defined using way-points.
STARTING in a moment...
"""
from __future__ import print_function
from __future__ import division
# System level imports
import sys
import os
import argparse
import logging
import time
import math
import numpy as np
import csv
import matplotlib.pyplot as plt
import controller2d
import configparser
import local_planner
import behavioural_planner
# Script level imports
sys.path.append(os.path.abspath(sys.path[0] + '/..'))
import live_plotter as lv # Custom live plotting library
from carla import sensor
from carla.client import make_carla_client, VehicleControl
from carla.settings import CarlaSettings
from carla.tcp import TCPConnectionError
from carla.controller import utils
"""
Configurable params
"""
ITER_FOR_SIM_TIMESTEP = 10 # no. iterations to compute approx sim timestep
WAIT_TIME_BEFORE_START = 1.00 # game seconds (time before controller start)
TOTAL_RUN_TIME = 100.00 # game seconds (total runtime before sim end)
TOTAL_FRAME_BUFFER = 300 # number of frames to buffer after total runtime
NUM_PEDESTRIANS = 0 # total number of pedestrians to spawn
NUM_VEHICLES = 2 # total number of vehicles to spawn
SEED_PEDESTRIANS = 0 # seed for pedestrian spawn randomizer
SEED_VEHICLES = 0 # seed for vehicle spawn randomizer
CLIENT_WAIT_TIME = 3 # wait time for client before starting episode
# used to make sure the server loads
# consistently
WEATHERID = {
"DEFAULT": 0,
"CLEARNOON": 1,
"CLOUDYNOON": 2,
"WETNOON": 3,
"WETCLOUDYNOON": 4,
"MIDRAINYNOON": 5,
"HARDRAINNOON": 6,
"SOFTRAINNOON": 7,
"CLEARSUNSET": 8,
"CLOUDYSUNSET": 9,
"WETSUNSET": 10,
"WETCLOUDYSUNSET": 11,
"MIDRAINSUNSET": 12,
"HARDRAINSUNSET": 13,
"SOFTRAINSUNSET": 14,
}
SIMWEATHER = WEATHERID["CLEARNOON"] # set simulation weather
PLAYER_START_INDEX = 1 # spawn index for player (keep to 1)
FIGSIZE_X_INCHES = 8 # x figure size of feedback in inches
FIGSIZE_Y_INCHES = 8 # y figure size of feedback in inches
PLOT_LEFT = 0.1 # in fractions of figure width and height
PLOT_BOT = 0.1
PLOT_WIDTH = 0.8
PLOT_HEIGHT = 0.8
WAYPOINTS_FILENAME = 'course4_waypoints.txt' # waypoint file to load
DIST_THRESHOLD_TO_LAST_WAYPOINT = 2.0 # some distance from last position before
# simulation ends
# Planning Constants
NUM_PATHS = 7
BP_LOOKAHEAD_BASE = 8.0 # m
BP_LOOKAHEAD_TIME = 2.0 # s
PATH_OFFSET = 1.5 # m
CIRCLE_OFFSETS = [-1.0, 1.0, 3.0] # m
CIRCLE_RADII = [1.5, 1.5, 1.5] # m
TIME_GAP = 1.0 # s
PATH_SELECT_WEIGHT = 10
A_MAX = 1.5 # m/s^2
SLOW_SPEED = 2.0 # m/s
STOP_LINE_BUFFER = 3.5 # m
LEAD_VEHICLE_LOOKAHEAD = 20.0 # m
LP_FREQUENCY_DIVISOR = 2 # Frequency divisor to make the
# local planner operate at a lower
# frequency than the controller
# (which operates at the simulation
# frequency). Must be a natural
# number.
# Course 4 specific parameters
C4_STOP_SIGN_FILE = 'stop_sign_params.txt'
C4_STOP_SIGN_FENCELENGTH = 5 # m
C4_PARKED_CAR_FILE = 'parked_vehicle_params.txt'
# Path interpolation parameters
INTERP_MAX_POINTS_PLOT = 10 # number of points used for displaying
# selected path
INTERP_DISTANCE_RES = 0.01 # distance between interpolated points
# controller output directory
CONTROLLER_OUTPUT_FOLDER = os.path.dirname(os.path.realpath(__file__)) +\
'/controller_output/'
def make_carla_settings(args):
"""Make a CarlaSettings object with the settings we need.
