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Tomb raider 2 (#35029)

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* db56b8fb-9135-4ab6-af18-99b7df7b2245/400

* fixes

* linter unhappy

* 6dbe0991-baa1-49ad-836a-ab370d1f0d92/400

* This one is good: 19387087-1005-475e-9015-9458dd8e7c5f/400

* Better every day: 39ed911c-0937-417f-97d2-58a8bb3caa53/400

* Actually end-to-end

* typo

* smooooooth: 94e23541-eb84-4fef-9f51-6a2d82aff314/360

* Revert "smooooooth: 94e23541-eb84-4fef-9f51-6a2d82aff314/360"

This reverts commit edd4f02386d83d82dd8a188985cde80ed1646b7f.

* 11632ef7-f555-489c-8480-e3bf97d9285e/400

* 08712d27-f6bd-4536-a30e-c729e5f62356/400

* 0a92a35e-1f72-476a-8cb6-c9f103f36822/400

* ee6d2394-2072-420c-a664-b4c0d4ed0b61/400

* no prev curv

* No double work

* fix bug

* smooth

* update prev action

* whitespace

* add little accel

* new ref

* Update plant.py
This commit is contained in:
Harald Schäfer
2025-04-17 23:21:25 -07:00
committed by GitHub
parent 65c210265b
commit 2c162d9b75
8 changed files with 36 additions and 25 deletions
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20
selfdrive/controls/lib/longitudinal_planner.py
View File

@@ -89,14 +89,15 @@ class LongitudinalPlanner:
return x, v, a, j, throttle_prob
def update(self, sm):
self.mpc.mode = 'blended' if sm['selfdriveState'].experimentalMode else 'acc'
self.mpc.mode = 'acc'
self.mode = 'blended' if sm['selfdriveState'].experimentalMode else 'acc'
if len(sm['carControl'].orientationNED) == 3:
accel_coast = get_coast_accel(sm['carControl'].orientationNED[1])
else:
accel_coast = ACCEL_MAX
v_ego = sm['carState'].vEgo
v_ego = sm['modelV2'].velocity.x[0]
v_cruise_kph = min(sm['carState'].vCruise, V_CRUISE_MAX)
v_cruise = v_cruise_kph * CV.KPH_TO_MS
v_cruise_initialized = sm['carState'].vCruise != V_CRUISE_UNSET
@@ -112,7 +113,7 @@ class LongitudinalPlanner:
# No change cost when user is controlling the speed, or when standstill
prev_accel_constraint = not (reset_state or sm['carState'].standstill)
if self.mpc.mode == 'acc':
if self.mode == 'acc':
accel_clip = [ACCEL_MIN, get_max_accel(v_ego)]
steer_angle_without_offset = sm['carState'].steeringAngleDeg - sm['liveParameters'].angleOffsetDeg
accel_clip = limit_accel_in_turns(v_ego, steer_angle_without_offset, accel_clip, self.CP)
@@ -128,7 +129,7 @@ class LongitudinalPlanner:
self.v_desired_filter.x = max(0.0, self.v_desired_filter.update(v_ego))
# Compute model v_ego error
self.v_model_error = get_speed_error(sm['modelV2'], v_ego)
x, v, a, j, throttle_prob = self.parse_model(sm['modelV2'], self.v_model_error)
x, v, a, j, throttle_prob = self.parse_model(sm['modelV2'], 0)
# Don't clip at low speeds since throttle_prob doesn't account for creep
self.allow_throttle = throttle_prob > ALLOW_THROTTLE_THRESHOLD or v_ego <= MIN_ALLOW_THROTTLE_SPEED
@@ -159,8 +160,17 @@ class LongitudinalPlanner:
self.v_desired_filter.x = self.v_desired_filter.x + self.dt * (self.a_desired + a_prev) / 2.0
action_t = self.CP.longitudinalActuatorDelay + DT_MDL
output_a_target, self.output_should_stop = get_accel_from_plan(self.v_desired_trajectory, self.a_desired_trajectory, CONTROL_N_T_IDX,
output_a_target_mpc, output_should_stop_mpc = get_accel_from_plan(self.v_desired_trajectory, self.a_desired_trajectory, CONTROL_N_T_IDX,
action_t=action_t, vEgoStopping=self.CP.vEgoStopping)
output_a_target_e2e = sm['modelV2'].action.desiredAcceleration
output_should_stop_e2e = sm['modelV2'].action.shouldStop
if self.mode == 'acc':
output_a_target = output_a_target_mpc
self.output_should_stop = output_should_stop_mpc
else:
output_a_target = min(output_a_target_mpc, output_a_target_e2e)
self.output_should_stop = output_should_stop_e2e or output_should_stop_mpc
for idx in range(2):
accel_clip[idx] = np.clip(accel_clip[idx], self.prev_accel_clip[idx] - 0.05, self.prev_accel_clip[idx] + 0.05)

