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Merge branch 'main' into autoware_msg
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‎autoware_launch/config/control/trajectory_follower/lateral/mpc.param.yaml

+2
Original file line numberDiff line numberDiff line change
@@ -74,3 +74,5 @@
7474
update_steer_threshold: 0.035
7575
average_num: 1000
7676
steering_offset_limit: 0.02
77+
78+
debug_publish_predicted_trajectory: false # publish debug predicted trajectory in Frenet coordinate

‎autoware_launch/config/localization/ekf_localizer.param.yaml

+5
Original file line numberDiff line numberDiff line change
@@ -4,6 +4,7 @@
44
enable_yaw_bias_estimation: true
55
predict_frequency: 50.0
66
tf_rate: 50.0
7+
publish_tf: true
78
extend_state_step: 50
89

910
# for Pose measurement
@@ -22,6 +23,10 @@
2223
proc_stddev_vx_c: 10.0
2324
proc_stddev_wz_c: 5.0
2425

26+
#Simple1DFilter parameters
27+
z_filter_proc_dev: 1.0
28+
roll_filter_proc_dev: 0.01
29+
pitch_filter_proc_dev: 0.01
2530
# for diagnostics
2631
pose_no_update_count_threshold_warn: 50
2732
pose_no_update_count_threshold_error: 100

‎autoware_launch/config/localization/ndt_scan_matcher.param.yaml

+84-76
Original file line numberDiff line numberDiff line change
@@ -1,106 +1,114 @@
11
/**:
22
ros__parameters:
3-
# Use dynamic map loading
4-
use_dynamic_map_loading: true
3+
frame:
4+
# Vehicle reference frame
5+
base_frame: "base_link"
56

6-
# Vehicle reference frame
7-
base_frame: "base_link"
7+
# NDT reference frame
8+
ndt_base_frame: "ndt_base_link"
89

9-
# NDT reference frame
10-
ndt_base_frame: "ndt_base_link"
10+
# Map frame
11+
map_frame: "map"
1112

12-
# map frame
13-
map_frame: "map"
1413

15-
# Subscriber queue size
16-
input_sensor_points_queue_size: 1
14+
ndt:
15+
# The maximum difference between two consecutive
16+
# transformations in order to consider convergence
17+
trans_epsilon: 0.01
1718

18-
# The maximum difference between two consecutive
19-
# transformations in order to consider convergence
20-
trans_epsilon: 0.01
19+
# The newton line search maximum step length
20+
step_size: 0.1
2121

22-
# The newton line search maximum step length
23-
step_size: 0.1
22+
# The ND voxel grid resolution
23+
resolution: 2.0
2424

25-
# The ND voxel grid resolution
26-
resolution: 2.0
25+
# The number of iterations required to calculate alignment
26+
max_iterations: 30
2727

28-
# The number of iterations required to calculate alignment
29-
max_iterations: 30
28+
# Number of threads used for parallel computing
29+
num_threads: 4
3030

31-
# Converged param type
32-
# 0=TRANSFORM_PROBABILITY, 1=NEAREST_VOXEL_TRANSFORMATION_LIKELIHOOD
33-
converged_param_type: 1
31+
regularization:
32+
enable: false
3433

35-
# If converged_param_type is 0
36-
# Threshold for deciding whether to trust the estimation result
37-
converged_param_transform_probability: 3.0
34+
# Regularization scale factor
35+
scale_factor: 0.01
3836

39-
# If converged_param_type is 1
40-
# Threshold for deciding whether to trust the estimation result
41-
converged_param_nearest_voxel_transformation_likelihood: 2.3
4237

43-
# The number of particles to estimate initial pose
44-
initial_estimate_particles_num: 200
38+
initial_pose_estimation:
39+
# The number of particles to estimate initial pose
40+
particles_num: 200
4541

46-
# The number of initial random trials in the TPE (Tree-Structured Parzen Estimator).
47-
# This value should be equal to or less than 'initial_estimate_particles_num' and more than 0.
48-
# If it is equal to 'initial_estimate_particles_num', the search will be the same as a full random search.
49-
n_startup_trials: 20
42+
# The number of initial random trials in the TPE (Tree-Structured Parzen Estimator).
43+
# This value should be equal to or less than 'initial_estimate_particles_num' and more than 0.
44+
# If it is equal to 'initial_estimate_particles_num', the search will be the same as a full random search.
45+
n_startup_trials: 20
5046