"""
settings = CarlaSettings()
# There is no need for non-agent info requests if there are no pedestrians
# or vehicles.
get_non_player_agents_info = False
if (NUM_PEDESTRIANS > 0 or NUM_VEHICLES > 0):
get_non_player_agents_info = True
# Base level settings
settings.set(
SynchronousMode=True,
SendNonPlayerAgentsInfo=get_non_player_agents_info,
NumberOfVehicles=NUM_VEHICLES,
NumberOfPedestrians=NUM_PEDESTRIANS,
SeedVehicles=SEED_VEHICLES,
SeedPedestrians=SEED_PEDESTRIANS,
WeatherId=SIMWEATHER,
QualityLevel=args.quality_level)
return settings
class Timer(object):
""" Timer Class
The steps are used to calculate FPS, while the lap or seconds since lap is
used to compute elapsed time.
"""
def __init__(self, period):
self.step = 0
self._lap_step = 0
self._lap_time = time.time()
self._period_for_lap = period
def tick(self):
self.step += 1
def has_exceeded_lap_period(self):
if self.elapsed_seconds_since_lap() >= self._period_for_lap:
return True
else:
return False
def lap(self):
self._lap_step = self.step
self._lap_time = time.time()
def ticks_per_second(self):
return float(self.step - self._lap_step) /\
self.elapsed_seconds_since_lap()
def elapsed_seconds_since_lap(self):
return time.time() - self._lap_time
def get_current_pose(measurement):
"""Obtains current x,y,yaw pose from the client measurements
Obtains the current x,y, and yaw pose from the client measurements.
Args:
measurement: The CARLA client measurements (from read_data())
Returns: (x, y, yaw)
x: X position in meters
y: Y position in meters
yaw: Yaw position in radians
"""
x = measurement.player_measurements.transform.location.x
y = measurement.player_measurements.transform.location.y
yaw = math.radians(measurement.player_measurements.transform.rotation.yaw)
return (x, y, yaw)
def get_start_pos(scene):
"""Obtains player start x,y, yaw pose from the scene
Obtains the player x,y, and yaw pose from the scene.
Args:
scene: The CARLA scene object
Returns: (x, y, yaw)
x: X position in meters
y: Y position in meters
yaw: Yaw position in radians
"""
x = scene.player_start_spots[0].location.x
y = scene.player_start_spots[0].location.y
yaw = math.radians(scene.player_start_spots[0].rotation.yaw)
return (x, y, yaw)
def get_player_collided_flag(measurement,
prev_collision_vehicles,
prev_collision_pedestrians,
prev_collision_other):
"""Obtains collision flag from player. Check if any of the three collision
metrics (vehicles, pedestrians, others) from the player are true, if so the
player has collided to something.
Note: From the CARLA documentation:
"Collisions are not annotated if the vehicle is not moving (<1km/h) to avoid
annotating undesired collision due to mistakes in the AI of non-player
agents."
"""
player_meas = measurement.player_measurements
current_collision_vehicles = player_meas.collision_vehicles
current_collision_pedestrians = player_meas.collision_pedestrians
current_collision_other = player_meas.collision_other
collided_vehicles = current_collision_vehicles > prev_collision_vehicles
collided_pedestrians = current_collision_pedestrians > \
prev_collision_pedestrians
collided_other = current_collision_other > prev_collision_other
return (collided_vehicles or collided_pedestrians or collided_other,
current_collision_vehicles,
current_collision_pedestrians,
current_collision_other)
def send_control_command(client, throttle, steer, brake,
hand_brake=False, reverse=False):
"""Send control command to CARLA client.
Send control command to CARLA client.
Args:
client: The CARLA client object
throttle: Throttle command for the sim car [0, 1]
steer: Steer command for the sim car [-1, 1]
brake: Brake command for the sim car [0, 1]
hand_brake: Whether the hand brake is engaged
reverse: Whether the sim car is in the reverse gear
"""
control = VehicleControl()
# Clamp all values within their limits
steer = np.fmax(np.fmin(steer, 1.0), -1.0)
throttle = np.fmax(np.fmin(throttle, 1.0), 0)
brake = np.fmax(np.fmin(brake, 1.0), 0)
control.steer = steer
control.throttle = throttle
control.brake = brake
control.hand_brake = hand_brake
control.reverse = reverse
client.send_control(control)
def create_controller_output_dir(output_folder):
if not os.path.exists(output_folder):
os.makedirs(output_folder)
def store_trajectory_plot(graph, fname):
""" Store the resulting plot.
