nb_lines_per_plot = [1, nb_actions + 1]
labels = [['speed'], ['chosen', 'cruise', 'go', 'stop']]
titles = ('Speed vs position', 'Epistemic uncertainty vs position')
time_series_visualizer = TimeSeriesVisualization(
nb_plots=nb_plots,
nb_lines_per_plot=nb_lines_per_plot,
y_range=[[0, env.max_ego_speed + 1], [0, 5]],
x_range=[0, env.max_steps],
labels=labels,
titles=titles)
if plot_decision_map:
decision_map = DecisionMap(agent, traci,
ps.road_params['intersection_position'],
ps.sim_params['ego_start_position'],
ps.sim_params['ego_end_position'], nb_actions)
env.reset()
# Lists for saving data of reruns
episode_rewards = []
episode_steps = []
nb_safe_actions_per_episode = []
episode_collision = []
episode_near_collision = []
episode_max_steps = []
episode_max_std_dev_a = []
episode_mean_std_dev_a = []
episode_max_std_dev_e = []
episode_mean_std_dev_e = []
episode_epistemic_uncertainty_data = []
episode_aleatoric_uncertainty_data = []
# Main loop, testing the agent in nb_reruns different scenarios
'use_gui': True,
'print_gui_info': True,
'draw_sensor_range': True,
'zoom_level': 3000
}
np.random.seed(13)
env = IntersectionEnv(sim_params=p.sim_params,
road_params=p.road_params,
gui_params=gui_params)
episode_rewards = []
episode_steps = []
for i in range(0, 100):
np.random.seed(i)
env.reset(ego_at_intersection=ego_at_intersection)
done = False
episode_reward = 0
step = 0
while done is False:
if step < 35:
action = 2
else:
action = 1
obs, reward, done, info = env.step(action)
episode_reward += reward
step += 1
episode_rewards.append(episode_reward)
episode_steps.append(step)
print("Episode: " + str(i))
class Tester(unittest.TestCase):
def __init__(self, *args, **kwargs):
super(Tester, self).__init__(*args, **kwargs)
np.random.seed(13)
# For some strange reason, test_init sometimes fails when running all the tests
# through "python3 -m unittest discover .". However, it always passes when running
# only the intersection environment tests. To make the test process pass, this test
# is therefore removed, but it should pass when run in isolation.
# def test_init(self):
# gui_params = {'use_gui': False, 'print_gui_info': False, 'draw_sensor_range': False, 'zoom_level': 3000}
# self.env = IntersectionEnv(sim_params=p.sim_params, road_params=p.road_params, gui_params=gui_params)
# self.env.reset()
# try:
# self.assertGreater(len(traci.vehicle.getIDList()), 1)
# self.assertTrue((self.env.speeds[:, 0] <= p.road_params['speed_range'][1]).all())
# self.assertTrue((self.env.speeds[:len(self.env.vehicles), 0] >= p.road_params['speed_range'][0]).all())
# finally:
# traci.close()
def test_step(self):
gui_params = {
'use_gui': False,
'print_gui_info': False,
'draw_sensor_range': False,
'zoom_level': 3000
}
self.env = IntersectionEnv(sim_params=p.sim_params,
road_params=p.road_params,
gui_params=gui_params)
self.env.reset()
try:
action = 0
self.env.step(action)
finally:
traci.close()
def test_reset(self):
gui_params = {
'use_gui': False,
'print_gui_info': False,
'draw_sensor_range': False,
'zoom_level': 3000
}
self.env = IntersectionEnv(sim_params=p.sim_params,
road_params=p.road_params,
gui_params=gui_params)
self.env.reset()
try:
action = 0
for _ in range(9):
self.env.step(action)
self.env.reset()
for _ in range(10):
self.env.step(action)
finally:
traci.close()
def test_gym_interface(self):
gui_params = {
'use_gui': False,
'print_gui_info': False,
'draw_sensor_range': False,
'zoom_level': 3000
}
self.env = IntersectionEnv(sim_params=p.sim_params,
road_params=p.road_params,
gui_params=gui_params)
self.env.reset()
try:
self.assertEqual(self.env.nb_actions, 3)
self.assertEqual(self.env.nb_observations,
3 + 4 * p.sim_params['sensor_nb_vehicles'])
finally:
traci.close()
def test_sensor_model(self):
gui_params = {
'use_gui': False,
'print_gui_info': False,
'draw_sensor_range': False,
'zoom_level': 3000
