package apollo.prediction

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Used in: LayerParameter

• optional string activation = 1

Used in: LayerParameter

• optional int32 kernel_size = 1

• optional int32 stride = 2

Used in: LayerParameter

• optional double epsilon = 1

• optional int32 axis = 2

• optional bool center = 3

• optional bool scale = 4

• optional float momentum = 5

• optional TensorParameter mu = 6

• optional TensorParameter sigma = 7

• optional TensorParameter gamma = 8

• optional TensorParameter beta = 9

Used in: LayerParameter

• optional int32 axis = 1

Used in: LayerParameter

• repeated int32 shape = 1

• optional bool use_bias = 2

• optional TensorParameter kernel = 3

• optional TensorParameter bias = 4

• optional int32 stride = 5

Used in: ListDataForLearning

• optional string category = 5

  The task category of the data for learning

• optional int32 id = 1

  The info. needed for identifying a unique data point:

• optional double timestamp = 2

• repeated double features_for_learning = 3

  The features for learning algorithms:

• repeated string string_features_for_learning = 7

• repeated double labels = 4

  The ground-truth labels:

• optional int32 lane_sequence_id = 6

  The lane sequence id if associated with a lane

Used in: ListDataForTuning

• optional string category = 1

  The task category of the cost values

• optional int32 id = 2

  The info. needed for identifying a unique data point:

• optional double timestamp = 3

• repeated double values_for_tuning = 4

  The cost values whose coefficients to be tuned

• repeated double real_cost_value = 5

  The cost values of the real future trajectory

• optional int32 lane_sequence_id = 6

  The lane sequence id if associated with a lane

• repeated common.TrajectoryPoint adc_trajectory_point = 7

  The associated adc trajectory

Used in: LayerParameter

• optional int32 units = 1

• optional string activation = 2

• optional bool use_bias = 3

• optional TensorParameter weights = 4

• optional TensorParameter bias = 5

next id = 37

Used in: Features, ObstacleHistory, PredictionObstacle

• optional int32 id = 1

  Obstacle ID

• repeated common.Point3D polygon_point = 30

  Obstacle features

• optional common.Point3D position = 2

• optional common.Point3D front_position = 27

• optional common.Point3D velocity = 3

• optional common.Point3D raw_velocity = 28

  from perception

• optional common.Point3D acceleration = 4

• optional double velocity_heading = 5

• optional double speed = 6

• optional double acc = 7

• optional double theta = 8

• optional double length = 9

• optional double width = 10

• optional double height = 11

• optional double tracking_time = 12

• optional double timestamp = 13

• optional Lane lane = 14

  Obstacle type-specific features

• optional JunctionFeature junction_feature = 26

• optional common.Point3D t_position = 16

  Obstacle tracked features

• optional common.Point3D t_velocity = 17

• optional double t_velocity_heading = 18

• optional double t_speed = 19

• optional common.Point3D t_acceleration = 20

• optional double t_acc = 21

• optional bool is_still = 22

• optional perception.PerceptionObstacle.Type type = 23

• optional double label_update_time_delta = 24

• optional ObstaclePriority priority = 25

• optional bool is_near_junction = 29

• repeated PredictionTrajectoryPoint future_trajectory_points = 31

  Obstacle ground-truth labels:

• repeated common.TrajectoryPoint short_term_predicted_trajectory_points = 32

  Obstacle short-term predicted trajectory points

• repeated Trajectory predicted_trajectory = 33

  Obstacle predicted trajectories

• repeated common.TrajectoryPoint adc_trajectory_point = 34

  ADC trajectory at the same frame

• repeated string surrounding_lane_id = 35

  Surrounding lanes

• repeated string within_lane_id = 36

• repeated Feature feature = 1

Used in: LayerParameter

(message has no fields)