"""
create_controller_output_dir(CONTROLLER_OUTPUT_FOLDER)
file_name = os.path.join(CONTROLLER_OUTPUT_FOLDER, fname)
graph.savefig(file_name)
def write_trajectory_file(x_list, y_list, v_list, t_list, collided_list):
create_controller_output_dir(CONTROLLER_OUTPUT_FOLDER)
file_name = os.path.join(CONTROLLER_OUTPUT_FOLDER, 'trajectory.txt')
with open(file_name, 'w') as trajectory_file:
for i in range(len(x_list)):
trajectory_file.write('%3.3f, %3.3f, %2.3f, %6.3f %r\n' %\
(x_list[i], y_list[i], v_list[i], t_list[i],
collided_list[i]))
def write_collisioncount_file(collided_list):
create_controller_output_dir(CONTROLLER_OUTPUT_FOLDER)
file_name = os.path.join(CONTROLLER_OUTPUT_FOLDER, 'collision_count.txt')
with open(file_name, 'w') as collision_file:
collision_file.write(str(sum(collided_list)))
def exec_waypoint_nav_demo(args):
""" Executes waypoint navigation demo.
"""
with make_carla_client(args.host, args.port) as client:
print('Carla client connected.')
settings = make_carla_settings(args)
# Now we load these settings into the server. The server replies
# with a scene description containing the available start spots for
# the player. Here we can provide a CarlaSettings object or a
# CarlaSettings.ini file as string.
scene = client.load_settings(settings)
# Refer to the player start folder in the WorldOutliner to see the
# player start information
player_start = PLAYER_START_INDEX
client.start_episode(player_start)
time.sleep(CLIENT_WAIT_TIME);
# Notify the server that we want to start the episode at the
# player_start index. This function blocks until the server is ready
# to start the episode.
print('Starting new episode at %r...' % scene.map_name)
client.start_episode(player_start)
#############################################
# Load Configurations
#############################################
# Load configuration file (options.cfg) and then parses for the various
# options. Here we have two main options:
# live_plotting and live_plotting_period, which controls whether
# live plotting is enabled or how often the live plotter updates
# during the simulation run.
config = configparser.ConfigParser()
config.read(os.path.join(
os.path.dirname(os.path.realpath(__file__)), 'options.cfg'))
demo_opt = config['Demo Parameters']
# Get options
enable_live_plot = demo_opt.get('live_plotting', 'true').capitalize()
enable_live_plot = enable_live_plot == 'True'
live_plot_period = float(demo_opt.get('live_plotting_period', 0))
# Set options
live_plot_timer = Timer(live_plot_period)
#############################################
# Load stop sign and parked vehicle parameters
# Convert to input params for LP
#############################################
# Stop sign (X(m), Y(m), Z(m), Yaw(deg))
stopsign_data = None
stopsign_fences = [] # [x0, y0, x1, y1]
with open(C4_STOP_SIGN_FILE, 'r') as stopsign_file:
next(stopsign_file) # skip header
stopsign_reader = csv.reader(stopsign_file,
delimiter=',',
quoting=csv.QUOTE_NONNUMERIC)
stopsign_data = list(stopsign_reader)
# convert to rad
for i in range(len(stopsign_data)):
stopsign_data[i][3] = stopsign_data[i][3] * np.pi / 180.0
# obtain stop sign fence points for LP
for i in range(len(stopsign_data)):
x = stopsign_data[i][0]
y = stopsign_data[i][1]
z = stopsign_data[i][2]
yaw = stopsign_data[i][3] + np.pi / 2.0 # add 90 degrees for fence
spos = np.array([
[0, 0 ],
[0, C4_STOP_SIGN_FENCELENGTH]])
rotyaw = np.array([
[np.cos(yaw), np.sin(yaw)],
[-np.sin(yaw), np.cos(yaw)]])
spos_shift = np.array([
[x, x],
[y, y]])
spos = np.add(np.matmul(rotyaw, spos), spos_shift)
stopsign_fences.append([spos[0,0], spos[1,0], spos[0,1], spos[1,1]])
# Parked car(s) (X(m), Y(m), Z(m), Yaw(deg), RADX(m), RADY(m), RADZ(m))
parkedcar_data = None
parkedcar_box_pts = [] # [x,y]
with open(C4_PARKED_CAR_FILE, 'r') as parkedcar_file:
next(parkedcar_file) # skip header
