def run_scenario(self):
global base_filename, default_model, default_color, default_scenario, setpoint
global prev_error
#vehicle_loadfile = 'vehicles/pickup/pristine.save.json'
# setup DNN model + weights
m = Model()
model = m.define_model_BeamNG("BeamNGmodel-4.h5")
random.seed(1703)
setup_logging()
#beamng = BeamNGpy('localhost', 64256, home='C:/Users/merie/Documents/BeamNG.research.v1.7.0.1')
beamng = BeamNGpy('localhost', 64256, home='H:/BeamNG.research.v1.7.0.1clean')
scenario = Scenario(default_scenario, 'research_test')
vehicle = Vehicle('ego_vehicle', model=default_model,
licence='LOWPRESS', color=default_color)
vehicle = setup_sensors(vehicle)
spawn = spawn_point(default_scenario, 'highway')
scenario.add_vehicle(vehicle, pos=spawn['pos'], rot=None, rot_quat=spawn['rot_quat'])
# Compile the scenario and place it in BeamNG's map folder
scenario.make(beamng)
# Start BeamNG and enter the main loop
bng = beamng.open(launch=True)
bng.hide_hud()
bng.set_deterministic() # Set simulator to be deterministic
bng.set_steps_per_second(100) # With 60hz temporal resolution
# Load and start the scenario
bng.load_scenario(scenario)
bng.start_scenario()
# perturb vehicle
#vehicle.ai_set_mode('span')
#vehicle.ai_drive_in_lane(True)
#vehicle_loadfile = 'vehicles/etk800/fronttires_0psi.pc'
# vehicle_loadfile = 'vehicles/etk800/backtires_0psi.pc'
# vehicle_loadfile = 'vehicles/etk800/chassis_forcefeedback201.pc'
# vehicle.load_pc(vehicle_loadfile, False)
vehicle.deflate_tires([1,1,1,1])
#vehicle.break_all_breakgroups()
#vehicle.break_hinges()
# Put simulator in pause awaiting further inputs
bng.pause()
assert vehicle.skt
bng.resume()
wheelspeed = 0.0; throttle = 0.0; prev_error = setpoint; damage_prev = None; runtime = 0.0
kphs = []
damage = None
final_img = None
# Send random inputs to vehice and advance the simulation 20 steps
for _ in range(1024):
# collect images
image = bng.poll_sensors(vehicle)['front_cam']['colour'].convert('RGB')
img = m.process_image(image)
prediction = model.predict(img)
# control params
kph = ms_to_kph(wheelspeed)
throttle = throttle_PID(kph, dt)
brake = 0
#if throttle < 0:
if setpoint < kph:
brake = throttle / 1000.0
throttle = 0.0
# throttle = 0.2 # random.uniform(0.0, 1.0)
# brake = random.choice([0, 0, 0.1 , 0.2])
steering = float(prediction[0][0]) #random.uniform(-1.0, 1.0)
vehicle.control(throttle=throttle, steering=steering, brake=brake)
steering_state = bng.poll_sensors(vehicle)['electrics']['steering']
steering_input = bng.poll_sensors(vehicle)['electrics']['steering_input']
avg_wheel_av = bng.poll_sensors(vehicle)['electrics']['avg_wheel_av']
wheelspeed = bng.poll_sensors(vehicle)['electrics']['wheelspeed']
damage = bng.poll_sensors(vehicle)['damage']
new_damage = diff_damage(damage, damage_prev)
damage_prev = damage
print("\n")
# #print("steering state: {}".format(steering_state))
# print("AI steering_input: {}".format(steering_input))
#print("avg_wheel_av: {}".format(avg_wheel_av))
# print("DAVE2 steering prediction: {}".format(float(prediction[0][0])))
print("throttle:{}".format(throttle))
print("brake:{}".format(brake))
print("kph: {}".format(ms_to_kph(wheelspeed)))
print("new_damage:{}".format(new_damage))
kphs.append(ms_to_kph(wheelspeed))
if new_damage > 0.0:
final_img = image
break
bng.step(5)
runtime += (0.05)
# print("runtime:{}".format(round(runtime, 2)))
# print("time to crash:{}".format(round(runtime, 2)))
bng.close()
avg_kph = float(sum(kphs)) / len(kphs)
plt.imshow(final_img)
plt.pause(0.01)
