7fa972be6a
version: sunnypilot v2025.003.000 (dev) date: 2026-02-09T02:04:38 master commit: 254f55ac15a40343d7255f2f098de3442e0c4a6f
224 lines
11 KiB
Python
224 lines
11 KiB
Python
import os
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import capnp
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import numpy as np
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from cereal import log
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from openpilot.sunnypilot.modeld.constants import ModelConstants, Plan
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from openpilot.sunnypilot.models.helpers import plan_x_idxs_helper
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from openpilot.sunnypilot.selfdrive.controls.lib.drive_helpers import CONTROL_N, get_lag_adjusted_curvature, MIN_SPEED
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SEND_RAW_PRED = os.getenv('SEND_RAW_PRED')
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ConfidenceClass = log.ModelDataV2.ConfidenceClass
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class PublishState:
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def __init__(self):
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self.disengage_buffer = np.zeros(ModelConstants.CONFIDENCE_BUFFER_LEN*ModelConstants.DISENGAGE_WIDTH, dtype=np.float32)
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self.prev_brake_5ms2_probs = np.zeros(ModelConstants.FCW_5MS2_PROBS_WIDTH, dtype=np.float32)
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self.prev_brake_3ms2_probs = np.zeros(ModelConstants.FCW_3MS2_PROBS_WIDTH, dtype=np.float32)
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def fill_xyzt(builder, t, x, y, z, x_std=None, y_std=None, z_std=None):
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builder.t = t
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builder.x = x.tolist()
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builder.y = y.tolist()
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builder.z = z.tolist()
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if x_std is not None:
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builder.xStd = x_std.tolist()
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if y_std is not None:
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builder.yStd = y_std.tolist()
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if z_std is not None:
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builder.zStd = z_std.tolist()
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def fill_xyvat(builder, t, x, y, v, a, x_std=None, y_std=None, v_std=None, a_std=None):
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builder.t = t
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builder.x = x.tolist()
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builder.y = y.tolist()
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builder.v = v.tolist()
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builder.a = a.tolist()
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if x_std is not None:
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builder.xStd = x_std.tolist()
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if y_std is not None:
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builder.yStd = y_std.tolist()
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if v_std is not None:
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builder.vStd = v_std.tolist()
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if a_std is not None:
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builder.aStd = a_std.tolist()
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def fill_xyz_poly(builder, degree, x, y, z):
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xyz = np.stack([x, y, z], axis=1)
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coeffs = np.polynomial.polynomial.polyfit(ModelConstants.T_IDXS, xyz, deg=degree)
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builder.xCoefficients = coeffs[:, 0].tolist()
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builder.yCoefficients = coeffs[:, 1].tolist()
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builder.zCoefficients = coeffs[:, 2].tolist()
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def fill_model_msg(base_msg: capnp._DynamicStructBuilder, extended_msg: capnp._DynamicStructBuilder,
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net_output_data: dict[str, np.ndarray], action: log.ModelDataV2.Action, publish_state: PublishState,
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vipc_frame_id: int, vipc_frame_id_extra: int, frame_id: int, frame_drop: float,
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timestamp_eof: int, model_execution_time: float, valid: bool,
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v_ego: float, steer_delay: float, meta_const) -> None:
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frame_age = frame_id - vipc_frame_id if frame_id > vipc_frame_id else 0
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frame_drop_perc = frame_drop * 100
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extended_msg.valid = valid
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base_msg.valid = valid
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if 'lat_planner_solution' in net_output_data:
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x, y, yaw, yawRate = [net_output_data['lat_planner_solution'][0, :, i].tolist() for i in range(4)]
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x_sol = np.column_stack([x, y, yaw, yawRate])
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v_ego = max(MIN_SPEED, v_ego)
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psis = x_sol[0:CONTROL_N, 2].tolist()
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curvatures = (x_sol[0:CONTROL_N, 3] / v_ego).tolist()
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desired_curvature = get_lag_adjusted_curvature(steer_delay, v_ego, psis, curvatures)
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else:
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desired_curvature = float(net_output_data['desired_curvature'][0, 0])
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driving_model_data = base_msg.drivingModelData
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driving_model_data.frameId = vipc_frame_id
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driving_model_data.frameIdExtra = vipc_frame_id_extra
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driving_model_data.frameDropPerc = frame_drop_perc
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driving_model_data.modelExecutionTime = model_execution_time
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# Populate drivingModelData.action
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driving_model_data_action = driving_model_data.action
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driving_model_data_action.desiredAcceleration = action.desiredAcceleration