}
self.env = IntersectionEnv(sim_params=p.sim_params,
road_params=p.road_params,
gui_params=gui_params)
self.env.reset()
try:
self.env.sensor_range = 200
self.env.occlusion_dist = 1e6
self.env.sensor_noise = {'pos': 0, 'speed': 0, 'heading': 0}
positions = np.zeros([self.env.max_nb_vehicles, 2])
rel_pos_intersect = np.array([[1.6, 0],
[-190, -self.env.lane_width / 2],
[190, self.env.lane_width / 2],
[-205, -self.env.lane_width / 2]])
positions[0:4, :] = rel_pos_intersect + np.array(
self.env.intersection_pos) # Last vehicle outside sensor range
speeds = np.zeros([self.env.max_nb_vehicles, 2])
speeds[0:4, :] = np.array([[15.0, 0], [15, 0.], [0, 0.], [7.5, 0]])
headings = np.zeros(self.env.max_nb_vehicles)
headings[0:4] = np.array([0, np.pi / 2, 3 * np.pi / 2, np.pi / 2])
done = False
state = [positions, speeds, headings, done]
observation = self.env.sensor_model(state)
self.assertEqual(
observation[0], -2 * p.sim_params['ego_end_position'] /
(p.sim_params['ego_end_position'] -
p.sim_params['ego_start_position']) + 1)
self.assertEqual(observation[1], 1)
self.assertEqual(observation[2], -1)
self.assertEqual(observation[3], -0.95)
self.assertAlmostEqual(observation[4], -1.6 / 200)
self.assertEqual(observation[5], 1)
self.assertEqual(observation[6], -0.5)
self.assertEqual(observation[7], 0.95)
self.assertAlmostEqual(observation[8], 1.6 / 200)
self.assertEqual(observation[9], -1)
self.assertEqual(observation[10], 0.5)
for i in range(11, self.env.sensor_nb_vehicles):
self.assertEqual(observation[i], -1)
rel_pos_intersect = [self.env.lane_width / 2, 30]
positions[0] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect)
]
speeds[0] = [0, 0]
done = True
state = [positions, speeds, headings, done]
observation = self.env.sensor_model(state)
self.assertEqual(observation[0], 1)
self.assertEqual(observation[1], -1)
self.assertEqual(observation[2], 1)
self.env.max_nb_vehicles = self.env.sensor_nb_vehicles + 5
positions = np.random.rand(self.env.sensor_nb_vehicles + 5,
2) * 300 + np.array(
self.env.intersection_pos)
positions[0] = self.env.intersection_pos
speeds = np.random.rand(self.env.sensor_nb_vehicles + 5, 2) * 15
headings = np.random.rand(self.env.sensor_nb_vehicles +
5) * 2 * np.pi
state = [positions, speeds, headings, done]
observation = self.env.sensor_model(state)
self.assertTrue((np.abs(observation) <= 1).all())
self.assertEqual(len(observation),
3 + self.env.sensor_nb_vehicles * 4)
# Noise
self.env.sensor_noise = {
'pos': 2,
'speed': 2,
'heading': 20 / 180 * np.pi
}
rel_pos_intersect = [-50, -self.env.lane_width / 2]
positions[1] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect)
]
# positions[1] = [950, 998.4]
speeds[1] = [0, 0]
state = [positions, speeds, headings, done]
observation = self.env.sensor_model(state)
self.assertFalse(observation[5] == -1)
finally:
traci.close()
def test_occlusion_model(self):
gui_params = {
'use_gui': False,
'print_gui_info': False,
'draw_sensor_range': False,
'zoom_level': 3000
}
self.env = IntersectionEnv(sim_params=p.sim_params,
road_params=p.road_params,
gui_params=gui_params)
self.env.reset()
try:
self.env.occlusion_dist = 1e6
self.assertTrue(self.env.occlusion_model(-100).all())
self.env.occlusion_dist = 2
rel_pos_intersect = [0, -self.env.lane_width / 2]
self.env.positions[1, :] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect)
]
self.env.headings[1] = 0
rel_pos_intersect = [0, self.env.lane_width / 2]
self.env.positions[2, :] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect)
]
self.env.headings[2] = np.pi
rel_pos_intersect = [-10.4, -self.env.lane_width / 2]
self.env.positions[3, :] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect)
]
self.env.headings[3] = 0
rel_pos_intersect = [-14.4, -self.env.lane_width / 2]
self.env.positions[4, :] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect)
]