return runtime, avg_kph, damage['damage'], kphs
def run_spawn_for_deflation(vehicle_model='etk800',
deflation_pattern=[0, 0, 0, 0]):
global base_filename, default_color, default_scenario, setpoint
global prev_error
global fcam
vehicle_loadfile = 'vehicles/hopper/crawler.pc'
# setup DNN model + weights
m = Model()
model = m.define_model_BeamNG("BeamNGmodel-5.h5")
random.seed(1703)
setup_logging()
# beamng = BeamNGpy('localhost', 64256, home='C:/Users/merie/Documents/BeamNG.research.v1.7.0.1')
beamng = BeamNGpy('localhost',
64256,
home='H:/BeamNG.research.v1.7.0.1clean')
scenario = Scenario(default_scenario, 'research_test')
vehicle = Vehicle('ego_vehicle',
model=vehicle_model,
licence='LOWPRESS',
color=default_color)
vehicle = setup_sensors(vehicle)
spawn = spawn_point(default_scenario, 'highway')
# scenario.add_vehicle(vehicle, pos=spawn['pos'], rot=None, rot_quat=spawn['rot_quat'])
# Compile the scenario and place it in BeamNG's map folder
scenario.make(beamng)
# Start BeamNG and enter the main loop
bng = beamng.open(launch=True)
# bng.hide_hud()
bng.set_deterministic() # Set simulator to be deterministic
# Load and start the scenario
bng.load_scenario(scenario)
bng.start_scenario()
# load part config
#pc = vehicle.get_part_config()
# loadfile = 'C:/Users/merie/Documents/BeamNG.research/vehicles/hopper/front17x8rear17x9.json'
# vehicle.load(loadfile)
bng.spawn_vehicle(vehicle,
pos=spawn['pos'],
rot=None,
rot_quat=spawn['rot_quat'],
partConfig='vehicles/hopper/custom.pc')
#bng.set_relative_camera()
# Put simulator in pause awaiting further inputs
bng.pause()
assert vehicle.skt
# vehicle.control(throttle=0.0, steering=0.0, brake=1.0)
# perturb vehicle
# before_state = copy.deepcopy(vehicle.state)
before_state = bng.poll_sensors(vehicle)['front_cam']
before_state['vel'] = vehicle.state['vel']
before_state['roll'] = vehicle.state['roll']
before_state['pitch'] = vehicle.state['pitch']
before_state['yaw'] = vehicle.state['yaw']
# print("vehicle position before deflation via beamstate:{}".format(vehicle.get_object_position()))
# print("vehicle position before deflation via vehicle state:{}".format(vehicle.state))
print(
"vehicle position before deflation via camera:{}".format(before_state))
image = bng.poll_sensors(vehicle)['front_cam']['colour'].convert('RGB')
# print("camera sensor before deflation: {}".format(bng.poll_sensors(vehicle)['front_cam']))
#get_camera_rot_and_pos([-0.3, 1, 1.0], [0, 0.75, 0], before_state['pos'], before_state['dir'], before_state['up'])
img = m.process_image(image)
before_prediction = model.predict(img)
before_state["prediction"] = before_prediction
plt.imshow(image)
plt.pause(0.01)
if deflation_pattern != [0, 0, 0, 0]:
print("deflating according to pattern {}".format(deflation_pattern))
vehicle.deflate_tires(deflation_pattern)
time.sleep(1)
bng.resume()
bng.set_steps_per_second(100)
bng.set_deterministic()
totalsecs = 0.0
deflation_traj = []
while totalsecs <= 30:
vehicle.update_vehicle()
# vehicle.control(throttle=0.0, steering=0.0, brake=1.0)
after_state = bng.poll_sensors(vehicle)['front_cam']
after_state['vel'] = vehicle.state['vel']
after_state['roll'] = vehicle.state['roll']
after_state['pitch'] = vehicle.state['pitch']
after_state['yaw'] = vehicle.state['yaw']
print("roll:{}, pitch:{}, yaw:{}".format(vehicle.state['roll'],
vehicle.state['pitch'],
vehicle.state['yaw']))
deflation_traj.append(round(math.degrees(vehicle.state['pitch'][0]),
4))
bng.step(100)
totalsecs += 1