51-
# Tolerance of timestamp difference between current time and sensor pointcloud. [sec]
52-
lidar_topic_timeout_sec: 1.0
5347

54-
# Tolerance of timestamp difference between initial_pose and sensor pointcloud. [sec]
55-
initial_pose_timeout_sec: 1.0
48+
validation:
49+
# Tolerance of timestamp difference between current time and sensor pointcloud. [sec]
50+
lidar_topic_timeout_sec: 1.0
5651

57-
# Tolerance of distance difference between two initial poses used for linear interpolation. [m]
58-
initial_pose_distance_tolerance_m: 10.0
52+
# Tolerance of timestamp difference between initial_pose and sensor pointcloud. [sec]
53+
initial_pose_timeout_sec: 1.0
5954

60-
# Number of threads used for parallel computing
61-
num_threads: 4
55+
# Tolerance of distance difference between two initial poses used for linear interpolation. [m]
56+
initial_pose_distance_tolerance_m: 10.0
6257

63-
# The covariance of output pose
64-
# Note that this covariance matrix is empirically derived
65-
output_pose_covariance:
66-
[
67-
0.0225, 0.0, 0.0, 0.0, 0.0, 0.0,
68-
0.0, 0.0225, 0.0, 0.0, 0.0, 0.0,
69-
0.0, 0.0, 0.0225, 0.0, 0.0, 0.0,
70-
0.0, 0.0, 0.0, 0.000625, 0.0, 0.0,
71-
0.0, 0.0, 0.0, 0.0, 0.000625, 0.0,
72-
0.0, 0.0, 0.0, 0.0, 0.0, 0.000625,
73-
]
58+
# The execution time which means probably NDT cannot matches scans properly. [ms]
59+
critical_upper_bound_exe_time_ms: 100.0
7460

75-
# 2D Real-time covariance estimation with multiple searches (output_pose_covariance is the minimum value)
76-
use_covariance_estimation: false
7761

78-
# Offset arrangement in covariance estimation [m]
79-
# initial_pose_offset_model_x & initial_pose_offset_model_y must have the same number of elements.
80-
initial_pose_offset_model_x: [0.0, 0.0, 0.5, -0.5, 1.0, -1.0]
81-
initial_pose_offset_model_y: [0.5, -0.5, 0.0, 0.0, 0.0, 0.0]
62+
score_estimation:
63+
# Converged param type
64+
# 0=TRANSFORM_PROBABILITY, 1=NEAREST_VOXEL_TRANSFORMATION_LIKELIHOOD
65+
converged_param_type: 1
8266

83-
# Regularization switch
84-
regularization_enabled: false
67+
# If converged_param_type is 0
68+
# Threshold for deciding whether to trust the estimation result
69+
converged_param_transform_probability: 3.0
8570

86-
# Regularization scale factor
87-
regularization_scale_factor: 0.01
71+
# If converged_param_type is 1
72+
# Threshold for deciding whether to trust the estimation result
73+
converged_param_nearest_voxel_transformation_likelihood: 2.3
8874

89-
# Dynamic map loading distance
90-
dynamic_map_loading_update_distance: 20.0
75+
# Scan matching score based on no ground LiDAR scan
76+
no_ground_points:
77+
enable: false
9178

92-
# Dynamic map loading loading radius
93-
dynamic_map_loading_map_radius: 150.0
79+
# If lidar_point.z - base_link.z <= this threshold , the point will be removed
80+
z_margin_for_ground_removal: 0.8
9481

95-
# Radius of input LiDAR range (used for diagnostics of dynamic map loading)
96-
lidar_radius: 100.0
9782

98-
# cspell: ignore degrounded
99-
# A flag for using scan matching score based on de-grounded LiDAR scan
100-
estimate_scores_for_degrounded_scan: false
83+
covariance:
84+
# The covariance of output pose
85+
# Note that this covariance matrix is empirically derived
86+
output_pose_covariance:
87+
[
88+
0.0225, 0.0, 0.0, 0.0, 0.0, 0.0,
89+
0.0, 0.0225, 0.0, 0.0, 0.0, 0.0,
90+
0.0, 0.0, 0.0225, 0.0, 0.0, 0.0,
91+
0.0, 0.0, 0.0, 0.000625, 0.0, 0.0,
92+
0.0, 0.0, 0.0, 0.0, 0.000625, 0.0,
93+
0.0, 0.0, 0.0, 0.0, 0.0, 0.000625,
94+
]
10195