self.env.headings[4] = 0
self.assertTrue(self.env.occlusion_model(-100)[0])
self.assertTrue(self.env.occlusion_model(-100)[1])
self.assertFalse(self.env.occlusion_model(-100)[2:].any())
self.assertTrue(self.env.occlusion_model(-10)[2])
self.assertFalse(self.env.occlusion_model(-10)[3:].any())
finally:
traci.close()
def test_reward_model(self):
gui_params = {
'use_gui': False,
'print_gui_info': False,
'draw_sensor_range': False,
'zoom_level': 3000
}
self.env = IntersectionEnv(sim_params=p.sim_params,
road_params=p.road_params,
gui_params=gui_params)
self.env.reset()
try:
self.assertEqual(
self.env.reward_model(goal_reached=False,
collision=False,
near_collision=False)[0], 0)
self.assertEqual(
self.env.reward_model(goal_reached=True,
collision=False,
near_collision=False)[0],
p.sim_params['goal_reward'])
self.assertEqual(
self.env.reward_model(goal_reached=False,
collision=True,
near_collision=False)[0],
p.sim_params['collision_penalty'])
self.assertEqual(
self.env.reward_model(goal_reached=False,
collision=False,
near_collision=True)[0],
p.sim_params['near_collision_penalty'])
finally:
traci.close()
def test_action_model(self):
gui_params = {
'use_gui': False,
'print_gui_info': False,
'draw_sensor_range': False,
'zoom_level': 3000
}
self.env = IntersectionEnv(sim_params=p.sim_params,
road_params=p.road_params,
gui_params=gui_params)
self.env.reset()
try:
ego_speed_0 = self.env.speeds[0][0]
self.env.step(2)
self.assertLess(self.env.speeds[0][0], ego_speed_0)
for i in range(3):
self.env.step(2)
ego_speed_0 = self.env.speeds[0][0]
self.env.step(0)
self.assertEqual(self.env.speeds[0][0], ego_speed_0)
self.env.step(1)
self.assertGreater(self.env.speeds[0][0], ego_speed_0)
self.env.reset()
self.env.step(1)
self.assertEqual(self.env.speeds[0][0], self.env.max_ego_speed)
done = False
while not done:
_, _, done, _ = self.env.step(2)
self.assertEqual(self.env.speeds[0][0], 0)
finally:
traci.close()
def test_safe_action(self):
gui_params = {
'use_gui': False,
'print_gui_info': False,
'draw_sensor_range': False,
'zoom_level': 3000
}
self.env = IntersectionEnv(sim_params=p.sim_params,
road_params=p.road_params,
gui_params=gui_params)
self.env.reset()
try:
# Stop at intersection
ego_speed_0 = self.env.speeds[0][0]
self.env.positions[0][
1] = p.road_params['intersection_position'][1] - 50
self.env.step(3)
self.assertEqual(self.env.speeds[0][0],
ego_speed_0 + p.sim_params['idm_params']['a_min'])
# Use original action
ego_speed_0 = 5
self.env.speeds[0][0] = ego_speed_0
self.env.positions[0][1] = p.road_params['intersection_position'][
1] - p.road_params['stop_line'] + 1
self.env.step(3, {'original_action': 0})
self.assertEqual(self.env.speeds[0][0], ego_speed_0)
self.env.speeds[0][0] = ego_speed_0
self.env.positions[0][1] = p.road_params['intersection_position'][
1] - p.road_params['stop_line'] + 1
self.env.step(3, {'original_action': 1})
self.assertGreater(self.env.speeds[0][0], ego_speed_0)
self.env.speeds[0][0] = ego_speed_0
self.env.positions[0][1] = p.road_params['intersection_position'][
1] - p.road_params['stop_line'] + 1
self.env.step(3, {'original_action': 2})
self.assertEqual(self.env.speeds[0][0],
ego_speed_0 + p.sim_params['idm_params']['a_min'])
finally:
traci.close()
def test_collision_detection(self):
gui_params = {
'use_gui': False,
'print_gui_info': False,
'draw_sensor_range': False,
'zoom_level': 3000
}
self.env = IntersectionEnv(sim_params=p.sim_params,
road_params=p.road_params,
gui_params=gui_params)
self.env.reset()
nb_cars = self.env.positions.shape[0] - 1
try:
# Outside intersection
collision, near_collision, info = self.env.collision_detection()
self.assertFalse(collision)
self.assertFalse(near_collision)
self.assertEqual(info, 'Ego before_intersection')
rel_pos_intersect = [self.env.lane_width / 2, 50]
self.env.positions[0, :] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect)