# after_state = copy.deepcopy(vehicle.state)
# print("vehicle position after deflation via beamstate:{}".format(vehicle.get_object_position()))
# print("vehicle position after deflation via vehicle state:{}".format(vehicle.state))
image = bng.poll_sensors(vehicle)['front_cam']['colour'].convert('RGB')
print("vehicle position after deflation via camera:{}".format(after_state))
#print("camera sensor output: {}".format(bng.poll_sensors(vehicle)['front_cam']['rot']))
#print("camera pos output: {}".format(bng.poll_sensors(vehicle)['front_cam']['pos']))
# print("camera rot output: {}".format(bng.poll_sensors(vehicle)['front_cam']['direction']))
# print("fcam.encode_engine_request() = {}".format(fcam.encode_engine_request()))
damages = bng.poll_sensors(vehicle)['damage']['deform_group_damage']
d = ["{}={}".format(k, damages[k]['damage']) for k in damages.keys()]
print("vehicle pressure after deflation: lowpressure={} damages:".format(
bng.poll_sensors(vehicle)['damage']['lowpressure'], d))
img = m.process_image(image)
after_state["prediction"] = model.predict(img)
plt.imshow(image)
plt.pause(0.01)
bng.close()
return before_state, after_state, deflation_traj
def main():
random.seed(1703)
setup_logging()
# Plotting code setting up a 3x2 figure
# fig = plt.figure(1, figsize=(10, 5))
# axarr = fig.subplots(2, 3)
# a_colour = axarr[0, 0]
# b_colour = axarr[1, 0]
# a_depth = axarr[0, 1]
# b_depth = axarr[1, 1]
# a_annot = axarr[0, 2]
# b_annot = axarr[1, 2]
# plt.ion()
#beamng = BeamNGpy('localhost', 64256, home='C:/Users/merie/Documents/BeamNG.research.v1.7.0.1')
beamng = BeamNGpy('localhost', 64256, home='H:/BeamNG.research.v1.7.0.1')
# Create a scenario in west_coast_usa
scenario = Scenario('west_coast_usa', 'research_test')
# Set up first vehicle, with two cameras, gforces sensor, lidar, electrical
# sensors, and damage sensors
vehicle = Vehicle('ego_vehicle',
model='etk800',
licence='RED',
color='Red')
# Set up sensors
pos = (-0.3, 1, 1.0)
direction = (0, 1, 0)
fov = 120
resolution = (512, 512)
front_camera = Camera(pos,
direction,
fov,
resolution,
colour=True,
depth=True,
annotation=True)
pos = (0.0, 3, 1.0)
direction = (0, -1, 0)
fov = 90
resolution = (512, 512)
back_camera = Camera(pos,
direction,
fov,
resolution,
colour=True,
depth=True,
annotation=True)
gforces = GForces()
electrics = Electrics()
damage = Damage()
#lidar = Lidar(visualized=False)
timer = Timer()
# Attach them
vehicle.attach_sensor('front_cam', front_camera)
vehicle.attach_sensor('back_cam', back_camera)
vehicle.attach_sensor('gforces', gforces)
vehicle.attach_sensor('electrics', electrics)
vehicle.attach_sensor('damage', damage)
vehicle.attach_sensor('timer', timer)
scenario.add_vehicle(vehicle,
pos=(-717.121, 101, 118.675),
rot=None,
rot_quat=(0, 0, 0.3826834, 0.9238795))
# Compile the scenario and place it in BeamNG's map folder
scenario.make(beamng)
# Start BeamNG and enter the main loop
bng = beamng.open(launch=True)
try:
bng.hide_hud()
bng.set_deterministic() # Set simulator to be deterministic
bng.set_steps_per_second(60) # With 60hz temporal resolution
# Load and start the scenario
bng.load_scenario(scenario)
bng.start_scenario()
# Put simulator in pause awaiting further inputs
bng.pause()
assert vehicle.skt
vehicle.deflate_tires([1, 1, 1, 1])
# Send random inputs to vehice and advance the simulation 20 steps
for _ in range(1024):
throttle = random.uniform(0.0, 1.0)
steering = random.uniform(-1.0, 1.0)
brake = random.choice([0, 0, 0, 1])
vehicle.control(throttle=throttle, steering=steering, brake=brake)
bng.step(20)