parkedcar_reader = csv.reader(parkedcar_file,
delimiter=',',
quoting=csv.QUOTE_NONNUMERIC)
parkedcar_data = list(parkedcar_reader)
# convert to rad
for i in range(len(parkedcar_data)):
parkedcar_data[i][3] = parkedcar_data[i][3] * np.pi / 180.0
# obtain parked car(s) box points for LP
for i in range(len(parkedcar_data)):
x = parkedcar_data[i][0]
y = parkedcar_data[i][1]
z = parkedcar_data[i][2]
yaw = parkedcar_data[i][3]
xrad = parkedcar_data[i][4]
yrad = parkedcar_data[i][5]
zrad = parkedcar_data[i][6]
cpos = np.array([
[-xrad, -xrad, -xrad, 0, xrad, xrad, xrad, 0 ],
[-yrad, 0, yrad, yrad, yrad, 0, -yrad, -yrad]])
rotyaw = np.array([
[np.cos(yaw), np.sin(yaw)],
[-np.sin(yaw), np.cos(yaw)]])
cpos_shift = np.array([
[x, x, x, x, x, x, x, x],
[y, y, y, y, y, y, y, y]])
cpos = np.add(np.matmul(rotyaw, cpos), cpos_shift)
for j in range(cpos.shape[1]):
parkedcar_box_pts.append([cpos[0,j], cpos[1,j]])
#############################################
# Load Waypoints
#############################################
# Opens the waypoint file and stores it to "waypoints"
waypoints_file = WAYPOINTS_FILENAME
waypoints_filepath =\
os.path.join(os.path.dirname(os.path.realpath(__file__)),
WAYPOINTS_FILENAME)
waypoints_np = None
with open(waypoints_filepath) as waypoints_file_handle:
waypoints = list(csv.reader(waypoints_file_handle,
delimiter=',',
quoting=csv.QUOTE_NONNUMERIC))
waypoints_np = np.array(waypoints)
#############################################
# Controller 2D Class Declaration
#############################################
# This is where we take the controller2d.py class
# and apply it to the simulator
controller = controller2d.Controller2D(waypoints)
#############################################
# Determine simulation average timestep (and total frames)
#############################################
# Ensure at least one frame is used to compute average timestep
num_iterations = ITER_FOR_SIM_TIMESTEP
if (ITER_FOR_SIM_TIMESTEP < 1):
num_iterations = 1
# Gather current data from the CARLA server. This is used to get the
# simulator starting game time. Note that we also need to
# send a command back to the CARLA server because synchronous mode
# is enabled.
measurement_data, sensor_data = client.read_data()
sim_start_stamp = measurement_data.game_timestamp / 1000.0
# Send a control command to proceed to next iteration.
# This mainly applies for simulations that are in synchronous mode.
send_control_command(client, throttle=0.0, steer=0, brake=1.0)
# Computes the average timestep based on several initial iterations
sim_duration = 0
for i in range(num_iterations):
# Gather current data
measurement_data, sensor_data = client.read_data()
# Send a control command to proceed to next iteration
send_control_command(client, throttle=0.0, steer=0, brake=1.0)
# Last stamp
if i == num_iterations - 1:
sim_duration = measurement_data.game_timestamp / 1000.0 -\
sim_start_stamp
# Outputs average simulation timestep and computes how many frames
# will elapse before the simulation should end based on various
# parameters that we set in the beginning.
SIMULATION_TIME_STEP = sim_duration / float(num_iterations)
print("SERVER SIMULATION STEP APPROXIMATION: " + \
str(SIMULATION_TIME_STEP))
TOTAL_EPISODE_FRAMES = int((TOTAL_RUN_TIME + WAIT_TIME_BEFORE_START) /\
SIMULATION_TIME_STEP) + TOTAL_FRAME_BUFFER
#############################################
# Frame-by-Frame Iteration and Initialization
#############################################
# Store pose history starting from the start position
measurement_data, sensor_data = client.read_data()
start_timestamp = measurement_data.game_timestamp / 1000.0
start_x, start_y, start_yaw = get_current_pose(measurement_data)
send_control_command(client, throttle=0.0, steer=0, brake=1.0)
x_history = [start_x]
y_history = [start_y]
yaw_history = [start_yaw]
time_history = [0]
speed_history = [0]