102-
# If lidar_point.z - base_link.z <= this threshold , the point will be removed
103-
z_margin_for_ground_removal: 0.8
96+
# 2D Real-time covariance estimation with multiple searches (output_pose_covariance is the minimum value)
97+
covariance_estimation:
98+
enable: false
10499

105-
# The execution time which means probably NDT cannot matches scans properly. [ms]
106-
critical_upper_bound_exe_time_ms: 100
100+
# Offset arrangement in covariance estimation [m]
101+
# initial_pose_offset_model_x & initial_pose_offset_model_y must have the same number of elements.
102+
initial_pose_offset_model_x: [0.0, 0.0, 0.5, -0.5, 1.0, -1.0]
103+
initial_pose_offset_model_y: [0.5, -0.5, 0.0, 0.0, 0.0, 0.0]
104+
105+
106+
dynamic_map_loading:
107+
# Dynamic map loading distance
108+
update_distance: 20.0
109+
110+
# Dynamic map loading loading radius
111+
map_radius: 150.0
112+
113+
# Radius of input LiDAR range (used for diagnostics of dynamic map loading)
114+
lidar_radius: 100.0

‎autoware_launch/config/localization/twist2accel.param.yaml

+4
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@@ -0,0 +1,4 @@
1+
/**:
2+
ros__parameters:
3+
use_odom: true
4+
accel_lowpass_gain: 0.9

‎autoware_launch/config/map/map_projection_loader.param.yaml

+4
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@@ -0,0 +1,4 @@
1+
/**:
2+
ros__parameters:
3+
map_projector_info_path: $(var map_projector_info_path)
4+
lanelet2_map_path: $(var lanelet2_map_path)

‎autoware_launch/config/map/pointcloud_map_loader.param.yaml

-1
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@@ -3,7 +3,6 @@
33
enable_whole_load: true
44
enable_downsampled_whole_load: false
55
enable_partial_load: true
6-
enable_differential_load: true
76
enable_selected_load: false
87

98
# only used when downsample_whole_load enabled

‎autoware_launch/config/perception/object_recognition/detection/detection_by_tracker/detection_by_tracker.param.yaml

+8-9
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@@ -1,11 +1,10 @@
11
/**:
22
ros__parameters:
3-
tracker_ignore_label:
4-
UNKNOWN : true
5-
CAR : false
6-
TRUCK : false
7-
BUS : false
8-
TRAILER : false
9-
MOTORCYCLE : false
10-
BICYCLE : false
11-
PEDESTRIAN : false
3+
tracker_ignore_label.UNKNOWN : true
4+
tracker_ignore_label.CAR : false
5+
tracker_ignore_label.TRUCK : false
6+
tracker_ignore_label.BUS : false
7+
tracker_ignore_label.TRAILER : false
8+
tracker_ignore_label.MOTORCYCLE : false
9+
tracker_ignore_label.BICYCLE : false
10+
tracker_ignore_label.PEDESTRIAN : false

‎autoware_launch/config/perception/object_recognition/detection/image_projection_based_fusion/roi_cluster_fusion.param.yaml

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1+
/**:
2+
ros__parameters:
3+
fusion_distance: 100.0
4+
trust_object_distance: 100.0
5+
trust_object_iou_mode: "iou"
6+
non_trust_object_iou_mode: "iou_x"
7+
use_cluster_semantic_type: false
8+
only_allow_inside_cluster: true
9+
roi_scale_factor: 1.1
10+
iou_threshold: 0.65
11+
unknown_iou_threshold: 0.1
12+
remove_unknown: false

‎autoware_launch/config/perception/object_recognition/detection/image_projection_based_fusion/roi_detected_object_fusion.param.yaml

+19
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@@ -0,0 +1,19 @@
1+
/**:
2+
ros__parameters:
3+
# UNKNOWN, CAR, TRUCK, BUS, TRAILER, MOTORBIKE, BICYCLE, PEDESTRIAN
4+
passthrough_lower_bound_probability_thresholds: [0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.35, 0.50]
5+
trust_distances: [50.0, 100.0, 100.0, 100.0, 100.0, 50.0, 50.0, 50.0]
6+
min_iou_threshold: 0.5
7+
use_roi_probability: false
8+
roi_probability_threshold: 0.5
9+
10+
can_assign_matrix:
11+
#UNKNOWN, CAR, TRUCK, BUS, TRAILER, MOTORBIKE, BICYCLE, PEDESTRIAN <-roi_msg
12+
[1, 0, 0, 0, 0, 0, 0, 0, # UNKNOWN <-detected_objects
13+
0, 1, 1, 1, 1, 0, 0, 0, # CAR
14+
0, 1, 1, 1, 1, 0, 0, 0, # TRUCK
15+
0, 1, 1, 1, 1, 0, 0, 0, # BUS
16+
0, 1, 1, 1, 1, 0, 0, 0, # TRAILER
17+
0, 0, 0, 0, 0, 1, 1, 1, # MOTORBIKE
18+
0, 0, 0, 0, 0, 1, 1, 1, # BICYCLE
19+
0, 0, 0, 0, 0, 1, 1, 1] # PEDESTRIAN