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driving_model_data_action.shouldStop = action.shouldStop
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driving_model_data_action.desiredCurvature = desired_curvature
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modelV2 = extended_msg.modelV2
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modelV2.frameId = vipc_frame_id
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modelV2.frameIdExtra = vipc_frame_id_extra
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modelV2.frameAge = frame_age
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modelV2.frameDropPerc = frame_drop_perc
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modelV2.timestampEof = timestamp_eof
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modelV2.modelExecutionTime = model_execution_time
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# plan
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position = modelV2.position
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fill_xyzt(position, ModelConstants.T_IDXS, *net_output_data['plan'][0,:,Plan.POSITION].T, *net_output_data['plan_stds'][0,:,Plan.POSITION].T)
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velocity = modelV2.velocity
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fill_xyzt(velocity, ModelConstants.T_IDXS, *net_output_data['plan'][0,:,Plan.VELOCITY].T)
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acceleration = modelV2.acceleration
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fill_xyzt(acceleration, ModelConstants.T_IDXS, *net_output_data['plan'][0,:,Plan.ACCELERATION].T)
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orientation = modelV2.orientation
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fill_xyzt(orientation, ModelConstants.T_IDXS, *net_output_data['plan'][0,:,Plan.T_FROM_CURRENT_EULER].T)
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orientation_rate = modelV2.orientationRate
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fill_xyzt(orientation_rate, ModelConstants.T_IDXS, *net_output_data['plan'][0,:,Plan.ORIENTATION_RATE].T)
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# temporal pose
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temporal_pose = modelV2.temporalPoseDEPRECATED
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temporal_pose.trans = net_output_data['plan'][0,0,Plan.VELOCITY].tolist()
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temporal_pose.transStd = net_output_data['plan_stds'][0,0,Plan.VELOCITY].tolist()
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temporal_pose.rot = net_output_data['plan'][0,0,Plan.ORIENTATION_RATE].tolist()
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temporal_pose.rotStd = net_output_data['plan_stds'][0,0,Plan.ORIENTATION_RATE].tolist()
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# poly path
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poly_path = driving_model_data.path
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fill_xyz_poly(poly_path, ModelConstants.POLY_PATH_DEGREE, *net_output_data['plan'][0,:,Plan.POSITION].T)
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# lateral planning
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modelV2_action = modelV2.action
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modelV2_action.desiredAcceleration = action.desiredAcceleration
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modelV2_action.shouldStop = action.shouldStop
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modelV2_action.desiredCurvature = desired_curvature
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# times at X_IDXS according to model plan
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PLAN_T_IDXS: list[float] = plan_x_idxs_helper(ModelConstants, Plan, net_output_data)
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# lane lines
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modelV2.init('laneLines', 4)
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for i in range(4):
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lane_line = modelV2.laneLines[i]
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fill_xyzt(lane_line, PLAN_T_IDXS, np.array(ModelConstants.X_IDXS), net_output_data['lane_lines'][0,i,:,0], net_output_data['lane_lines'][0,i,:,1])
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modelV2.laneLineStds = net_output_data['lane_lines_stds'][0,:,0,0].tolist()
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modelV2.laneLineProbs = net_output_data['lane_lines_prob'][0,1::2].tolist()
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lane_line_meta = driving_model_data.laneLineMeta
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lane_line_meta.leftY = modelV2.laneLines[1].y[0]
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lane_line_meta.leftProb = modelV2.laneLineProbs[1]
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lane_line_meta.rightY = modelV2.laneLines[2].y[0]
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lane_line_meta.rightProb = modelV2.laneLineProbs[2]
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# road edges
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modelV2.init('roadEdges', 2)
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for i in range(2):
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road_edge = modelV2.roadEdges[i]
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fill_xyzt(road_edge, PLAN_T_IDXS, np.array(ModelConstants.X_IDXS), net_output_data['road_edges'][0,i,:,0], net_output_data['road_edges'][0,i,:,1])
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modelV2.roadEdgeStds = net_output_data['road_edges_stds'][0,:,0,0].tolist()
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# leads
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modelV2.init('leadsV3', 3)
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for i in range(3):
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lead = modelV2.leadsV3[i]
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fill_xyvat(lead, ModelConstants.LEAD_T_IDXS, *net_output_data['lead'][0,i].T, *net_output_data['lead_stds'][0,i].T)
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lead.prob = net_output_data['lead_prob'][0,i].tolist()
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lead.probTime = ModelConstants.LEAD_T_OFFSETS[i]
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# meta
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meta = modelV2.meta
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meta.desireState = net_output_data['desire_state'][0].reshape(-1).tolist()
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meta.desirePrediction = net_output_data['desire_pred'][0].reshape(-1).tolist()
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meta.engagedProb = net_output_data['meta'][0,meta_const.ENGAGED].item()