collided_flag_history = [False] # assume player starts off non-collided
#############################################
# Vehicle Trajectory Live Plotting Setup
#############################################
# Uses the live plotter to generate live feedback during the simulation
# The two feedback includes the trajectory feedback and
# the controller feedback (which includes the speed tracking).
lp_traj = lv.LivePlotter(tk_title="Trajectory Trace")
lp_1d = lv.LivePlotter(tk_title="Controls Feedback")
###
# Add 2D position / trajectory plot
###
trajectory_fig = lp_traj.plot_new_dynamic_2d_figure(
title='Vehicle Trajectory',
figsize=(FIGSIZE_X_INCHES, FIGSIZE_Y_INCHES),
edgecolor="black",
rect=[PLOT_LEFT, PLOT_BOT, PLOT_WIDTH, PLOT_HEIGHT])
trajectory_fig.set_invert_x_axis() # Because UE4 uses left-handed
# coordinate system the X
# axis in the graph is flipped
trajectory_fig.set_axis_equal() # X-Y spacing should be equal in size
# Add waypoint markers
trajectory_fig.add_graph("waypoints", window_size=waypoints_np.shape[0],
x0=waypoints_np[:,0], y0=waypoints_np[:,1],
linestyle="-", marker="", color='g')
# Add trajectory markers
trajectory_fig.add_graph("trajectory", window_size=TOTAL_EPISODE_FRAMES,
x0=[start_x]*TOTAL_EPISODE_FRAMES,
y0=[start_y]*TOTAL_EPISODE_FRAMES,
color=[1, 0.5, 0])
# Add starting position marker
trajectory_fig.add_graph("start_pos", window_size=1,
x0=[start_x], y0=[start_y],
marker=11, color=[1, 0.5, 0],
markertext="Start", marker_text_offset=1)
# Add end position marker
trajectory_fig.add_graph("end_pos", window_size=1,
x0=[waypoints_np[-1, 0]],
y0=[waypoints_np[-1, 1]],
marker="D", color='r',
markertext="End", marker_text_offset=1)
# Add car marker
trajectory_fig.add_graph("car", window_size=1,
marker="s", color='b', markertext="Car",
marker_text_offset=1)
# Add lead car information
trajectory_fig.add_graph("leadcar", window_size=1,
marker="s", color='g', markertext="Lead Car",
marker_text_offset=1)
# Add stop sign position
trajectory_fig.add_graph("stopsign", window_size=1,
x0=[stopsign_fences[0][0]], y0=[stopsign_fences[0][1]],
marker="H", color="r",
markertext="Stop Sign", marker_text_offset=1)
# Add stop sign "stop line"
trajectory_fig.add_graph("stopsign_fence", window_size=1,
x0=[stopsign_fences[0][0], stopsign_fences[0][2]],
y0=[stopsign_fences[0][1], stopsign_fences[0][3]],
color="r")
# Load parked car points
parkedcar_box_pts_np = np.array(parkedcar_box_pts)
trajectory_fig.add_graph("parkedcar_pts", window_size=parkedcar_box_pts_np.shape[0],
x0=parkedcar_box_pts_np[:,0], y0=parkedcar_box_pts_np[:,1],
linestyle="", marker="+", color='b')
# Add lookahead path
trajectory_fig.add_graph("selected_path",
window_size=INTERP_MAX_POINTS_PLOT,
x0=[start_x]*INTERP_MAX_POINTS_PLOT,
y0=[start_y]*INTERP_MAX_POINTS_PLOT,
color=[1, 0.5, 0.0],
linewidth=3)
# Add local path proposals
for i in range(NUM_PATHS):
trajectory_fig.add_graph("local_path " + str(i), window_size=200,
x0=None, y0=None, color=[0.0, 0.0, 1.0])
###
# Add 1D speed profile updater
###
forward_speed_fig =\
lp_1d.plot_new_dynamic_figure(title="Forward Speed (m/s)")
forward_speed_fig.add_graph("forward_speed",
label="forward_speed",
window_size=TOTAL_EPISODE_FRAMES)
forward_speed_fig.add_graph("reference_signal",
label="reference_Signal",
window_size=TOTAL_EPISODE_FRAMES)
# Add throttle signals graph
throttle_fig = lp_1d.plot_new_dynamic_figure(title="Throttle")
throttle_fig.add_graph("throttle",
label="throttle",
window_size=TOTAL_EPISODE_FRAMES)
# Add brake signals graph
brake_fig = lp_1d.plot_new_dynamic_figure(title="Brake")
brake_fig.add_graph("brake",
label="brake",
window_size=TOTAL_EPISODE_FRAMES)
# Add steering signals graph
steer_fig = lp_1d.plot_new_dynamic_figure(title="Steer")
steer_fig.add_graph("steer",
label="steer",
window_size=TOTAL_EPISODE_FRAMES)
# live plotter is disabled, hide windows
if not enable_live_plot:
lp_traj._root.withdraw()
lp_1d._root.withdraw()
#############################################
# Local Planner Variables
#############################################
wp_goal_index = 0
local_waypoints = None
path_validity = np.zeros((NUM_PATHS, 1), dtype=bool)
lp = local_planner.LocalPlanner(NUM_PATHS,
PATH_OFFSET,
CIRCLE_OFFSETS,
CIRCLE_RADII,
PATH_SELECT_WEIGHT,
TIME_GAP,
A_MAX,
SLOW_SPEED,
STOP_LINE_BUFFER)
bp = behavioural_planner.BehaviouralPlanner(BP_LOOKAHEAD_BASE,
stopsign_fences,
LEAD_VEHICLE_LOOKAHEAD)