10
selfdrive/modeld/fill_model_msg.py
View File

@@ -90,11 +90,11 @@ def fill_model_msg(base_msg: capnp._DynamicStructBuilder, extended_msg: capnp._D
fill_xyzt(modelV2.orientationRate, ModelConstants.T_IDXS, *net_output_data['plan'][0,:,Plan.ORIENTATION_RATE].T)
# temporal pose
temporal_pose = modelV2.temporalPose
temporal_pose.trans = net_output_data['sim_pose'][0,:ModelConstants.POSE_WIDTH//2].tolist()
temporal_pose.transStd = net_output_data['sim_pose_stds'][0,:ModelConstants.POSE_WIDTH//2].tolist()
temporal_pose.rot = net_output_data['sim_pose'][0,ModelConstants.POSE_WIDTH//2:].tolist()
temporal_pose.rotStd = net_output_data['sim_pose_stds'][0,ModelConstants.POSE_WIDTH//2:].tolist()
#temporal_pose = modelV2.temporalPose
#temporal_pose.trans = net_output_data['sim_pose'][0,:ModelConstants.POSE_WIDTH//2].tolist()
#temporal_pose.transStd = net_output_data['sim_pose_stds'][0,:ModelConstants.POSE_WIDTH//2].tolist()
#temporal_pose.rot = net_output_data['sim_pose'][0,ModelConstants.POSE_WIDTH//2:].tolist()
#temporal_pose.rotStd = net_output_data['sim_pose_stds'][0,ModelConstants.POSE_WIDTH//2:].tolist()
# poly path
fill_xyz_poly(driving_model_data.path, ModelConstants.POLY_PATH_DEGREE, *net_output_data['plan'][0,:,Plan.POSITION].T)

7
selfdrive/modeld/modeld.py
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@@ -43,8 +43,8 @@ POLICY_PKL_PATH = Path(__file__).parent / 'models/driving_policy_tinygrad.pkl'
VISION_METADATA_PATH = Path(__file__).parent / 'models/driving_vision_metadata.pkl'
POLICY_METADATA_PATH = Path(__file__).parent / 'models/driving_policy_metadata.pkl'
LAT_SMOOTH_SECONDS = 0.0
LONG_SMOOTH_SECONDS = 0.0
LAT_SMOOTH_SECONDS = 0.3
LONG_SMOOTH_SECONDS = 0.3
def smooth_value(val, prev_val, tau):
alpha = 1 - np.exp(-DT_MDL / tau) if tau > 0 else 1
@@ -174,7 +174,7 @@ class ModelState:
# TODO model only uses last value now
self.full_prev_desired_curv[0,:-1] = self.full_prev_desired_curv[0,1:]
self.full_prev_desired_curv[0,-1,:] = policy_outputs_dict['desired_curvature'][0, :]
self.numpy_inputs['prev_desired_curv'][:] = self.full_prev_desired_curv[0, self.temporal_idxs]
self.numpy_inputs['prev_desired_curv'][:] = 0*self.full_prev_desired_curv[0, self.temporal_idxs]
combined_outputs_dict = {**vision_outputs_dict, **policy_outputs_dict}
if SEND_RAW_PRED:
@@ -338,6 +338,7 @@ def main(demo=False):
posenet_send = messaging.new_message('cameraOdometry')
action = get_action_from_model(model_output, prev_action, lat_delay + DT_MDL, long_delay + DT_MDL)
prev_action = action
fill_model_msg(drivingdata_send, modelv2_send, model_output, action,
publish_state, meta_main.frame_id, meta_extra.frame_id, frame_id,
frame_drop_ratio, meta_main.timestamp_eof, model_execution_time, live_calib_seen)