]
collision, near_collision, info = self.env.collision_detection()
self.assertFalse(collision)
self.assertFalse(near_collision)
self.assertEqual(info, '')
# Collision
# West bound
rel_pos_intersect_0 = [self.env.lane_width / 2, 0]
self.env.positions[0] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_0)
]
rel_pos_intersect_1 = [
self.env.lane_width / 2 + self.env.ego_width / 2 +
self.env.car_length / 2 + 0.1, self.env.lane_width / 2
]
self.env.positions[-1] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_1)
]
self.env.headings[-1] = np.pi
collision, near_collision, info = self.env.collision_detection()
self.assertFalse(collision)
rel_pos_intersect_1 = [
self.env.lane_width / 2 + self.env.ego_width / 2 +
self.env.car_length / 2 - 0.1, self.env.lane_width / 2
]
self.env.positions[-1] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_1)
]
collision, near_collision, info = self.env.collision_detection()
self.assertTrue(collision)
self.assertFalse(near_collision)
self.assertTrue('collision' == info[0])
self.assertTrue(str(nb_cars) in info[1])
# North bound
rel_pos_intersect_1 = [
self.env.lane_width / 2,
self.env.ego_length / 2 + self.env.car_length / 2 + 0.1,
self.env.lane_width / 2
]
self.env.positions[-1] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_1)
]
self.env.headings[-1] = np.pi / 2
collision, near_collision, info = self.env.collision_detection()
self.assertFalse(collision)
rel_pos_intersect_1 = [
self.env.lane_width / 2,
self.env.ego_length / 2 + self.env.car_length / 2 - 0.1,
self.env.lane_width / 2
]
self.env.positions[-1] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_1)
]
self.env.headings[-1] = np.pi / 2
collision, near_collision, info = self.env.collision_detection()
self.assertTrue(collision)
self.assertFalse(near_collision)
self.assertTrue('collision' == info[0])
self.assertTrue(str(nb_cars) in info[1])
# East bound
rel_pos_intersect_2 = [
self.env.lane_width / 2 - self.env.ego_width / 2 -
self.env.car_length / 2 + 0.1, -self.env.lane_width / 2
]
self.env.positions[-2] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_2)
]
self.env.headings[-2] = 0
collision, near_collision, info = self.env.collision_detection()
self.assertTrue(collision)
self.assertFalse(near_collision)
self.assertTrue('collision' == info[0])
self.assertTrue(str(nb_cars - 1) in info[1])
self.assertTrue(str(nb_cars) in info[1])
self.env.reset()
# Near collision, west
# Within x margin, but ego vehicle too high
rel_pos_intersect_0 = [
self.env.lane_width / 2, self.env.ego_length / 2 +
self.env.car_width / 2 + self.env.lane_width / 2 +
self.env.near_collision_margin[1] + 0.1
]
self.env.positions[0] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_0)
]
rel_pos_intersect_1 = [
self.env.lane_width / 2 + self.env.ego_width / 2 +
self.env.car_length / 2 + self.env.near_collision_margin[0] -
0.1, self.env.lane_width / 2
]
self.env.positions[-1] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_1)
]
self.env.headings[-1] = np.pi
collision, near_collision, info = self.env.collision_detection()
self.assertFalse(collision)
self.assertFalse(near_collision)
# Within x margin, but ego vehicle too low
rel_pos_intersect_0 = [
self.env.lane_width / 2, -self.env.ego_length / 2 -
self.env.car_width / 2 + self.env.lane_width / 2 -
self.env.near_collision_margin[1] - 0.1
]
self.env.positions[0] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_0)
]
rel_pos_intersect_1 = [
self.env.lane_width / 2 + self.env.ego_width / 2 +
self.env.car_length / 2 + self.env.near_collision_margin[0] -
0.1, self.env.lane_width / 2
]
self.env.positions[-1] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_1)
]
self.env.headings[-1] = np.pi
collision, near_collision, info = self.env.collision_detection()
self.assertFalse(collision)
self.assertFalse(near_collision)