#############################################
# Scenario Execution Loop
#############################################
# Iterate the frames until the end of the waypoints is reached or
# the TOTAL_EPISODE_FRAMES is reached. The controller simulation then
# ouptuts the results to the controller output directory.
reached_the_end = False
skip_first_frame = True
# Initialize the current timestamp.
current_timestamp = start_timestamp
# Initialize collision history
prev_collision_vehicles = 0
prev_collision_pedestrians = 0
prev_collision_other = 0
for frame in range(TOTAL_EPISODE_FRAMES):
# Gather current data from the CARLA server
measurement_data, sensor_data = client.read_data()
# Update pose and timestamp
prev_timestamp = current_timestamp
current_x, current_y, current_yaw = \
get_current_pose(measurement_data)
current_speed = measurement_data.player_measurements.forward_speed
current_timestamp = float(measurement_data.game_timestamp) / 1000.0
# Wait for some initial time before starting the demo
if current_timestamp <= WAIT_TIME_BEFORE_START:
send_control_command(client, throttle=0.0, steer=0, brake=1.0)
continue
else:
current_timestamp = current_timestamp - WAIT_TIME_BEFORE_START
# Store history
x_history.append(current_x)
y_history.append(current_y)
yaw_history.append(current_yaw)
speed_history.append(current_speed)
time_history.append(current_timestamp)
# Store collision history
collided_flag,\
prev_collision_vehicles,\
prev_collision_pedestrians,\
prev_collision_other = get_player_collided_flag(measurement_data,
prev_collision_vehicles,
prev_collision_pedestrians,
prev_collision_other)
collided_flag_history.append(collided_flag)
###
# Local Planner Update:
# This will use the behavioural_planner.py and local_planner.py
# implementations that the learner will be tasked with in
# the Course 4 final project
###
# Obtain Lead Vehicle information.
lead_car_pos = []
lead_car_length = []
lead_car_speed = []
for agent in measurement_data.non_player_agents:
agent_id = agent.id
if agent.HasField('vehicle'):
lead_car_pos.append(
[agent.vehicle.transform.location.x,
agent.vehicle.transform.location.y])
lead_car_length.append(agent.vehicle.bounding_box.extent.x)
lead_car_speed.append(agent.vehicle.forward_speed)
# Execute the behaviour and local planning in the current instance
# Note that updating the local path during every controller update
# produces issues with the tracking performance (imagine everytime
# the controller tried to follow the path, a new path appears). For
# this reason, the local planner (LP) will update every X frame,
# stored in the variable LP_FREQUENCY_DIVISOR, as it is analogous
# to be operating at a frequency that is a division to the
# simulation frequency.
if frame % LP_FREQUENCY_DIVISOR == 0:
# TODO Once you have completed the prerequisite functions of each of these
# lines, you can uncomment the code below the dashed line to run the planner.
# Note that functions lower in this block often require outputs from the functions
# earlier in this block, so it may be easier to implement those first to
# get a more intuitive flow of the planner.
# In addition, some of these functions have already been implemented for you,
# but it is useful for you to understand what each function is doing.
# Before you uncomment a function, please take the time to take a look at
# it and understand what is going on. It will also help inform you on the
# flow of the planner, which in turn will help you implement the functions
# flagged for you in the TODO's.
# TODO: Uncomment each code block between the dashed lines to run the planner.
# --------------------------------------------------------------
# Compute open loop speed estimate.
open_loop_speed = lp._velocity_planner.get_open_loop_speed(current_timestamp - prev_timestamp)
# Calculate the goal state set in the local frame for the local planner.
# Current speed should be open loop for the velocity profile generation.
ego_state = [current_x, current_y, current_yaw, open_loop_speed]
# Set lookahead based on current speed.
bp.set_lookahead(BP_LOOKAHEAD_BASE + BP_LOOKAHEAD_TIME * open_loop_speed)
# Perform a state transition in the behavioural planner.
bp.transition_state(waypoints, ego_state, current_speed)
# Check to see if we need to follow the lead vehicle.
bp.check_for_lead_vehicle(ego_state, lead_car_pos[1])
# Compute the goal state set from the behavioural planner's computed goal state.
goal_state_set = lp.get_goal_state_set(bp._goal_index, bp._goal_state, waypoints, ego_state)
# Calculate planned paths in the local frame.
paths, path_validity = lp.plan_paths(goal_state_set)
# Transform those paths back to the global frame.
paths = local_planner.transform_paths(paths, ego_state)
# Perform collision checking.
collision_check_array = lp._collision_checker.collision_check(paths, [parkedcar_box_pts])
# Compute the best local path.
best_index = lp._collision_checker.select_best_path_index(paths, collision_check_array, bp._goal_state)
# If no path was feasible, continue to follow the previous best path.
if best_index == None:
best_path = lp._prev_best_path
else:
best_path = paths[best_index]
