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CARLA Benchmark
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===============
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Running the Benchmark
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-------------------------
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The "carla" api provides a basic benchmarking system, that allows making
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several tests on a certain agent. We already provide the same benchmark
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used in the CoRL 2017 paper. By running this benchmark you can compare
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the results of your agent to the results obtained by the agents
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show in the paper.
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The basic functionality requires only the protobuf module to be installed
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$ sudo apt-get install python3 python3-pip
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$ sudo pip3 install protobuf
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However, other operations as handling images require some extra modules, and the
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"manual_control.py" example requires pygame
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$ sudo pip3 install numpy Pillow pygame
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The script "PythonClient/client_example.py" provides basic functionality for
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controlling the vehicle and saving images to disk. Run the help command to see
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options available
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$ ./client_example.py --help
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The script "PythonClient/manual_control.py" launches a PyGame window with
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several views and allows to control the vehicle using the WASD keys.
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$ ./manual_control.py --help
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import math
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import matplotlib.pyplot as plt
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import numpy as np
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from matplotlib import collections as mc
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def string_to_node(string):
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vec = string.split(',')
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return self._nodes
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def plot_ori(self, c):
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from matplotlib import collections as mc
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import matplotlib.pyplot as plt
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line_len = 1
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lines = [[(p[0], p[1]), (p[0] + line_len * self._angles[p][0],
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plt.scatter(xs, ys, color=c)
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def plot(self, c):
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import matplotlib.pyplot as plt
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xs = [p[0] for p in self._nodes]
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ys = [p[1] for p in self._nodes]
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@ -54,7 +54,6 @@ class Planner(object):
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:return: a command ( Straight,Lane Follow, Left or Right)
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"""
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track_source = self._city_track.project_node(source, source_ori)
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track_target = self._city_track.project_node(target, target_ori)
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