# Within x margin right
rel_pos_intersect_0 = [self.env.lane_width / 2, 0]
self.env.positions[0] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_0)
]
collision, near_collision, info = self.env.collision_detection()
self.assertFalse(collision)
self.assertTrue(near_collision)
self.assertTrue('near_collision' == info[0])
self.assertTrue(str(nb_cars) in info[1])
# Within x margin left
rel_pos_intersect_1 = [
self.env.lane_width / 2 - self.env.ego_width / 2 -
self.env.car_length / 2 - self.env.near_collision_margin[0] +
0.1, self.env.lane_width / 2
]
self.env.positions[-1] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_1)
]
self.env.headings[-1] = np.pi
collision, near_collision, info = self.env.collision_detection()
self.assertFalse(collision)
self.assertTrue(near_collision)
self.assertTrue('near_collision' == info[0])
self.assertTrue(str(nb_cars) in info[1])
# Within y margin top
rel_pos_intersect_0 = [
self.env.lane_width / 2, self.env.ego_length / 2 +
self.env.car_width / 2 + self.env.lane_width / 2 +
self.env.near_collision_margin[1] - 0.1
]
self.env.positions[0] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_0)
]
rel_pos_intersect_1 = [
self.env.lane_width / 2 + self.env.ego_width / 2 +
self.env.car_length / 2 - 0.1, self.env.lane_width / 2
]
self.env.positions[-1] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_1)
]
self.env.headings[-1] = np.pi
collision, near_collision, info = self.env.collision_detection()
self.assertFalse(collision)
self.assertTrue(near_collision)
self.assertTrue('near_collision' == info[0])
self.assertTrue(str(nb_cars) in info[1])
# Within y margin bottom
rel_pos_intersect_0 = [
self.env.lane_width / 2, -self.env.ego_length / 2 -
self.env.car_width / 2 + self.env.lane_width / 2 -
self.env.near_collision_margin[1] + 0.1
]
self.env.positions[0] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_0)
]
collision, near_collision, info = self.env.collision_detection()
self.assertFalse(collision)
self.assertTrue(near_collision)
self.assertTrue('near_collision' == info[0])
self.assertTrue(str(nb_cars) in info[1])
# Near collision, east
# Within margin but ego vehicle too high
rel_pos_intersect_0 = [
self.env.lane_width / 2, self.env.ego_length / 2 +
self.env.car_width / 2 + self.env.lane_width / 2 +
self.env.near_collision_margin[1] + 0.1
]
self.env.positions[0] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_0)
]
rel_pos_intersect_1 = [
self.env.lane_width / 2 - self.env.ego_width / 2 -
self.env.car_length / 2 - self.env.near_collision_margin[0] +
0.1, -self.env.lane_width / 2
]
self.env.positions[-1] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_1)
]
self.env.headings[-1] = 0
collision, near_collision, info = self.env.collision_detection()
self.assertFalse(collision)
self.assertFalse(near_collision)
# Within margin but ego vehicle too low
rel_pos_intersect_0 = [
self.env.lane_width / 2, -self.env.ego_length / 2 -
self.env.car_width / 2 - self.env.lane_width / 2 -
self.env.near_collision_margin[1] - 0.1
]
self.env.positions[0] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_0)
]
rel_pos_intersect_1 = [
self.env.lane_width / 2 - self.env.ego_width / 2 -
self.env.car_length / 2 - self.env.near_collision_margin[0] +
0.1, -self.env.lane_width / 2
]
self.env.positions[-1] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_1)
]
self.env.headings[-1] = 0
collision, near_collision, info = self.env.collision_detection()
self.assertFalse(collision)
self.assertFalse(near_collision)
# Within margin left
rel_pos_intersect_0 = [self.env.lane_width / 2, 0]
self.env.positions[0] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_0)
]
collision, near_collision, info = self.env.collision_detection()
self.assertTrue(near_collision)
self.assertTrue('near_collision' == info[0])
self.assertTrue(str(nb_cars) in info[1])
# Within margin right
rel_pos_intersect_1 = [