• optional int32 dim_input = 1

• optional Vector samples_mean = 2

• optional Vector samples_std = 3

• optional int32 num_layer = 4

• repeated Layer layer = 5

  num_layer must equal to first layer layer_input_dim

• optional int32 dim_output = 6

  dim_ouput must equal to last layer layer_output_dim

Used in: ListFrameEnv

• optional double timestamp = 1

• optional ObstacleHistory ego_history = 2

• repeated ObstacleHistory obstacles_history = 3

Used in: LayerParameter

• repeated int32 input_shape = 1

• optional string dtype = 2

  data type of the input

• optional bool sparse = 3

self driving car intent

Used in: PredictionObstacles

• optional Intent.Type type = 1

Used in: Intent

• UNKNOWN = 0

• STOP = 1

• CRUISE = 2

• CHANGE_LANE = 3

Used in: JunctionFeature

• optional string exit_lane_id = 1

• optional common.Point3D exit_position = 2

• optional double exit_heading = 3

• optional double exit_width = 4

Used in: Feature

• optional string junction_id = 1

• optional double junction_range = 2

• optional LaneFeature enter_lane = 3

• repeated JunctionExit junction_exit = 4

• repeated double junction_mlp_feature = 5

• repeated int32 junction_mlp_label = 6

  dim is number of masks, i.e. 12

• repeated double junction_mlp_probability = 7

• repeated string start_lane_id = 8

Used in: LayerParameter

• optional int32 units = 1

• optional bool return_sequences = 2

• optional bool stateful = 3

• optional string activation = 4

• optional string recurrent_activation = 5

• optional bool use_bias = 6

• optional bool unit_forget_bias = 7

• optional bool go_backwards = 8

• optional bool unroll = 9

• optional int32 implementation = 10

• optional TensorParameter weights_input = 15

• optional TensorParameter weights_forget = 16

• optional TensorParameter weights_cell = 17

• optional TensorParameter weights_output = 18

• optional TensorParameter bias_input = 19

• optional TensorParameter bias_forget = 20

• optional TensorParameter bias_cell = 21

• optional TensorParameter bias_output = 22

• optional TensorParameter recurrent_weights_input = 25

• optional TensorParameter recurrent_weights_forget = 26

• optional TensorParameter recurrent_weights_cell = 27

• optional TensorParameter recurrent_weights_output = 28

Used in: Feature

• repeated LaneFeature current_lane_feature = 1

  Features of all possible current lanes.

• optional LaneFeature lane_feature = 2

  Features of the most possible current lane.

• repeated LaneFeature nearby_lane_feature = 3

  Features of all nearby lanes.

• optional LaneGraph lane_graph = 4

  Lane graph

• optional LaneGraph lane_graph_ordered = 5

• optional double label_update_time_delta = 26

  For modeling

Used in: JunctionFeature, Lane

• optional string lane_id = 1

• optional uint32 lane_turn_type = 2

• optional double lane_s = 3

• optional double lane_l = 4

• optional double angle_diff = 5

• optional double dist_to_left_boundary = 6

• optional double dist_to_right_boundary = 7

• optional double lane_heading = 8

• optional hdmap.Lane.LaneType lane_type = 9

Used in: Lane

• repeated LaneSequence lane_sequence = 1

• optional int32 obstacle_id = 1

• optional string lane_id = 2

• optional double lane_s = 3

• optional double lane_l = 4

Used in: LaneSegment, LaneSequence

• optional common.Point3D position = 1

• optional double heading = 2

• optional double width = 3

• optional double relative_s = 4

  relative s to focus obstacle

• optional double relative_l = 5

  relative l to focus obstacle

• optional double angle_diff = 6

  lane point heading relative to focused obstacle heading

• optional double kappa = 7

• optional LanePoint.ScenarioType scenario_type = 8

• optional double speed_limit = 9

Used in: LanePoint

• URBAN_ROAD = 0

• JUNCTION = 1

• HIGHWAY = 2

Used in: LaneSequence

• optional string lane_id = 1

• optional double start_s = 2

  s offset of the first lane point

• optional double end_s = 3

  s offset of the last lane point

• optional uint32 lane_turn_type = 4

• repeated LanePoint lane_point = 5

• optional double adc_s = 7

  If this lane-segment is the one that's closest to ADC, then adc_s gives the s of the ADC.

• optional int32 adc_lane_point_idx = 8

• optional double total_length = 6

next id = 24

Used in: LaneGraph

• optional int32 lane_sequence_id = 1

• repeated LaneSegment lane_segment = 2

• optional int32 adc_lane_segment_idx = 23

  The index of the lane-segment that is closest to ADC:

• repeated common.PathPoint path_point = 8

• optional hdmap.Lane.LaneType lane_type = 22

• optional double lane_s = 17

• optional double lane_l = 18

• optional bool vehicle_on_lane = 10

• repeated NearbyObstacle nearby_obstacle = 3

  nearby obstacles on this lane sequence

• optional StopSign stop_sign = 20

• optional int32 right_of_way = 21

  right_of_way score

• optional LaneSequence.Features features = 4

• optional int32 label = 5

• optional double probability = 6

• optional double acceleration = 7

• optional double time_to_lane_center = 16

• optional double time_to_lane_edge = 19

• optional LaneSequence.BehaviorType behavior_type = 9

• repeated LanePoint curr_lane_point = 11

  lane points on this lane sequence

• repeated LanePoint left_neighbor_point = 12

  lane points on the left neighbor lane sequence

• repeated LanePoint right_neighbor_point = 13

  lane points on the right neighbor lane sequence

• repeated NearbyObstacle left_nearby_obstacle = 14

  nearby obstacles on the left neighbor lane sequence

• repeated NearbyObstacle right_nearby_obstacle = 15

  nearby obstacles on the right neighbor lane sequence

Used in: LaneSequence

• NOT_GOTO_LANE = 1

• CONSTANT_SPEED = 2

• SMALL_ACCELERATION = 3

• LARGE_ACCELERATION = 4

• SMALL_DECELERATION = 5

• LARGE_DECELERATION = 6

Used in: LaneSequence

• repeated double mlp_features = 1

Used in: FnnVehicleModel

• optional int32 layer_input_dim = 1

• optional int32 layer_output_dim = 2

• optional Matrix layer_input_weight = 3

  weight matrix of |input_dim| x |output_dim|

• optional Vector layer_bias = 4

  vector of bias, size of |output_dim|

• optional Layer.ActivationFunc layer_activation_func = 5

  optional string layer_activation_type = 6 [deprecated = true];