# Retrieve sensor data and show the camera data.
sensors = bng.poll_sensors(vehicle)
print('{} seconds passed.'.format(sensors['timer']['time']))
# a_colour.imshow(sensors['front_cam']['colour'].convert('RGB'))
# a_depth.imshow(sensors['front_cam']['depth'].convert('L'))
# a_annot.imshow(sensors['front_cam']['annotation'].convert('RGB'))
#
# b_colour.imshow(sensors['back_cam']['colour'].convert('RGB'))
# b_depth.imshow(sensors['back_cam']['depth'].convert('L'))
# b_annot.imshow(sensors['back_cam']['annotation'].convert('RGB'))
# plt.pause(0.00001)
finally:
bng.close()
def run_scenario(vehicle_model='etk800',
deflation_pattern=[0, 0, 0, 0],
parts_config='vehicles/hopper/custom.pc',
run_number=0):
global base_filename, default_color, default_scenario, default_spawnpoint, steps_per_sec
global integral, prev_error, setpoint
integral = 0.0
prev_error = 0.0
# setup DNN model + weights
sm = DAVE2Model()
# steering_model = Model().define_model_BeamNG("BeamNGmodel-racetracksteering8.h5")
# throttle_model = Model().define_model_BeamNG("BeamNGmodel-racetrackthrottle8.h5")
dual_model = sm.define_dual_model_BeamNG()
# dual_model = sm.load_weights("BeamNGmodel-racetrackdualcomparison10K.h5")
# dual_model = sm.define_multi_input_model_BeamNG()
# dual_model = sm.load_weights("BeamNGmodel-racetrackdual-comparison10K-PIDcontrolset-4.h5")
# dual_model = sm.load_weights("BeamNGmodel-racetrackdual-comparison40K-PIDcontrolset-1.h5")
# BeamNGmodel-racetrack-multiinput-dualoutput-comparison10K-PIDcontrolset-1.h5
# BeamNGmodel-racetrackdual-comparison100K-PIDcontrolset-2
dual_model = sm.load_weights(
"BeamNGmodel-racetrackdual-comparison100K-PIDcontrolset-2.h5")
# dual_model = sm.load_weights("BeamNGmodel-racetrack-multiinput-dualoutput-comparison103K-PIDcontrolset-1.h5")
random.seed(1703)
setup_logging()
beamng = BeamNGpy('localhost',
64256,
home='H:/BeamNG.research.v1.7.0.1clean',
user='******')
scenario = Scenario(default_scenario, 'research_test')
vehicle = Vehicle('ego_vehicle',
model=vehicle_model,
licence='EGO',
color=default_color)
vehicle = setup_sensors(vehicle)
spawn = spawn_point(default_scenario, default_spawnpoint)
scenario.add_vehicle(
vehicle, pos=spawn['pos'], rot=None,
rot_quat=spawn['rot_quat']) #, partConfig=parts_config)
add_barriers(scenario)
# Compile the scenario and place it in BeamNG's map folder
scenario.make(beamng)
# Start BeamNG and enter the main loop
bng = beamng.open(launch=True)
#bng.hide_hud()
bng.set_deterministic() # Set simulator to be deterministic
bng.set_steps_per_second(steps_per_sec) # With 60hz temporal resolution
bng.load_scenario(scenario)
bng.start_scenario()
# bng.spawn_vehicle(vehicle, pos=spawn['pos'], rot=None, rot_quat=spawn['rot_quat'], partConfig=parts_config)
# Put simulator in pause awaiting further inputs
bng.pause()
assert vehicle.skt
# bng.resume()
# perturb vehicle
print("vehicle position before deflation via beamstate:{}".format(
vehicle.get_object_position()))
print("vehicle position before deflation via vehicle state:{}".format(
vehicle.state))
image = bng.poll_sensors(vehicle)['front_cam']['colour'].convert('RGB')
# plt.imshow(image)
# plt.pause(0.01)
vehicle.deflate_tires(deflation_pattern)
bng.step(steps_per_sec * 6)
vehicle.update_vehicle()
# print("vehicle position after deflation via beamstate:{}".format(vehicle.get_object_position()))
# print("vehicle position after deflation via vehicle state:{}".format(vehicle.state))
pitch = vehicle.state['pitch'][0]
roll = vehicle.state['roll'][0]
z = vehicle.state['pos'][2]
