def test_gforces(beamng):
with beamng as bng:
scenario = Scenario('west_coast_usa', 'gforce_test')
vehicle = Vehicle('test_car', model='etk800')
gforces = GForces()
vehicle.attach_sensor('gforces', gforces)
scenario.add_vehicle(vehicle,
pos=(-717.121, 101, 118.675),
rot=(0, 0, 45))
scenario.make(beamng)
gx = []
gy = []
bng.load_scenario(scenario)
bng.start_scenario()
bng.step(120)
vehicle.ai_set_aggression(2)
vehicle.ai_set_mode('span')
for _ in range(64):
bng.step(30)
vehicle.poll_sensors()
gx.append(gforces.data['gx'])
gy.append(gforces.data['gy'])
assert np.var(gx) > 1 and np.var(gy) > 1
def setup_beamng(traffic=2):
global sp, beamng_home, beamng_user
setup_logging()
beamng = BeamNGpy('localhost', 64256, home=beamng_home, user=beamng_user)
scenario = Scenario(
'west_coast_usa',
'lidar_tour',
description=
'Tour through highway with variable traffic vehicles gathering '
'Lidar data')
# setup vehicle
vehicle = Vehicle('ego_vehicle',
model='etk800',
licence='LIDAR',
color='Red')
vehicle = setup_sensors(vehicle)
# setup vehicle poses
lane = random.choice([1, 2, 3, 4])
ego_sp = spawn_point(sp, lane)
ego_pos = ego_sp['pos']
ego_rot_quat = ego_sp['rot_quat']
lane = ego_sp['lane']
# add vehicles to scenario
# print("adding vehicle to scenario...")
scenario.add_vehicle(vehicle, pos=ego_pos, rot=None, rot_quat=ego_rot_quat)
tvs = traffic_vehicles(traffic)
ps = generate_vehicle_positions(ego_pos, ego_rot_quat, lane, tvs)
for i in range(len(tvs)):
print("adding vehicle {}...".format(i))
scenario.add_vehicle(tvs[i], pos=ps[i], rot_quat=ego_rot_quat)
print("making scenario...")
scenario.make(beamng)
print("opening beamNG...")
bng = beamng.open(launch=True)
st = time.time()
print("loading scenario...")
bng.load_scenario(scenario)
bng.set_steps_per_second(60)
bng.set_deterministic()
#print("Bounding box: {}".format(vehicle.get_bbox()))
bng.hide_hud()
print("starting scenario...")
bng.start_scenario()
vehicle.ai_set_mode('traffic')
#"DecalRoad31765_8"
vehicle.ai_drive_in_lane(False)
vehicle.ai_set_aggression(2.0)
bng.start_traffic(tvs)
bng.switch_vehicle(vehicle)
bng.pause()
return vehicle, bng
import mmap
def main():
global base_filename, training_dir, default_model
f = setup_dir(training_dir)
spawn_pt = spawn_point(default_scenario)
random.seed(1703)
setup_logging()
beamng = BeamNGpy('localhost', 64256, home='C:/Users/merie/Documents/BeamNG.research.v1.7.0.1')
scenario = Scenario(default_scenario, 'research_test')
vehicle = Vehicle('ego_vehicle', model=default_model,
licence='RED', color='Red')
vehicle = setup_sensors(vehicle)
scenario.add_vehicle(vehicle, pos=spawn_pt['pos'], rot=None, rot_quat=spawn_pt['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)
images = []
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()
vehicle.ai_set_mode('span')
vehicle.ai_drive_in_lane(True)
vehicle.ai_set_aggression(0.1)
# Put simulator in pause awaiting further inputs
bng.pause()
assert vehicle.skt
# Send random inputs to vehice and advance the simulation 20 steps
imagecount = 0
wheelvel = [0.1, 0.1, 0.1]
with open(f, 'w') as datafile:
datafile.write('filename,timestamp,steering_input,throttle_input,brake_input,driveshaft,engine_load,fog_lights,fuel,'
'lowpressure,oil,oil_temperature,parkingbrake,rpm,water_temperature\n')
#for _ in range(1024):
for _ in range(32768):
#throttle = 1.0 #random.uniform(0.0, 1.0)
#steering = random.uniform(-1.0, 1.0)
#brake = random.choice([0, 0, 0, 1])
#vehicle.control(throttle=throttle)
# collect images
sensors = bng.poll_sensors(vehicle)
image = sensors['front_cam']['colour'].convert('RGB')
imagecount += 1
filename = "{}{}.bmp".format(base_filename, imagecount)
# collect ancillary data
datafile.write('{},{},{},{},{},{},{},{},{},{},{},{},{},{}\n'.format(filename,
str(round(sensors['timer']['time'], 2)),
sensors['electrics']['steering_input'],
sensors['electrics']['throttle_input'],
sensors['electrics']['brake_input'],
sensors['electrics']['driveshaft'],
sensors['electrics']['engine_load'],
sensors['electrics']['fog_lights'],
sensors['electrics']['fuel'],
sensors['electrics']['lowpressure'],
sensors['electrics']['oil'],
sensors['electrics']['oil_temperature'],
sensors['electrics']['parkingbrake'],
sensors['electrics']['rpm'],
sensors['electrics']['water_temperature'],))
# save the image
image.save(filename)
# step sim forward
bng.step(20)
print('{} seconds passed.'.format(str(round(sensors['timer']['time'], 2))))
# check for stopping condition
for i in range(len(wheelvel)-1):
wheelvel[i] = wheelvel[i+1]
wheelvel[2] = float(sensors['electrics']['wheelspeed'])
print('wheelvel = {}'.format(sum(wheelvel) / 3.0 ))
if sum(wheelvel) / 3.0 == 0.0:
print("avg wheelspeed is zero; exiting...")
