huangfu/InteractionImitation
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Change IDM default params

Browse Source
This commit is contained in:
Johannes Fischer
2022-02-28 10:49:58 +01:00
parent 8e12996dfe
commit 6d867466c6
1 changed files with 11 additions and 10 deletions
Download Patch File Download Diff File Expand all files Collapse all files

21
src/baselines/rule_policies.py
View File

@@ -87,9 +87,10 @@ class IDMRulePolicy(BaseAlgorithm):
assert target_speed>0, 'negative target speed' assert target_speed>0, 'negative target speed'
self.v_max = target_speed self.v_max = target_speed
self.a_max = np.array([3.]) # nominal acceleration self.a_max = np.array([3.]) # nominal acceleration
self.tau = 0.5 # desired time headway self.tau = 1 # desired time headway
self.b_pref = 2.5 # preferred deceleration self.b_pref = 2.5 # preferred deceleration
self.d_min = 1 #minimum spacing self.d_min = 5 #minimum spacing
# self.delta_psi = 40 # [degree] max deviation in orientation to be mapped onto
# for np.remainder nan warnings # for np.remainder nan warnings
np.seterr(invalid='ignore') np.seterr(invalid='ignore')
@@ -142,22 +143,22 @@ class IDMRulePolicy(BaseAlgorithm):
# time_horizon = np.array(range(3)) # time_horizon = np.array(range(3))
# predictions = state[:,0:1] + np.outer(state[:,1], time_horizon) # predictions = state[:,0:1] + np.outer(state[:,1], time_horizon)
paths = np.stack([x[:,:-1],y[:,:-1], heading], axis=1) # (nv x 3 x (path_length-1)) paths = np.stack([x[:,:-1],y[:,:-1], heading], axis=1) # (nv, 3, (path_length-1))
ego_path = paths[agent:agent+1] # (1 x 3 x path_length-1) ego_path = paths[agent:agent+1] # (1, 3, path_length-1)
# (x,y,phi) of all vehicles # (x,y,phi) of all vehicles
poses = np.expand_dims(full_state[:, [0,1,3]], 2) # (nv x 3 x 1) poses = np.expand_dims(full_state[:, [0,1,3]], 2) # (nv, 3, 1)
diff = ego_path - poses diff = ego_path - poses
diff[:, 2, :] = to_circle(diff[:, 2, :]) diff[:, 2, :] = to_circle(diff[:, 2, :])
# Test if position and heading angle are close for some point on the future vehicle track # Test if position and heading angle are close for some point on the future vehicle track
max_pos_error = 1 max_pos_error = 2
pos_close = np.sum(diff[:, 0:2, :]**2, 1) <= max_pos_error**2 # (nv x path_length-1) pos_close = np.sum(diff[:, 0:2, :]**2, 1) <= max_pos_error**2 # (nv, path_length-1)
max_deg_error = 20 max_deg_error = 30
heading_close = np.abs(diff[:, 2, :]) <= 20 * np.pi / 180 # (nv x path_length-1) heading_close = np.abs(diff[:, 2, :]) <= max_deg_error * np.pi / 180 # (nv, path_length-1)
# For all vehicles get the path points where they are close to the ego path # For all vehicles get the path points where they are close to the ego path
close = np.logical_and(pos_close, heading_close) # (nv x path_length-1) close = np.logical_and(pos_close, heading_close) # (nv, path_length-1)
close[agent, :] = False # exclude ego agent close[agent, :] = False # exclude ego agent
leader = agent leader = agent