lp._prev_best_path = best_path
# Compute the velocity profile for the path, and compute the waypoints.
# Use the lead vehicle to inform the velocity profile's dynamic obstacle handling.
# In this scenario, the only dynamic obstacle is the lead vehicle at index 1.
desired_speed = bp._goal_state[2]
lead_car_state = [lead_car_pos[1][0], lead_car_pos[1][1], lead_car_speed[1]]
decelerate_to_stop = bp._state == behavioural_planner.DECELERATE_TO_STOP
local_waypoints = lp._velocity_planner.compute_velocity_profile(best_path, desired_speed, ego_state, current_speed, decelerate_to_stop, lead_car_state, bp._follow_lead_vehicle)
# --------------------------------------------------------------
if local_waypoints != None:
# Update the controller waypoint path with the best local path.
# This controller is similar to that developed in Course 1 of this
# specialization. Linear interpolation computation on the waypoints
# is also used to ensure a fine resolution between points.
wp_distance = [] # distance array
local_waypoints_np = np.array(local_waypoints)
for i in range(1, local_waypoints_np.shape[0]):
wp_distance.append(
np.sqrt((local_waypoints_np[i, 0] - local_waypoints_np[i-1, 0])**2 +
(local_waypoints_np[i, 1] - local_waypoints_np[i-1, 1])**2))
wp_distance.append(0) # last distance is 0 because it is the distance
# from the last waypoint to the last waypoint
# Linearly interpolate between waypoints and store in a list
wp_interp = [] # interpolated values
# (rows = waypoints, columns = [x, y, v])
for i in range(local_waypoints_np.shape[0] - 1):
# Add original waypoint to interpolated waypoints list (and append
# it to the hash table)
wp_interp.append(list(local_waypoints_np[i]))
# Interpolate to the next waypoint. First compute the number of
# points to interpolate based on the desired resolution and
# incrementally add interpolated points until the next waypoint
# is about to be reached.
num_pts_to_interp = int(np.floor(wp_distance[i] /\
float(INTERP_DISTANCE_RES)) - 1)
wp_vector = local_waypoints_np[i+1] - local_waypoints_np[i]
wp_uvector = wp_vector / np.linalg.norm(wp_vector[0:2])
for j in range(num_pts_to_interp):
next_wp_vector = INTERP_DISTANCE_RES * float(j+1) * wp_uvector
wp_interp.append(list(local_waypoints_np[i] + next_wp_vector))
# add last waypoint at the end
wp_interp.append(list(local_waypoints_np[-1]))
# Update the other controller values and controls
controller.update_waypoints(wp_interp)
pass
###
# Controller Update
###
if local_waypoints != None and local_waypoints != []:
controller.update_values(current_x, current_y, current_yaw,
current_speed,
current_timestamp, frame)
controller.update_controls()
cmd_throttle, cmd_steer, cmd_brake = controller.get_commands()
else:
cmd_throttle = 0.0
cmd_steer = 0.0
cmd_brake = 0.0
# Skip the first frame or if there exists no local paths
if skip_first_frame and frame == 0:
pass
elif local_waypoints == None:
pass
else:
# Update live plotter with new feedback
trajectory_fig.roll("trajectory", current_x, current_y)
trajectory_fig.roll("car", current_x, current_y)
if lead_car_pos: # If there exists a lead car, plot it
trajectory_fig.roll("leadcar", lead_car_pos[1][0],
lead_car_pos[1][1])
forward_speed_fig.roll("forward_speed",
current_timestamp,
current_speed)
forward_speed_fig.roll("reference_signal",
current_timestamp,
controller._desired_speed)
throttle_fig.roll("throttle", current_timestamp, cmd_throttle)
brake_fig.roll("brake", current_timestamp, cmd_brake)
steer_fig.roll("steer", current_timestamp, cmd_steer)
# Local path plotter update
if frame % LP_FREQUENCY_DIVISOR == 0:
path_counter = 0
for i in range(NUM_PATHS):