bng.close()
break
def run_scenario(traffic=2, run_number=0):
global sp
setup_logging()
beamng = BeamNGpy('localhost',
64256,
home='C:/Users/merie/Documents/BeamNG.research.v1.7.0.1')
scenario = Scenario('west_coast_usa',
'lidar_tour',
description='Tour through the west coast gathering '
'Lidar data')
vehicle = Vehicle('ego_vehicle',
model='etk800',
licence='LIDAR',
color='Red')
vehicle = setup_sensors(vehicle)
lidar = setup_lidar('hood')
vehicle.attach_sensor('lidar', lidar)
ego_sp = spawn_point(sp)
ego_pos = ego_sp['pos']
ego_rot_quat = ego_sp['rot_quat']
scenario.add_vehicle(vehicle, pos=ego_pos, rot=None, rot_quat=ego_rot_quat)
tvs = traffic_vehicles(traffic)
ps = generate_vehicle_positions(ego_pos, ego_rot_quat, tvs)
for i in range(len(tvs)):
scenario.add_vehicle(tvs[i], pos=ps[i], rot_quat=ego_rot_quat)
scenario.make(beamng)
bng = beamng.open(launch=True)
try:
bng.load_scenario(scenario)
bng.set_steps_per_second(60)
#bng.set_deterministic()
#print("Bounding box: {}".format(vehicle.get_bbox()))
bng.hide_hud()
bng.start_scenario()
vehicle.ai_set_mode('traffic')
vehicle.ai_drive_in_lane(True)
#vehicle.ai_set_speed(16, mode='limit')
#vehicle.ai_set_target('traffic')
vehicle.ai_set_aggression(0.9)
bng.start_traffic(tvs)
bng.switch_vehicle(vehicle)
start = time.time()
end = time.time()
damage_prev = None
with open(
'H:/traffic_traces/traffic_lidar_data_{}traffic_run{}.csv'.