self.env.lane_width / 2 + self.env.ego_width / 2 +
self.env.car_length / 2 + self.env.near_collision_margin[0] -
0.1, -self.env.lane_width / 2
]
self.env.positions[-1] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_1)
]
self.env.headings[-1] = 0
collision, near_collision, info = self.env.collision_detection()
self.assertFalse(collision)
self.assertTrue(near_collision)
self.assertTrue('near_collision' == info[0])
self.assertTrue(str(nb_cars) in info[1])
# Within y margin top
rel_pos_intersect_0 = [
self.env.lane_width / 2, self.env.ego_length / 2 +
self.env.car_width / 2 - self.env.lane_width / 2 +
self.env.near_collision_margin[1] - 0.1
]
self.env.positions[0] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_0)
]
rel_pos_intersect_1 = [
self.env.lane_width / 2 - self.env.ego_width / 2 -
self.env.car_length / 2 + 0.1, -self.env.lane_width / 2
]
self.env.positions[-1] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_1)
]
self.env.headings[-1] = 0
collision, near_collision, info = self.env.collision_detection()
self.assertFalse(collision)
self.assertTrue(near_collision)
self.assertTrue('near_collision' == info[0])
self.assertTrue(str(nb_cars) in info[1])
# Within y margin bottom
rel_pos_intersect_0 = [
self.env.lane_width / 2, -self.env.ego_length / 2 -
self.env.car_width / 2 - self.env.lane_width / 2 -
self.env.near_collision_margin[1] + 0.1
]
self.env.positions[0] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_0)
]
collision, near_collision, info = self.env.collision_detection()
self.assertFalse(collision)
self.assertTrue(near_collision)
self.assertTrue('near_collision' == info[0])
self.assertTrue(str(nb_cars) in info[1])
# Turning vehicle
# South
rel_pos_intersect_0 = [self.env.lane_width / 2, 0]
self.env.positions[0] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_0)
]
rel_pos_intersect_1 = [-3.84, -3.2]
self.env.positions[-1] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_1)
]
self.env.headings[-1] = 5.6012
self.env.positions[-2] = self.env.positions[-3]
collision, near_collision, info = self.env.collision_detection()
self.assertFalse(collision)
self.assertFalse(near_collision)
self.assertEqual(info, '')
rel_pos_intersect_1 = [-1.66, -8.79]
self.env.positions[-1] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_1)
]
self.env.headings[-1] = 4.7363
collision, near_collision, info = self.env.collision_detection()
self.assertFalse(collision)
self.assertFalse(near_collision)
self.assertEqual(info, '')
# North
rel_pos_intersect_1 = [3.84, 3.2]
self.env.positions[-1] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_1)
]
self.env.headings[-1] = 2.4596
collision, near_collision, info = self.env.collision_detection()
self.assertTrue(collision)
self.assertFalse(near_collision)
self.assertTrue('collision' == info[0])
self.assertTrue(str(nb_cars) in info[1])
rel_pos_intersect_0 = [
self.env.lane_width / 2,
3.2 - self.env.ego_length / 2 - self.env.car_width / 2 - 0.1
]
self.env.positions[0] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_0)
]
collision, near_collision, info = self.env.collision_detection()
self.assertFalse(collision)
self.assertTrue(near_collision)
self.assertTrue('near_collision' == info[0])
self.assertTrue(str(nb_cars) in info[1])
rel_pos_intersect_0 = [self.env.lane_width / 2, 0]
self.env.positions[0] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_0)
]
rel_pos_intersect_1 = [
self.env.lane_width / 2,
self.env.ego_length / 2 + self.env.car_length / 2 +
self.env.near_collision_margin[1] - 0.1
]
self.env.positions[-1] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_1)
]
self.env.headings[-1] = np.pi / 2
collision, near_collision, info = self.env.collision_detection()
self.assertFalse(collision)
self.assertTrue(near_collision)
self.assertTrue('near_collision' == info[0])
self.assertTrue(str(nb_cars) in info[1])