image = bng.poll_sensors(vehicle)['front_cam']['colour'].convert('RGB')
# plt.imshow(image)
# plt.pause(0.01)
# bng.resume()
# vehicle.break_all_breakgroups()
# vehicle.break_hinges()
wheelspeed = 0.0
throttle = 0.0
prev_error = setpoint
damage_prev = None
runtime = 0.0
kphs = []
traj = []
pitches = []
rolls = []
steering_inputs = []
throttle_inputs = []
timestamps = []
damage = None
final_img = None
# Send random inputs to vehice and advance the simulation 20 steps
overall_damage = 0.0
total_loops = 0
total_imgs = 0
total_predictions = 0
while overall_damage <= 0:
# collect images
sensors = bng.poll_sensors(vehicle)
image = sensors['front_cam']['colour'].convert('RGB')
# plt.imshow(image)
# plt.pause(0.01)
total_imgs += 1
img = sm.process_image(np.asarray(image))
wheelspeed = sensors['electrics']['wheelspeed']
kph = ms_to_kph(wheelspeed)
dual_prediction = dual_model.predict(x=[img, np.array([kph])])
# steering_prediction = steering_model.predict(img)
# throttle_prediction = throttle_model.predict(img)
dt = sensors['timer']['time'] - runtime
runtime = sensors['timer']['time']
# control params
brake = 0
# steering = float(steering_prediction[0][0]) #random.uniform(-1.0, 1.0)
# throttle = float(throttle_prediction[0][0])
steering = float(dual_prediction[0][0]) #random.uniform(-1.0, 1.0)
throttle = float(dual_prediction[0][1])
total_predictions += 1
if abs(steering) > 0.2:
setpoint = 20
else:
setpoint = 40
# if runtime < 10:
throttle = throttle_PID(kph, dt)
# if throttle > 1:
# throttle = 1
# if setpoint < kph:
# brake = 0.0 #throttle / 10000.0
# throttle = 0.0
vehicle.control(throttle=throttle, steering=steering, brake=brake)
steering_inputs.append(steering)
throttle_inputs.append(throttle)
timestamps.append(runtime)
steering_state = sensors['electrics']['steering']
steering_input = sensors['electrics']['steering_input']
avg_wheel_av = sensors['electrics']['avg_wheel_av']
damage = sensors['damage']
overall_damage = damage["damage"]
new_damage = diff_damage(damage, damage_prev)
damage_prev = damage
vehicle.update_vehicle()
traj.append(vehicle.state['pos'])
pitches.append(vehicle.state['pitch'][0])
rolls.append(vehicle.state['roll'][0])
kphs.append(ms_to_kph(wheelspeed))
total_loops += 1
if new_damage > 0.0:
final_img = image
break
bng.step(1, wait=True)
if runtime > 300:
print("Exited after 5 minutes successful runtime")
break
if distance(
spawn['pos'],
vehicle.state['pos']) < 5 and sensors['timer']['time'] > 10:
reached_start = True
break
# print("runtime:{}".format(round(runtime, 2)))
# print("time to crash:{}".format(round(runtime, 2)))
bng.close()
# avg_kph = float(sum(kphs)) / len(kphs)
plt.imshow(final_img)
plt.savefig("Run-{}-finalimg.png".format(run_number))
plt.pause(0.01)
plot_input(timestamps, steering_inputs, "Steering", run_number=run_number)
plot_input(timestamps, throttle_inputs, "Throttle", run_number=run_number)
plot_input(timestamps, kphs, "KPHs", run_number=run_number)
print("Number of steering_inputs:", len(steering_inputs))
print("Number of throttle inputs:", len(throttle_inputs))
results = "Total loops: {} \ntotal images: {} \ntotal predictions: {} \nexpected predictions ={}*{}={}".format(
total_loops, total_imgs, total_predictions, round(runtime, 3),
steps_per_sec, round(runtime * steps_per_sec, 3))
print(results)
results = {
'runtime': round(runtime, 3),
'damage': damage,
'kphs': kphs,
'traj': traj,
'pitch': round(pitch, 3),
'roll': round(roll, 3),
"z": round(z, 3),
'final_img': final_img,
'total_predictions': total_predictions,
'expected_predictions': round(runtime * steps_per_sec, 3)