format(traffic, run_number), 'w') as f:
f.write(
"TIMESTAMP,VEHICLE_POSITION,VEHICLE_ORIENTATION,VELOCITY,LIDAR,CRASH,EXTERNAL_VEHICLES \n"
)
for _ in range(1024):
sensors = bng.poll_sensors(vehicle)
points = sensors['lidar']['points']
damage = sensors['damage']
v_state = vehicle.state
#print("vehicle_state = {}".format(v_state))
ai_state = vehicle.ai_get_state()
#print("ai_state = {}".format(ai_state))
new_damage = diff_damage(damage, damage_prev)
damage_prev = damage
if new_damage > 0:
print("new damage = {}".format(new_damage))
f.write("{},{},{},{},{},{},{}\n".format(
_ * 0.5, v_state['pos'], v_state['dir'], v_state['vel'],
points.tolist(), str(new_damage), traffic))
#print("Time passed since last step: {}".format(time.time() - end))
end = time.time()
#print("Time passed since scenario begun: {}\n".format(end - start))
print()
if end - start >= 45:
bng.close()
bng.step(30, wait=False)
except Exception as e:
print(e)
finally:
bng.close()
def main():
setup_logging()
beamng = BeamNGpy('localhost',
64256,
home='C:/Users/merie/Documents/BeamNG.research.v1.7.0.1')
scenario = Scenario(
'west_coast_usa',
'lidar_tour',
description='Tour through the west coast gathering Lidar data')
vehicle = Vehicle('ego_vehicle', model='etk800', licence='LIDAR')
vehicle = setup_sensors(vehicle)
lidar = setup_lidar('hood')
vehicle.attach_sensor('lidar', lidar)
spawn_pt = spawn_point()
ego_pos = spawn_pt['pos']
ego_rot_quat = spawn_pt['rot_quat']
scenario.add_vehicle(vehicle, pos=ego_pos, rot=None, rot_quat=ego_rot_quat)
tvs = traffic_vehicles()
scenario.make(beamng)
bng = beamng.open(launch=True)
#try:
bng.load_scenario(scenario)
bng.set_steps_per_second(60)
bng.set_deterministic()
bng.hide_hud()
bng.start_scenario()
vehicle.ai_set_mode('traffic')
vehicle.ai_set_aggression(0.5)
vehicle.ai_drive_in_lane(True)
#vehicle.ai_set_speed(16, mode='limit')
#vehicle.ai_set_target('traffic')
bng.switch_vehicle(vehicle)
damage_prev = None
start = time.time()
end = time.time()
bng.pause()
print("Bounding box: {}".format(vehicle.get_bbox()))
with open('lidar_data.csv', 'w') as f:
f.write(
"TIMESTAMP,VEHICLE_POSITION,VEHICLE_ORIENTATION,VELOCITY,LIDAR,CRASH\n"
)
for _ in range(1024):
sensors = bng.poll_sensors(vehicle)
points = sensors['lidar']['points']
damage = sensors['damage']
v_state = vehicle.state
print("vehicle_state = {}".format(v_state))
print("vehicle_state[pos] = {}".format(v_state['pos']))
print("vehicle_state[dir] = {}".format(v_state['dir']))
print("Vehicle bounding box:{}".format(vehicle.get_bbox()))
#ai_state = vehicle.ai_get_state()
#print("ai_state = {}".format(ai_state))
new_damage = diff_damage(damage, damage_prev)
damage_prev = damage
#print("new damage = {}".format(new_damage))
print()
f.write("{},{},{},{},{},{}\n".format(_ * 0.5, v_state['pos'],
v_state['dir'],
v_state['vel'],
points.tolist(),
str(new_damage)))
#bng.step(30, wait=False)
bng.step(30)
#print("Time passed since last step: {}".format(time.time() - end))
end = time.time()
#print("Time passed since scenario begun: {}\n".format(end - start))
#screenrecord()
if end - start >= 30:
#bng.close()
continue
def testrun(speed=11, risk=0.6):
global base_filename, training_dir, default_model, setpoint
global spawnpoint
f = setup_dir(training_dir)
spawn_pt = spawn_point(default_scenario, spawnpoint)
random.seed(1703)
setup_logging()
beamng = BeamNGpy('localhost',
64256,
home='H:/BeamNG.research.v1.7.0.1clean',
user='******')
scenario = Scenario(default_scenario, 'research_test')
# add barriers and cars to get the ego vehicle to avoid the barriers
add_barriers(scenario)
# add_barrier_cars(scenario)
vehicle = Vehicle('ego_vehicle',
model=default_model,
licence='RED',
color='White')
vehicle = setup_sensors(vehicle)
scenario.add_vehicle(vehicle,