BIN
selfdrive/modeld/models/driving_policy.onnx LFS
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selfdrive/modeld/models/driving_vision.onnx LFS
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13
selfdrive/modeld/parse_model_outputs.py
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@@ -88,6 +88,12 @@ class Parser:
self.parse_mdn('pose', outs, in_N=0, out_N=0, out_shape=(ModelConstants.POSE_WIDTH,))
self.parse_mdn('wide_from_device_euler', outs, in_N=0, out_N=0, out_shape=(ModelConstants.WIDE_FROM_DEVICE_WIDTH,))
self.parse_mdn('road_transform', outs, in_N=0, out_N=0, out_shape=(ModelConstants.POSE_WIDTH,))
self.parse_mdn('lane_lines', outs, in_N=0, out_N=0, out_shape=(ModelConstants.NUM_LANE_LINES,ModelConstants.IDX_N,ModelConstants.LANE_LINES_WIDTH))
self.parse_mdn('road_edges', outs, in_N=0, out_N=0, out_shape=(ModelConstants.NUM_ROAD_EDGES,ModelConstants.IDX_N,ModelConstants.LANE_LINES_WIDTH))
self.parse_mdn('lead', outs, in_N=ModelConstants.LEAD_MHP_N, out_N=ModelConstants.LEAD_MHP_SELECTION,
out_shape=(ModelConstants.LEAD_TRAJ_LEN,ModelConstants.LEAD_WIDTH))
for k in ['lead_prob', 'lane_lines_prob']:
self.parse_binary_crossentropy(k, outs)
self.parse_categorical_crossentropy('desire_pred', outs, out_shape=(ModelConstants.DESIRE_PRED_LEN,ModelConstants.DESIRE_PRED_WIDTH))
self.parse_binary_crossentropy('meta', outs)
return outs
@@ -95,17 +101,10 @@ class Parser:
def parse_policy_outputs(self, outs: dict[str, np.ndarray]) -> dict[str, np.ndarray]:
self.parse_mdn('plan', outs, in_N=ModelConstants.PLAN_MHP_N, out_N=ModelConstants.PLAN_MHP_SELECTION,
out_shape=(ModelConstants.IDX_N,ModelConstants.PLAN_WIDTH))
self.parse_mdn('lane_lines', outs, in_N=0, out_N=0, out_shape=(ModelConstants.NUM_LANE_LINES,ModelConstants.IDX_N,ModelConstants.LANE_LINES_WIDTH))
self.parse_mdn('road_edges', outs, in_N=0, out_N=0, out_shape=(ModelConstants.NUM_ROAD_EDGES,ModelConstants.IDX_N,ModelConstants.LANE_LINES_WIDTH))
self.parse_mdn('sim_pose', outs, in_N=0, out_N=0, out_shape=(ModelConstants.POSE_WIDTH,))
self.parse_mdn('lead', outs, in_N=ModelConstants.LEAD_MHP_N, out_N=ModelConstants.LEAD_MHP_SELECTION,
out_shape=(ModelConstants.LEAD_TRAJ_LEN,ModelConstants.LEAD_WIDTH))
if 'lat_planner_solution' in outs:
self.parse_mdn('lat_planner_solution', outs, in_N=0, out_N=0, out_shape=(ModelConstants.IDX_N,ModelConstants.LAT_PLANNER_SOLUTION_WIDTH))
if 'desired_curvature' in outs:
self.parse_mdn('desired_curvature', outs, in_N=0, out_N=0, out_shape=(ModelConstants.DESIRED_CURV_WIDTH,))
for k in ['lead_prob', 'lane_lines_prob']:
self.parse_binary_crossentropy(k, outs)
self.parse_categorical_crossentropy('desire_state', outs, out_shape=(ModelConstants.DESIRE_PRED_WIDTH,))
return outs

1
selfdrive/test/longitudinal_maneuvers/plant.py
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@@ -107,6 +107,7 @@ class Plant:
position = log.XYZTData.new_message()
position.x = [float(x) for x in (self.speed + 0.5) * np.array(ModelConstants.T_IDXS)]
model.modelV2.position = position
model.modelV2.action.desiredAcceleration = float(self.acceleration + 0.1)
velocity = log.XYZTData.new_message()
velocity.x = [float(x) for x in (self.speed + 0.5) * np.ones_like(ModelConstants.T_IDXS)]
velocity.x[0] = float(self.speed) # always start at current speed

2
selfdrive/test/process_replay/ref_commit
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@@ -1 +1 @@
3d3f875dc211ab0ca8a8e564ecf6dd3f52fcf7d9
0800e73b5d9c801f161b9559557c0559b0682fec