Tomb raider 2 (#35029)

* 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

selfdrive/controls/lib/longitudinal_planner.py

@@ -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)

selfdrive/modeld/fill_model_msg.py

@@ -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)

selfdrive/modeld/modeld.py

@@ -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)

selfdrive/modeld/models/driving_policy.onnx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:98f0121ccb6f850077b04cc91bd33d370fc6cbdc2bd35f1ab55628a15a813f36
-size 15966721
+oid sha256:fa00a07a4307b0f1638a10ec33b651d8a102e38371289b744f43215c89dfd342
+size 15578328

selfdrive/modeld/models/driving_vision.onnx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:897f80d0388250f99bba69b6a8434560cc0fd83157cbeb0bc134c67fe4e64624
-size 34882971
+oid sha256:f222d2c528f1763828de01bb55e8979b1e4056e1dbb41350f521d2d2bb09d177
+size 46265585

selfdrive/modeld/parse_model_outputs.py

@@ -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
 

selfdrive/test/longitudinal_maneuvers/plant.py

@@ -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

selfdrive/test/process_replay/ref_commit

@@ -1 +1 @@
-3d3f875dc211ab0ca8a8e564ecf6dd3f52fcf7d9
+0800e73b5d9c801f161b9559557c0559b0682fec