pos=spawn_pt['pos'],
rot=None,
rot_quat=spawn_pt['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_nondeterministic() # Set simulator to be deterministic
bng.set_steps_per_second(36) #
# Load and start the scenario
bng.load_scenario(scenario)
bng.start_scenario()
bng.switch_vehicle(vehicle)
vehicle.ai_set_mode('traffic')
vehicle.ai_drive_in_lane(False)
vehicle.ai_set_speed(speed, mode='set')
vehicle.ai_set_aggression(risk)
# Put simulator in pause awaiting further inputs
bng.pause()
assert vehicle.skt
# bng.resume()
start_time = time.time()
# Send random inputs to vehicle and advance the simulation 20 steps
imagecount = 0
with open(f, 'w') as datafile:
datafile.write(
'filename,timestamp,steering_input,throttle_input,brake_input,driveshaft,engine_load,fog_lights,fuel,'
'lowpressure,oil,oil_temperature,parkingbrake,rpm,water_temperature,wheelspeed\n'
)
reached_start = False
vels = []
vel_dict = {}
while imagecount < 10000:
# collect images
sensors = bng.poll_sensors(vehicle)
image = sensors['front_cam']['colour'].convert('RGB')
full_filename = "{}{}{}.jpg".format(training_dir, base_filename,
imagecount)
qualified_filename = "{}{}.jpg".format(base_filename, imagecount)
# collect ancillary data
datafile.write(
'{},{},{},{},{},{},{},{},{},{},{},{},{},{},{}\n'.format(
qualified_filename, str(
round(sensors['timer']['time'],
2)), sensors['electrics']['steering_input'],
sensors['electrics']['throttle_input'],
sensors['electrics']['brake_input'],
sensors['electrics']['driveshaft'],
sensors['electrics']['engine_load'],
sensors['electrics']['fog_lights'],
sensors['electrics']['fuel'],
sensors['electrics']['lowpressure'],
sensors['electrics']['oil'],
sensors['electrics']['oil_temperature'],
sensors['electrics']['parkingbrake'],
sensors['electrics']['rpm'],
sensors['electrics']['water_temperature'],
sensors['electrics']['wheelspeed']))
if sensors['timer']['time'] > 10:
kph = sensors['electrics']['wheelspeed'] * 3.6
vels.append(kph)
vel_dict[sensors['timer']['time']] = kph
if distance(spawn_pt['pos'], vehicle.state['pos']
) < 5 and sensors['timer']['time'] > 10:
reached_start = True
break
if sensors['damage']['damage'] > 0:
print("CRASHED; QUITTING")
break
# save the image
image.save(full_filename)
imagecount += 1
# plt.title("90 degrees FOV")
# plt.imshow(image)
# plt.pause(0.01)
# plt.title("120 degrees FOV")
# plt.imshow(sensors['back_cam']['colour'].convert('RGB'))
# plt.pause(0.01)
# step sim forward
bng.step(1, wait=True)
wheelspeed_avg = round((sum(vels) / float(len(vels))), 3)
wheelspeed_var = round(np.var(vels), 3)
return_str = ''
if sensors['damage']['damage'] > 0:
# print("QUIT DUE TO CRASH VALUE {}".format(sensors['damage']['damage']))
return_str = "QUIT DUE TO CRASH VALUE {}".format(
sensors['damage']['damage'])
print(return_str)
maxtime = from_val_get_key(vel_dict, max(vels))
mintime = from_val_get_key(vel_dict, min(vels))
info = "IMAGE COUNT:{}\nSIM TIME:{} WALLCLOCK TIME:{}\nWHEELSPEED AVG:{} VAR:{} \nMAX:{} @ timestep {} MIN:{} @ timestep {} ".format(
imagecount, str(round(sensors['timer']['time'], 2)),
time.time() - start_time, wheelspeed_avg, wheelspeed_var,
round(max(vels), 3), round(maxtime, 3), round(min(vels), 3),
round(mintime, 3))
print("SPEED:{} RISK:{}".format(speed, risk))
print(info)
return_str = "{}\n{}".format(return_str, info)
bng.close()
return return_str
def main():
global sp
setup_logging()
beamng = BeamNGpy('localhost', 64256, home='H:/BeamNG.research.v1.7.0.1clean')
scenario = Scenario('west_coast_usa', 'lidar_tour',
description='Tour through the west coast gathering '
'Lidar data')
vehicle = Vehicle('ego_vehicle', model='etk800', licence='LIDAR', color='Red')
lidar = Lidar()
lidar.__init__(offset=(0, 0, 1.7), direction=(-0.707, -0.707, 0), vres=32,