}
return results
def run_scenario(vehicle_model='etk800',
deflation_pattern=[0, 0, 0, 0],
parts_config=None):
global base_filename, default_color, default_scenario, default_spawnpoint, setpoint, steps_per_sec
global integral, prev_error
integral = 0.0
prev_error = 0.0
# setup DNN model + weights
# m = Model()
# model = m.define_model_BeamNG("BeamNGmodel-racetrack.h5")
random.seed(1703)
setup_logging()
beamng = BeamNGpy('localhost',
64256,
home='H:/BeamNG.research.v1.7.0.1clean')
scenario = Scenario(default_scenario, 'research_test')
# unperturbed_vehicle = Vehicle('unperturbed_vehicle', model=vehicle_model,
# licence='SAFE', color='Red')
# unperturbed_vehicle = setup_sensors(unperturbed_vehicle)
vehicle = Vehicle('ego_vehicle',
model=vehicle_model,
licence='EGO',
color=default_color)
vehicle = setup_sensors(vehicle)
spawn = get_spawn_point(default_scenario, default_spawnpoint)
scenario.add_vehicle(vehicle,
pos=spawn['pos'],
rot=None,
rot_quat=spawn['rot_quat'])
temp = copy.deepcopy(spawn['pos'])
temp = [temp[0] + lanewidth, temp[1] + lanewidth, temp[2]]
# scenario.add_vehicle(unperturbed_vehicle, pos=temp, rot=None, rot_quat=spawn['rot_quat'])
# Compile the scenario and place it in BeamNG's map folder
scenario.make(beamng)
bng = beamng.open(launch=True)
#bng.hide_hud()
bng.set_deterministic() # Set simulator to be deterministic
bng.set_steps_per_second(steps_per_sec)
# Load and start the scenario
bng.load_scenario(scenario)
bng.start_scenario()
#bng.spawn_vehicle(vehicle, pos=spawn['pos'], rot=None, rot_quat=spawn['rot_quat'], partConfig=parts_config)
# Put simulator in pause awaiting further inputs
bng.pause()
assert vehicle.skt
bng.resume()
# perturb vehicle
vehicle.deflate_tires(deflation_pattern)
bng.step(steps_per_sec * 6)
vehicle.update_vehicle()
damage = 0
runtime = 0
while damage <= 0:
sensors = bng.poll_sensors(vehicle)
headlight_img = sensors['headlight_cam']['colour'].convert('RGB')
# sensors['']
# prediction = model.predict()
# steering = float(prediction[0][0]) #random.uniform(-1.0, 1.0)
# vehicle.control(throttle=throttle, steering=steering, brake=brake)
return_str = '\nPERTURBED headlight_cam INFO:'
print('\nPERTURBED headlight_cam INFO:')
temp = bng.poll_sensors(vehicle)['headlight_cam']
for key in temp:
if key == 'rotation':
degs = euler_from_quaternion(temp[key][0], temp[key][1],
temp[key][2], temp[key][3])
return_str = "{}\nquaternions {}".format(return_str, temp[key])
return_str = "{}\n{} {}".format(return_str, key,
[round(i, 3) for i in degs])
print(key, degs)
elif key != "colour" and key != "annotation" and key != "depth":
return_str = "{}\n{} {}".format(return_str, key, temp[key])
print(key, temp[key])
print('\nPERTURBED rearview_cam INFO:')
return_str = "{}\nPERTURBED rearview_cam INFO:".format(return_str)
# temp = bng.poll_sensors(vehicle)['rearview_cam']
for key in temp:
if key == 'rotation':
degs = euler_from_quaternion(temp[key][0], temp[key][1],
temp[key][2], temp[key][3])
return_str = "{}\nquaternions {}".format(return_str, temp[key])
return_str = "{}\n{} {}".format(return_str, key,
[round(i, 3) for i in degs])
print(key, degs)
elif key != "colour" and key != "annotation" and key != "depth":
return_str = "{}\n{} {}".format(return_str, key, temp[key])
print(key, temp[key])
# rearview_img = bng.poll_sensors(vehicle)['rearview_cam']['colour'].convert('RGB')
headlight_img = sensors['headlight_cam']['colour'].convert('RGB')
bng.step(1)
damage = sensors['damage']['damage']
runtime = sensors['timer']['time']