Used in: Layer

• RELU = 0

• TANH = 1

• SIGMOID = 2

• SOFTMAX = 3

Used in: NetParameter

• optional string type = 1

• optional string name = 2

• optional int32 order_number = 3

• oneof oneof_layers

  • InputParameter input = 4

  • ActivationParameter activation = 5

  • DenseParameter dense = 6

  • BatchNormalizationParameter batch_normalization = 7

  • LSTMParameter lstm = 8

  • FlattenParameter flatten = 9

  • ConcatenateParameter concatenate = 10

  • Conv1dParameter conv1d = 11

  • MaxPool1dParameter maxpool1d = 12

  • AvgPool1dParameter avgpool1d = 13

• repeated DataForLearning data_for_learning = 1

• repeated DataForTuning data_for_tuning = 1

• repeated FrameEnv frame_env = 1

• repeated PredictionResult prediction_result = 1

Used in: Layer

• repeated Vector rows = 1

Used in: LayerParameter

• optional int32 kernel_size = 1

• optional int32 stride = 2

Used in: LaneSequence

• optional int32 id = 1

• optional double s = 2

  relative to focus obstacle

• optional double l = 3

  relative to focus obstacle

• optional int32 id = 1

• optional string name = 2

• repeated LayerParameter layers = 3

• repeated VerificationSample verification_samples = 4

• optional Performance performance = 5

  performance of the model in test dataset

• optional float time_dumped = 6

  time [s] of dumped the model

Used in: PredictionConf, PredictionResult

• optional perception.PerceptionObstacle.Type obstacle_type = 1

• optional ObstacleConf.ObstacleStatus obstacle_status = 2

• optional ObstaclePriority.Priority priority_type = 5

• optional ObstacleConf.EvaluatorType evaluator_type = 3

• optional ObstacleConf.PredictorType predictor_type = 4

Used in: ObstacleConf

• MLP_EVALUATOR = 0

• RNN_EVALUATOR = 1

• COST_EVALUATOR = 2

  navi mode can only support this evaluator

• CRUISE_MLP_EVALUATOR = 3

• JUNCTION_MLP_EVALUATOR = 4

• CYCLIST_KEEP_LANE_EVALUATOR = 5

• LANE_SCANNING_EVALUATOR = 6

• PEDESTRIAN_INTERACTION_EVALUATOR = 7

• JUNCTION_MAP_EVALUATOR = 8

• LANE_AGGREGATING_EVALUATOR = 9

• SEMANTIC_LSTM_EVALUATOR = 10

Used in: ObstacleConf

• ON_LANE = 0

• OFF_LANE = 1

• STATIONARY = 3

• MOVING = 4

• IN_JUNCTION = 5

Used in: ObstacleConf

• LANE_SEQUENCE_PREDICTOR = 0

• FREE_MOVE_PREDICTOR = 1

• REGIONAL_PREDICTOR = 2

• MOVE_SEQUENCE_PREDICTOR = 3

• EMPTY_PREDICTOR = 4

• SINGLE_LANE_PREDICTOR = 5

• JUNCTION_PREDICTOR = 6

• EXTRAPOLATION_PREDICTOR = 7

• INTERACTION_PREDICTOR = 8

Used in: FrameEnv

• repeated Feature feature = 1

• optional bool is_trainable = 2

estimated obstacle intent

Used in: planning.PredictionObstacleFeature, PredictionObstacle

• optional ObstacleIntent.Type type = 1

Used in: ObstacleIntent

• UNKNOWN = 0

• STOP = 1

• STATIONARY = 2

• MOVING = 3

• CHANGE_LANE = 4

• LOW_ACCELERATION = 5

• HIGH_ACCELERATION = 6

• LOW_DECELERATION = 7

• HIGH_DECELERATION = 8

Used in: dreamview.Object, planning.PredictionObstacleFeature, Feature, PredictionObstacle

• optional ObstaclePriority.Priority priority = 25

Used in: ObstacleConf, ObstaclePriority

• CAUTION = 1

• NORMAL = 2

• IGNORE = 3

Used in: NetParameter

• repeated float accuracy = 1

• repeated float recall = 2

• repeated float precision = 3

• optional TopicConf topic_conf = 1

• repeated ObstacleConf obstacle_conf = 2

Used in: PredictionObstacles

• optional perception.PerceptionObstacle perception_obstacle = 1

• optional double timestamp = 2

  GPS time in seconds

• optional double predicted_period = 3

  the length of the time for this prediction (e.g. 10s)

• repeated Trajectory trajectory = 4

  can have multiple trajectories per obstacle

• optional ObstacleIntent intent = 5

  estimated obstacle intent

• optional ObstaclePriority priority = 6

• optional bool is_static = 7

• repeated Feature feature = 8

  Feature history latest -> earliest sequence

• optional common.Header header = 1

  timestamp is included in header

• repeated PredictionObstacle prediction_obstacle = 2

  make prediction for multiple obstacles

• optional common.ErrorCode perception_error_code = 3

  perception error code

• optional double start_timestamp = 4

  start timestamp

• optional double end_timestamp = 5

  end timestamp

• optional Intent intent = 6

  self driving car intent

• optional Scenario scenario = 7

  Scenario

Used in: PredictionTrajectoryPoint

• required double x = 1

• required double y = 2

• optional double velocity_heading = 3

Used in: ListPredictionResult

• optional int32 id = 1

• optional double timestamp = 2

• repeated Trajectory trajectory = 3

• optional ObstacleConf obstacle_conf = 4

• optional Scenario scenario = 5

Used in: Feature

• required PredictionPathPoint path_point = 1

• required double timestamp = 2

Used in: PredictionObstacles, PredictionResult

• optional Scenario.Type type = 1

• optional string junction_id = 2

Used in: Scenario

• UNKNOWN = 0

• CRUISE = 1000

• CRUISE_URBAN = 1001

• CRUISE_HIGHWAY = 1002

• JUNCTION = 2000

• JUNCTION_TRAFFIC_LIGHT = 2001

• JUNCTION_STOP_SIGN = 2002

Used in: LaneSequence

• optional string stop_sign_id = 1

• optional string lane_id = 2

• optional double lane_s = 3

  the s value within the lane

• optional double lane_sequence_s = 4

  the s value along lane sequence

Used in: BatchNormalizationParameter, Conv1dParameter, DenseParameter, LSTMParameter, VerificationSample

• repeated float data = 1

• repeated int32 shape = 2

Used in: PredictionConf

• optional string adccontainer_topic_name = 1

• optional string container_topic_name = 2

• optional string evaluator_topic_name = 3

• optional string localization_topic = 4

• optional string perception_obstacle_topic = 5

• optional string perception_obstacles_topic_name = 6

• optional string planning_trajectory_topic = 7

• optional string prediction_topic = 8

• optional string storytelling_topic = 9

Used in: Feature, PredictionObstacle, PredictionResult

• optional double probability = 1

  probability of this trajectory

• repeated common.TrajectoryPoint trajectory_point = 2

Used in: FnnVehicleModel, Layer, Matrix

• repeated double columns = 1

Used in: NetParameter

• repeated TensorParameter features = 1

  data used to vertify the network model

  input to the network

• optional float probability = 2

  output probability of the network

• optional float distance = 3

  output acceleration of the network