vangle=0.01, rps=2200000, hz=20, angle=360, max_dist=200,
visualized=True)
vehicle.attach_sensor('lidar', lidar)
ego_sp = spawn_point(sp)
ego_pos = ego_sp['pos']
ego_rot_quat = ego_sp['rot_quat']
scenario.add_vehicle(vehicle, pos=ego_pos, rot=None, rot_quat=ego_rot_quat)
tvs = traffic_vehicles()
#ps = [(-722.121, 103, 118.675), (-714.121, 101, 118.675), (-715.121, 105, 118.675), (-721.121, 100, 118.675)]
ps = [(ego_pos[0]-10, ego_pos[1]+2, ego_pos[2]),
(ego_pos[0]-10, ego_pos[1]-2, ego_pos[2]),
(ego_pos[0]-14, ego_pos[1]+5, ego_pos[2]),
(ego_pos[0]+5, ego_pos[1]-1, ego_pos[2]),
(ego_pos[0]+8, ego_pos[1]-4, ego_pos[2]),
(ego_pos[0]+10, ego_pos[1]+9, ego_pos[2]),
(ego_pos[0]+1, ego_pos[1]+11, ego_pos[2])]
ps1 = [(ego_pos[0] - 10, ego_pos[1] + 3, ego_pos[2]),
(ego_pos[0] - 10, ego_pos[1] - 1, ego_pos[2]),
(ego_pos[0] - 14, ego_pos[1] + 5, ego_pos[2]),
(ego_pos[0] + 5, ego_pos[1] - 1, ego_pos[2]),
(ego_pos[0] + 8, ego_pos[1] - 4, ego_pos[2]),
(ego_pos[0] + 10, ego_pos[1] + 9, ego_pos[2]),
(ego_pos[0] + 1, ego_pos[1] + 11, ego_pos[2])]
ps_oldorig = [(ego_pos[0]-7, ego_pos[1]+3, ego_pos[2]),
(ego_pos[0]+5, ego_pos[1]-1, ego_pos[2]),
(ego_pos[0]+4, ego_pos[1]+5, ego_pos[2]),
(ego_pos[0]+5, ego_pos[1]-1, ego_pos[2]),
(ego_pos[0]+8, ego_pos[1]-4, ego_pos[2]),
(ego_pos[0]+10, ego_pos[1]+9, ego_pos[2]),
(ego_pos[0]+1, ego_pos[1]+11, ego_pos[2])]
for i in range(len(tvs)):
scenario.add_vehicle(tvs[i], pos=ps[i], rot_quat=ego_rot_quat)
scenario.make(beamng)
bng = beamng.open(launch=True)
try:
bng.load_scenario(scenario)
bng.set_steps_per_second(60)
bng.set_deterministic()
bng.hide_hud()
bng.start_scenario()
vehicle.ai_set_mode('traffic')
vehicle.ai_drive_in_lane(True)
#vehicle.ai_set_speed(16, mode='limit')
#vehicle.ai_set_target('traffic')
vehicle.ai_set_aggression(0.5) #0.7)
bng.start_traffic(tvs)
bng.switch_vehicle(vehicle)
start = time.time()
end = time.time()
damage_prev = bng.poll_sensors(vehicle)
with open('lidar_data.csv', 'w') as f:
for _ in range(1024):
sensors = bng.poll_sensors(vehicle)
points = sensors['lidar']['points']
#print(points.tolist())
#print()
#f.write("{}\n".format(points.tolist()))
print("Time passed since last step: {}".format(time.time() - end))
end = time.time()
print("Time passed since scenario begun: {}\n".format(end - start))
if end - start >= 30:
bng.close()
bng.step(30, wait=False)
except Exception as e:
print(e)
finally:
bng.close()
def run_sim(nodes, ai_aggression, street_1: DecalRoad, street_2: DecalRoad):
waypoint_goal = BeamNGWaypoint('waypoint_goal',
get_node_coords(street_1.nodes[-1]))
maps.beamng_map.generated().write_items(street_1.to_json() + '\n' +
waypoint_goal.to_json() + '\n' +
street_2.to_json())
beamng = BeamNGpy('localhost', 64256)
scenario = Scenario('tig', 'tigscenario')
vehicle = Vehicle('ego_vehicle',
model='etk800',
licence='TIG',
color='Red')
sim_data_collector = TrainingDataCollectorAndWriter(
vehicle, beamng, street_1)
scenario.add_vehicle(vehicle,
pos=get_node_coords(street_1.nodes[0]),
rot=get_rotation(street_1))
scenario.make(beamng)
beamng.open()
beamng.set_deterministic()
beamng.load_scenario(scenario)
beamng.pause()
beamng.start_scenario()
vehicle.ai_set_aggression(ai_aggression)
vehicle.ai_drive_in_lane(True)
# vehicle.ai_set_speed(25.0 / 4)
vehicle.ai_set_waypoint(waypoint_goal.name)
# vehicle.ai_set_mode("manual")
# sleep(5)
steps = 5
print(nodes)
print(beamng.get_road_edges("street_1"))
def start():
for idx in range(1000):
if (idx * 0.05 * steps) > 3.:
sim_data_collector.collect_and_write_current_data()
dist = distance(sim_data_collector.last_state.pos,
waypoint_goal.position)
if dist < 15.0:
beamng.resume()
break
# one step is 0.05 seconds (5/100)
beamng.step(steps)
try:
start()
finally:
beamng.close()