# Collision between time steps
# East
rel_pos_intersect_0 = [
self.env.lane_width / 2,
self.env.lane_width / 2 + self.env.ego_length / 2 +
self.env.car_width / 2 + self.env.speeds[0][0] - 5
]
self.env.positions[0] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_0)
]
self.env.speeds[-1] = [15, 0]
rel_pos_intersect_1 = [
self.env.lane_width / 2 + self.env.ego_width / 2 +
self.env.car_length / 2 + self.env.speeds[-1][0] - 5,
-self.env.lane_width / 2
]
self.env.positions[-1] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_1)
]
self.env.headings[-1] = 0
collision, near_collision, info = self.env.collision_detection()
self.assertTrue(collision)
self.assertFalse(near_collision)
self.assertTrue('collision' == info[0])
self.assertTrue(str(nb_cars) in info[1])
rel_pos_intersect_0 = [
self.env.lane_width / 2,
self.env.lane_width / 2 + self.env.ego_length / 2 +
self.env.car_width / 2 + self.env.speeds[0][0] + 1
]
self.env.positions[0] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_0)
]
collision, near_collision, info = self.env.collision_detection()
self.assertFalse(collision)
self.assertFalse(near_collision)
self.assertEqual(info, '')
# West
rel_pos_intersect_0 = [
self.env.lane_width / 2,
-self.env.lane_width / 2 + self.env.ego_length / 2 +
self.env.car_width / 2 + self.env.speeds[0][0] - 5
]
self.env.positions[0] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_0)
]
self.env.speeds[-1] = [15, 0]
rel_pos_intersect_1 = [
self.env.lane_width / 2 - self.env.ego_width / 2 -
self.env.car_length / 2 - self.env.speeds[-1][0] + 5,
-self.env.lane_width / 2
]
self.env.positions[-1] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_1)
]
self.env.headings[-1] = np.pi
collision, near_collision, info = self.env.collision_detection()
self.assertTrue(collision)
self.assertFalse(near_collision)
self.assertTrue('collision' == info[0])
self.assertTrue(str(nb_cars) in info[1])
rel_pos_intersect_0 = [
self.env.lane_width / 2,
-self.env.lane_width / 2 + self.env.ego_length / 2 +
self.env.car_width / 2 + self.env.speeds[0][0] + 1
]
self.env.positions[0] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_0)
]
collision, near_collision, info = self.env.collision_detection()
self.assertFalse(collision)
self.assertFalse(near_collision)
self.assertEqual(info, '')
# Near collision between time steps
# East
rel_pos_intersect_0 = [
self.env.lane_width / 2, -self.env.lane_width / 2 +
self.env.ego_length / 2 + self.env.car_width / 2 +
self.env.speeds[0][0] + self.env.near_collision_margin[1] - 0.6
]
self.env.positions[0] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_0)
]
self.env.speeds[-1] = [15, 0]
rel_pos_intersect_1 = [
self.env.lane_width / 2 - self.env.ego_width / 2 -
self.env.car_length / 2 + self.env.speeds[-1][0] -
self.env.near_collision_margin[0] - 0.5,
-self.env.lane_width / 2
]
self.env.positions[-1] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_1)
]
self.env.headings[-1] = 0
collision, near_collision, info = self.env.collision_detection()
self.assertFalse(collision)
self.assertTrue(near_collision)
self.assertTrue('near_collision' == info[0])
self.assertTrue(str(nb_cars) in info[1])
rel_pos_intersect_0 = [
self.env.lane_width / 2, -self.env.lane_width / 2 +
self.env.ego_length / 2 + self.env.car_width / 2 +
self.env.speeds[0][0] + self.env.near_collision_margin[1] + 0.5
]
self.env.positions[0] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_0)
]
collision, near_collision, info = self.env.collision_detection()
self.assertFalse(collision)
self.assertFalse(near_collision)
self.assertEqual(info, '')
self.env.speeds[0][0] = 0
rel_pos_intersect_0 = [
self.env.lane_width / 2, -self.env.lane_width / 2 -
self.env.ego_length / 2 - self.env.car_width / 2 -
self.env.near_collision_margin[1] + 0.5
]
self.env.positions[0] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_0)