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meta.init('disengagePredictions')
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disengage_predictions = meta.disengagePredictions
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disengage_predictions.t = ModelConstants.META_T_IDXS
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disengage_predictions.brakeDisengageProbs = net_output_data['meta'][0,meta_const.BRAKE_DISENGAGE].tolist()
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disengage_predictions.gasDisengageProbs = net_output_data['meta'][0,meta_const.GAS_DISENGAGE].tolist()
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disengage_predictions.steerOverrideProbs = net_output_data['meta'][0,meta_const.STEER_OVERRIDE].tolist()
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disengage_predictions.brake3MetersPerSecondSquaredProbs = net_output_data['meta'][0,meta_const.HARD_BRAKE_3].tolist()
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disengage_predictions.brake4MetersPerSecondSquaredProbs = net_output_data['meta'][0,meta_const.HARD_BRAKE_4].tolist()
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disengage_predictions.brake5MetersPerSecondSquaredProbs = net_output_data['meta'][0,meta_const.HARD_BRAKE_5].tolist()
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if 'sim_pose' not in net_output_data:
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disengage_predictions.gasPressProbs = net_output_data['meta'][0,meta_const.GAS_PRESS].tolist()
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disengage_predictions.brakePressProbs = net_output_data['meta'][0,meta_const.BRAKE_PRESS].tolist()
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publish_state.prev_brake_5ms2_probs[:-1] = publish_state.prev_brake_5ms2_probs[1:]
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publish_state.prev_brake_5ms2_probs[-1] = net_output_data['meta'][0,meta_const.HARD_BRAKE_5][0]
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publish_state.prev_brake_3ms2_probs[:-1] = publish_state.prev_brake_3ms2_probs[1:]
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publish_state.prev_brake_3ms2_probs[-1] = net_output_data['meta'][0,meta_const.HARD_BRAKE_3][0]
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hard_brake_predicted = (publish_state.prev_brake_5ms2_probs > ModelConstants.FCW_THRESHOLDS_5MS2).all() and \
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(publish_state.prev_brake_3ms2_probs > ModelConstants.FCW_THRESHOLDS_3MS2).all()
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meta.hardBrakePredicted = hard_brake_predicted.item()
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# confidence
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if vipc_frame_id % (2*ModelConstants.MODEL_FREQ) == 0:
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# any disengage prob
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brake_disengage_probs = net_output_data['meta'][0,meta_const.BRAKE_DISENGAGE]
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gas_disengage_probs = net_output_data['meta'][0,meta_const.GAS_DISENGAGE]
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steer_override_probs = net_output_data['meta'][0,meta_const.STEER_OVERRIDE]
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any_disengage_probs = 1-((1-brake_disengage_probs)*(1-gas_disengage_probs)*(1-steer_override_probs))
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# independent disengage prob for each 2s slice
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ind_disengage_probs = np.r_[any_disengage_probs[0], np.diff(any_disengage_probs) / (1 - any_disengage_probs[:-1])]
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# rolling buf for 2, 4, 6, 8, 10s
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publish_state.disengage_buffer[:-ModelConstants.DISENGAGE_WIDTH] = publish_state.disengage_buffer[ModelConstants.DISENGAGE_WIDTH:]
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publish_state.disengage_buffer[-ModelConstants.DISENGAGE_WIDTH:] = ind_disengage_probs
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score = 0.
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for i in range(ModelConstants.DISENGAGE_WIDTH):
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score += publish_state.disengage_buffer[i*ModelConstants.DISENGAGE_WIDTH+ModelConstants.DISENGAGE_WIDTH-1-i].item() / ModelConstants.DISENGAGE_WIDTH
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if score < ModelConstants.RYG_GREEN:
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modelV2.confidence = ConfidenceClass.green
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elif score < ModelConstants.RYG_YELLOW:
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modelV2.confidence = ConfidenceClass.yellow
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else:
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modelV2.confidence = ConfidenceClass.red
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# raw prediction if enabled
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if SEND_RAW_PRED:
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modelV2.rawPredictions = net_output_data['raw_pred'].tobytes()
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def fill_pose_msg(msg: capnp._DynamicStructBuilder, net_output_data: dict[str, np.ndarray],
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vipc_frame_id: int, vipc_dropped_frames: int, timestamp_eof: int, live_calib_seen: bool) -> None:
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msg.valid = live_calib_seen & (vipc_dropped_frames < 1)
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cameraOdometry = msg.cameraOdometry
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cameraOdometry.frameId = vipc_frame_id
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cameraOdometry.timestampEof = timestamp_eof
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cameraOdometry.trans = net_output_data['pose'][0,:3].tolist()
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cameraOdometry.rot = net_output_data['pose'][0,3:].tolist()
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cameraOdometry.wideFromDeviceEuler = net_output_data['wide_from_device_euler'][0,:].tolist()
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cameraOdometry.roadTransformTrans = net_output_data['road_transform'][0,:3].tolist()
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cameraOdometry.transStd = net_output_data['pose_stds'][0,:3].tolist()
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cameraOdometry.rotStd = net_output_data['pose_stds'][0,3:].tolist()
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cameraOdometry.wideFromDeviceEulerStd = net_output_data['wide_from_device_euler_stds'][0,:].tolist()
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cameraOdometry.roadTransformTransStd = net_output_data['road_transform_stds'][0,:3].tolist()
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