Flux Capacitor Model commaai#33902

Merge commit '83476c501d426f4817cc8fd4f2c35e6f2647531a' into master-cit02-v4

# Conflicts:
#	selfdrive/modeld/fill_model_msg.py
#	selfdrive/modeld/modeld.py
#	selfdrive/modeld/models/supercombo.onnx

selfdrive/modeld/fill_model_msg.py

@@ -25,6 +25,15 @@
 
 ConfidenceClass = log.ModelDataV2.ConfidenceClass
 
+def curv_from_psis(psi_target, psi_rate, vego, delay):
+  curv_from_psi = psi_target / ((vego + 1e-3) * delay)  # epsilon to prevent divide-by-zero
+  return 2*curv_from_psi - psi_rate / (1e-3 + vego)
+
+def get_curvature_from_plan(plan, vego, delay):
+  psi_target = np.interp(delay, ModelConstants.T_IDXS, plan[:, Plan.T_FROM_CURRENT_EULER][:, 2])
+  psi_rate = plan[:, Plan.ORIENTATION_RATE][0, 2]
+  return curv_from_psis(psi_target, psi_rate, vego, delay)
+
 class PublishState:
   def __init__(self):
     self.disengage_buffer = np.zeros(ModelConstants.CONFIDENCE_BUFFER_LEN*ModelConstants.DISENGAGE_WIDTH, dtype=np.float32)
@@ -72,14 +81,17 @@ def fill_lane_line_meta(builder, lane_lines, lane_line_probs):
   builder.rightProb = lane_line_probs[2]
 
 def fill_model_msg(base_msg: capnp._DynamicStructBuilder, extended_msg: capnp._DynamicStructBuilder,
-                   net_output_data: dict[str, np.ndarray], publish_state: PublishState,
-                   vipc_frame_id: int, vipc_frame_id_extra: int, frame_id: int, frame_drop: float,
-                   timestamp_eof: int, model_execution_time: float, valid: bool , STEER_CTRL_Y: float, v_ego: float, DH) -> None:
+                   net_output_data: dict[str, np.ndarray], v_ego: float, delay: float,
+                   publish_state: PublishState, vipc_frame_id: int, vipc_frame_id_extra: int,
+                   frame_id: int, frame_drop: float, timestamp_eof: int, model_execution_time: float,
+                   valid: bool , STEER_CTRL_Y: float, v_ego: float, DH) -> None:
   frame_age = frame_id - vipc_frame_id if frame_id > vipc_frame_id else 0
   frame_drop_perc = frame_drop * 100
   extended_msg.valid = valid
   base_msg.valid = valid
 
+  desired_curv = float(get_curvature_from_plan(net_output_data['plan'][0], v_ego, delay))
+
   driving_model_data = base_msg.drivingModelData
 
   driving_model_data.frameId = vipc_frame_id
@@ -88,7 +100,7 @@ def fill_model_msg(base_msg: capnp._DynamicStructBuilder, extended_msg: capnp._D
   driving_model_data.modelExecutionTime = model_execution_time
 
   action = driving_model_data.action
-  action.desiredCurvature = float(net_output_data['desired_curvature'][0,0])
+  action.desiredCurvature = desired_curv
 
   modelV2 = extended_msg.modelV2
   modelV2.frameId = vipc_frame_id
@@ -123,7 +135,7 @@ def fill_model_msg(base_msg: capnp._DynamicStructBuilder, extended_msg: capnp._D
 
   # lateral planning
   action = modelV2.action
-  action.desiredCurvature = float(net_output_data['desired_curvature'][0,0])
+  action.desiredCurvature = desired_curv
 
   # times at X_IDXS according to model plan
   PLAN_T_IDXS = [np.nan] * ModelConstants.IDX_N

selfdrive/modeld/modeld.py

@@ -57,14 +57,11 @@ def __init__(self, context: CLContext):
     self.prev_desire = np.zeros(ModelConstants.DESIRE_LEN, dtype=np.float32)
     self.full_features_20Hz = np.zeros((ModelConstants.FULL_HISTORY_BUFFER_LEN, ModelConstants.FEATURE_LEN), dtype=np.float32)
     self.desire_20Hz =  np.zeros((ModelConstants.FULL_HISTORY_BUFFER_LEN + 1, ModelConstants.DESIRE_LEN), dtype=np.float32)
-    self.prev_desired_curv_20hz = np.zeros((ModelConstants.FULL_HISTORY_BUFFER_LEN + 1, ModelConstants.PREV_DESIRED_CURV_LEN), dtype=np.float32)
 