]
self.env.speeds[-1] = [15, 0]
rel_pos_intersect_1 = [
self.env.lane_width / 2 - self.env.ego_width / 2 -
self.env.car_length / 2 + self.env.speeds[-1][0] - 3,
-self.env.lane_width / 2
]
self.env.positions[-1] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_1)
]
self.env.headings[-1] = 0
collision, near_collision, info = self.env.collision_detection()
self.assertFalse(collision)
self.assertTrue(near_collision)
self.assertTrue('near_collision' == info[0])
rel_pos_intersect_0 = [
self.env.lane_width / 2,
-self.env.stop_line - self.env.ego_length / 2
]
self.env.positions[0] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_0)
]
collision, near_collision, info = self.env.collision_detection()
self.assertFalse(collision)
self.assertFalse(near_collision)
self.assertEqual(info, '')
self.env.reset()
# West
rel_pos_intersect_0 = [
self.env.lane_width / 2, self.env.lane_width / 2 +
self.env.ego_length / 2 + self.env.car_width / 2 +
self.env.speeds[0][0] + self.env.near_collision_margin[1] - 0.6
]
self.env.positions[0] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_0)
]
self.env.speeds[-1] = [15, 0]
rel_pos_intersect_1 = [
self.env.lane_width / 2 + self.env.ego_width / 2 +
self.env.car_length / 2 - self.env.speeds[-1][0] +
self.env.near_collision_margin[0] + 0.5,
self.env.lane_width / 2
]
self.env.positions[-1] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_1)
]
self.env.headings[-1] = np.pi
collision, near_collision, info = self.env.collision_detection()
self.assertFalse(collision)
self.assertTrue(near_collision)
self.assertTrue('near_collision' == info[0])
self.assertTrue(str(nb_cars) in info[1])
rel_pos_intersect_0 = [
self.env.lane_width / 2, self.env.lane_width / 2 +
self.env.ego_length / 2 + self.env.car_width / 2 +
self.env.speeds[0][0] + self.env.near_collision_margin[1] + 0.5
]
self.env.positions[0] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_0)
]
collision, near_collision, info = self.env.collision_detection()
self.assertFalse(collision)
self.assertFalse(near_collision)
self.assertEqual(info, '')
self.env.speeds[0][0] = 1
rel_pos_intersect_0 = [
self.env.lane_width / 2, self.env.lane_width / 2 +
self.env.ego_length / 2 + self.env.car_width / 2 +
self.env.speeds[0][0] + self.env.near_collision_margin[1] - 0.5
]
self.env.positions[0] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_0)
]
self.env.speeds[-1] = [15, 0]
rel_pos_intersect_1 = [
self.env.lane_width / 2 + self.env.ego_width / 2 +
self.env.car_length / 2 - self.env.speeds[-1][0] +
self.env.near_collision_margin[0] + 0.5,
self.env.lane_width / 2
]
self.env.positions[-1] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_1)
]
self.env.headings[-1] = np.pi
collision, near_collision, info = self.env.collision_detection()
self.assertFalse(collision)
self.assertTrue(near_collision)
self.assertTrue('near_collision' == info[0])
rel_pos_intersect_0 = [
self.env.lane_width / 2, self.env.lane_width / 2 +
self.env.ego_length / 2 + self.env.car_width / 2 +
self.env.speeds[0][0] + self.env.near_collision_margin[1] + 0.5
]
self.env.positions[0] = [
sum(e)
for e in zip(self.env.intersection_pos, rel_pos_intersect_0)
]
collision, near_collision, info = self.env.collision_detection()
self.assertFalse(collision)
self.assertFalse(near_collision)
self.assertEqual(info, '')
finally:
traci.close()
def test_timeout(self):
gui_params = {
'use_gui': False,
'print_gui_info': False,
'draw_sensor_range': False,
'zoom_level': 3000
}
self.env = IntersectionEnv(sim_params=p.sim_params,
road_params=p.road_params,
gui_params=gui_params)
self.env.reset()
try:
done = False
while not done:
_, _, done, info = self.env.step(
2) # Action stop at intersection
self.assertEqual(self.env.step_, p.sim_params['max_steps'])
self.assertEqual('Max steps', info['terminal_reason'])
finally:
traci.close()