# If a path was invalid in the set, there is no path to plot.
if path_validity[i]:
# Colour paths according to collision checking.
if not collision_check_array[path_counter]:
colour = 'r'
elif i == best_index:
colour = 'k'
else:
colour = 'b'
trajectory_fig.update("local_path " + str(i), paths[path_counter][0], paths[path_counter][1], colour)
path_counter += 1
else:
trajectory_fig.update("local_path " + str(i), [ego_state[0]], [ego_state[1]], 'r')
# When plotting lookahead path, only plot a number of points
# (INTERP_MAX_POINTS_PLOT amount of points). This is meant
# to decrease load when live plotting
wp_interp_np = np.array(wp_interp)
path_indices = np.floor(np.linspace(0,
wp_interp_np.shape[0]-1,
INTERP_MAX_POINTS_PLOT))
trajectory_fig.update("selected_path",
wp_interp_np[path_indices.astype(int), 0],
wp_interp_np[path_indices.astype(int), 1],
new_colour=[1, 0.5, 0.0])
# Refresh the live plot based on the refresh rate
# set by the options
if enable_live_plot and \
live_plot_timer.has_exceeded_lap_period():
lp_traj.refresh()
lp_1d.refresh()
live_plot_timer.lap()
# Output controller command to CARLA server
send_control_command(client,
throttle=cmd_throttle,
steer=cmd_steer,
brake=cmd_brake)
# Find if reached the end of waypoint. If the car is within
# DIST_THRESHOLD_TO_LAST_WAYPOINT to the last waypoint,
# the simulation will end.
dist_to_last_waypoint = np.linalg.norm(np.array([
waypoints[-1][0] - current_x,
waypoints[-1][1] - current_y]))
if dist_to_last_waypoint < DIST_THRESHOLD_TO_LAST_WAYPOINT:
reached_the_end = True
if reached_the_end:
break
# End of demo - Stop vehicle and Store outputs to the controller output
# directory.
if reached_the_end:
print("Reached the end of path. Writing to controller_output...")
else:
print("Exceeded assessment time. Writing to controller_output...")
# Stop the car
send_control_command(client, throttle=0.0, steer=0.0, brake=1.0)
# Store the various outputs
store_trajectory_plot(trajectory_fig.fig, 'trajectory.png')
store_trajectory_plot(forward_speed_fig.fig, 'forward_speed.png')
store_trajectory_plot(throttle_fig.fig, 'throttle_output.png')
store_trajectory_plot(brake_fig.fig, 'brake_output.png')
store_trajectory_plot(steer_fig.fig, 'steer_output.png')
write_trajectory_file(x_history, y_history, speed_history, time_history,
collided_flag_history)
write_collisioncount_file(collided_flag_history)
def main():
"""Main function.
Args:
-v, --verbose: print debug information
--host: IP of the host server (default: localhost)
-p, --port: TCP port to listen to (default: 2000)
-a, --autopilot: enable autopilot
-q, --quality-level: graphics quality level [Low or Epic]
-i, --images-to-disk: save images to disk
-c, --carla-settings: Path to CarlaSettings.ini file
"""
argparser = argparse.ArgumentParser(description=__doc__)
argparser.add_argument(
'-v', '--verbose',
action='store_true',
dest='debug',
help='print debug information')
argparser.add_argument(
'--host',
metavar='H',
default='localhost',
help='IP of the host server (default: localhost)')
argparser.add_argument(
'-p', '--port',
metavar='P',
default=2000,
type=int,
help='TCP port to listen to (default: 2000)')
argparser.add_argument(
'-a', '--autopilot',
action='store_true',
help='enable autopilot')
argparser.add_argument(
'-q', '--quality-level',
choices=['Low', 'Epic'],
type=lambda s: s.title(),
default='Low',
help='graphics quality level.')
argparser.add_argument(
'-c', '--carla-settings',
metavar='PATH',
dest='settings_filepath',
default=None,
help='Path to a "CarlaSettings.ini" file')
args = argparser.parse_args()
# Logging startup info
log_level = logging.DEBUG if args.debug else logging.INFO
logging.basicConfig(format='%(levelname)s: %(message)s', level=log_level)
logging.info('listening to server %s:%s', args.host, args.port)
args.out_filename_format = '_out/episode_{:0>4d}/{:s}/{:0>6d}'
# Execute when server connection is established
while True:
try:
exec_waypoint_nav_demo(args)
print('Done.')
return
except TCPConnectionError as error:
logging.error(error)
time.sleep(1)
if __name__ == '__main__':
try:
main()
except KeyboardInterrupt:
print('\nCancelled by user. Bye!')