     # img buffers are managed in openCL transform code
     self.inputs = {
       'desire': np.zeros(ModelConstants.DESIRE_LEN * (ModelConstants.HISTORY_BUFFER_LEN+1), dtype=np.float32),
       'traffic_convention': np.zeros(ModelConstants.TRAFFIC_CONVENTION_LEN, dtype=np.float32),
-      'lateral_control_params': np.zeros(ModelConstants.LATERAL_CONTROL_PARAMS_LEN, dtype=np.float32),
-      'prev_desired_curv': np.zeros(ModelConstants.PREV_DESIRED_CURV_LEN * (ModelConstants.HISTORY_BUFFER_LEN+1), dtype=np.float32),
       'features_buffer': np.zeros(ModelConstants.HISTORY_BUFFER_LEN * ModelConstants.FEATURE_LEN, dtype=np.float32),
     }
 
@@ -100,7 +97,6 @@ def run(self, buf: VisionBuf, wbuf: VisionBuf, transform: np.ndarray, transform_
     self.inputs['desire'][:] = self.desire_20Hz.reshape((25,4,-1)).max(axis=1).flatten()
 
     self.inputs['traffic_convention'][:] = inputs['traffic_convention']
-    self.inputs['lateral_control_params'][:] = inputs['lateral_control_params']
 
     self.model.setInputBuffer("input_imgs", self.frame.prepare(buf, transform.flatten(), self.model.getCLBuffer("input_imgs")))
     self.model.setInputBuffer("big_input_imgs", self.wide_frame.prepare(wbuf, transform_wide.flatten(), self.model.getCLBuffer("big_input_imgs")))
@@ -114,13 +110,8 @@ def run(self, buf: VisionBuf, wbuf: VisionBuf, transform: np.ndarray, transform_
     self.full_features_20Hz[:-1] = self.full_features_20Hz[1:]
     self.full_features_20Hz[-1] = outputs['hidden_state'][0, :]
 
-    self.prev_desired_curv_20hz[:-1] = self.prev_desired_curv_20hz[1:]
-    self.prev_desired_curv_20hz[-1] = outputs['desired_curvature'][0, :]
-
     idxs = np.arange(-4,-100,-4)[::-1]
     self.inputs['features_buffer'][:] = self.full_features_20Hz[idxs].flatten()
-    # TODO model only uses last value now, once that changes we need to input strided action history buffer
-    self.inputs['prev_desired_curv'][-ModelConstants.PREV_DESIRED_CURV_LEN:] = 0. * self.prev_desired_curv_20hz[-4, :]
     return outputs
 
 
@@ -231,7 +222,6 @@ def main(demo=False):
     is_rhd = sm["driverMonitoringState"].isRHD
     frame_id = sm["roadCameraState"].frameId
     v_ego = max(sm["carState"].vEgo, 0.)
-    lateral_control_params = np.array([v_ego, steer_delay], dtype=np.float32)
     if sm.updated["liveCalibration"] and sm.seen['roadCameraState'] and sm.seen['deviceState']:
       device_from_calib_euler = np.array(sm["liveCalibration"].rpyCalib, dtype=np.float32)
       dc = DEVICE_CAMERAS[(str(sm['deviceState'].deviceType), str(sm['roadCameraState'].sensor))]
@@ -262,7 +252,6 @@ def main(demo=False):
     inputs:dict[str, np.ndarray] = {
       'desire': vec_desire,
       'traffic_convention': traffic_convention,
-      'lateral_control_params': lateral_control_params,
       }
 
     mt1 = time.perf_counter()
@@ -274,7 +263,8 @@ def main(demo=False):
       modelv2_send = messaging.new_message('modelV2')
       drivingdata_send = messaging.new_message('drivingModelData')
       posenet_send = messaging.new_message('cameraOdometry')
-      fill_model_msg(drivingdata_send, modelv2_send, model_output, publish_state, meta_main.frame_id, meta_extra.frame_id, frame_id,
+      fill_model_msg(drivingdata_send, modelv2_send, model_output, v_ego, steer_delay,
+                     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, sm['carState'].steeringAngleDeg, sm["carState"].vEgo, DH)
 
       desire_state = modelv2_send.modelV2.meta.desireState