# print("runtime:{}".format(round(runtime, 2)))
print("time to crash:{} damage:{}".format(round(runtime, 2), damage))
bng.close()
# avg_kph = float(sum(kphs)) / len(kphs)
# plt.imshow(rearview_img)
# plt.pause(0.01)
plt.imshow(headlight_img)
plt.pause(0.01)
# results = {'pitch': round(pitch,3), 'roll':round(roll,3), "z":round(z,3), 'rearview_img':rearview_img, 'headlight_img':headlight_img}
return return_str
def run_scenario_ai_version(vehicle_model='etk800',
deflation_pattern=[0, 0, 0, 0],
parts_config=None):
global base_filename, default_color, default_scenario, default_spawnpoint, setpoint, steps_per_sec
global prev_error
random.seed(1703)
setup_logging()
beamng = BeamNGpy('localhost',
64256,
home='H:/BeamNG.research.v1.7.0.1clean',
user="******")
scenario = Scenario(default_scenario, 'research_test')
vehicle = Vehicle('ego_vehicle',
model=vehicle_model,
licence='AI',
color=default_color)
vehicle = setup_sensors(vehicle)
spawn = get_spawn_point(default_scenario, default_spawnpoint)
# scenario.add_vehicle(vehicle, pos=spawn['pos'], rot=None, rot_quat=spawn['rot_quat'])
# Compile the scenario and place it in BeamNG's map folder
scenario.make(beamng)
# Start BeamNG and enter the main loop
bng = beamng.open(launch=True)
bng.set_deterministic() # Set simulator to be deterministic
bng.set_steps_per_second(steps_per_sec) # With 100hz temporal resolution
# Load and start the scenario
bng.load_scenario(scenario)
bng.start_scenario()
# create vehicle to be chased
chase_vehicle = Vehicle('chase_vehicle',
model='miramar',
licence='CHASEE',
color='Red')
bng.spawn_vehicle(chase_vehicle,
pos=(469.784, 346.391, 144.982),
rot=None,
rot_quat=(-0.0037852677050978, -0.0031219546217471,
-0.78478640317917, 0.61974692344666))
bng.spawn_vehicle(vehicle,
pos=spawn['pos'],
rot=None,
rot_quat=spawn['rot_quat'],
partConfig=parts_config)
# Put simulator in pause awaiting further inputs
bng.pause()
assert vehicle.skt
#bng.resume()
# perturb vehicle
print("vehicle position before deflation via beamstate:{}".format(
vehicle.get_object_position()))
print("vehicle position before deflation via vehicle state:{}".format(
vehicle.state))
image = bng.poll_sensors(vehicle)['front_cam']['colour'].convert('RGB')
plt.imshow(image)
plt.pause(0.01)
vehicle.deflate_tires(deflation_pattern)
bng.step(steps_per_sec * 6)
vehicle.update_vehicle()
# print("vehicle position after deflation via beamstate:{}".format(vehicle.get_object_position()))
# print("vehicle position after deflation via vehicle state:{}".format(vehicle.state))
pitch = vehicle.state['pitch'][0]
roll = vehicle.state['roll'][0]
z = vehicle.state['pos'][2]
image = bng.poll_sensors(vehicle)['front_cam']['colour'].convert('RGB')
plt.imshow(image)
plt.pause(0.01)
bng.resume()
vehicle.ai_set_mode('chase')
vehicle.ai_set_target('chase_vehicle')
vehicle.ai_drive_in_lane(True)
damage_prev = None
runtime = 0.0
traj = []
kphs = []
for _ in range(650):
image = bng.poll_sensors(vehicle)['front_cam']['colour'].convert('RGB')
damage = bng.poll_sensors(vehicle)['damage']
wheelspeed = bng.poll_sensors(vehicle)['electrics']['wheelspeed']
new_damage = diff_damage(damage, damage_prev)
damage_prev = damage
runtime = bng.poll_sensors(vehicle)['timer']['time']
vehicle.update_vehicle()
traj.append(vehicle.state['pos'])
kphs.append(ms_to_kph(wheelspeed))
if new_damage > 0.0:
break
bng.step(5)
bng.close()
results = {
'runtime': round(runtime, 3),
'damage': damage,
'kphs': kphs,
'traj': traj,
'pitch': round(pitch, 3),
'roll': round(roll, 3),
"z": round(z, 3),
'final_img': image
}
return results