‎autoware_launch/config/perception/object_recognition/detection/image_projection_based_fusion/roi_pointcloud_fusion.param.yaml

+5
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@@ -0,0 +1,5 @@
1+
/**:
2+
ros__parameters:
3+
fuse_unknown_only: true
4+
min_cluster_size: 2
5+
cluster_2d_tolerance: 0.5

‎autoware_launch/config/perception/object_recognition/detection/image_projection_based_fusion/roi_sync.param.yaml

+2
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@@ -3,6 +3,8 @@
33
input_offset_ms: [61.67, 111.67, 45.0, 28.33, 78.33, 95.0]
44
timeout_ms: 70.0
55
match_threshold_ms: 50.0
6+
image_buffer_size: 15
7+
debug_mode: false
68
filter_scope_min_x: -100.0
79
filter_scope_min_y: -100.0
810
filter_scope_min_z: -100.0

‎autoware_launch/config/perception/object_recognition/detection/lidar_model/detection_class_remapper.param.yaml

+3-3
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@@ -29,9 +29,9 @@
2929
max_area_matrix:
3030
#UNKNOWN, CAR, TRUCK, BUS, TRAILER, MOTORBIKE, BICYCLE, PEDESTRIAN
3131
[ 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, #UNKNOWN
32-
0.000, 0.000, 36.000, 0.000, inf, 0.000, 0.000, 0.000, #CAR
33-
0.000, 0.000, 0.000, 0.000, inf, 0.000, 0.000, 0.000, #TRUCK
34-
0.000, 0.000, 0.000, 0.000, inf, 0.000, 0.000, 0.000, #BUS
32+
0.000, 0.000, 36.000, 0.000, 999.999, 0.000, 0.000, 0.000, #CAR
33+
0.000, 0.000, 0.000, 0.000, 999.999, 0.000, 0.000, 0.000, #TRUCK
34+
0.000, 0.000, 0.000, 0.000, 999.999, 0.000, 0.000, 0.000, #BUS
3535
0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, #TRAILER
3636
0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, #MOTORBIKE
3737
0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, #BICYCLE

‎autoware_launch/config/perception/object_recognition/detection/lidar_model/pointpainting.param.yaml

+31-16
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@@ -1,18 +1,33 @@
11
/**:
22
ros__parameters:
3-
class_names: ["CAR", "TRUCK", "BUS", "BICYCLE", "PEDESTRIAN"]
4-
paint_class_names: ["CAR", "BICYCLE", "PEDESTRIAN"]
5-
point_feature_size: 7 # x, y, z, time-lag and car, pedestrian, bicycle
6-
max_voxel_size: 40000
7-
point_cloud_range: [-121.6, -76.8, -3.0, 121.6, 76.8, 5.0]
8-
voxel_size: [0.32, 0.32, 8.0]
9-
downsample_factor: 1
10-
encoder_in_feature_size: 12
11-
yaw_norm_thresholds: [0.3, 0.3, 0.3, 0.3, 0.0]
12-
# post-process params
13-
circle_nms_dist_threshold: 0.3
14-
iou_nms_target_class_names: ["CAR"]
15-
iou_nms_search_distance_2d: 10.0
16-
iou_nms_threshold: 0.1
17-
# omp params
18-
omp_num_threads: 1
3+
trt_precision: fp16
4+
build_only: false
5+
encoder_onnx_path: "$(var model_path)/pts_voxel_encoder_$(var model_name).onnx"
6+
encoder_engine_path: "$(var model_path)/pts_voxel_encoder_$(var model_name).engine"
7+
head_onnx_path: "$(var model_path)/pts_backbone_neck_head_$(var model_name).onnx"
8+
head_engine_path: "$(var model_path)/pts_backbone_neck_head_$(var model_name).engine"
9+
10+
model_params:
11+
class_names: ["CAR", "TRUCK", "BUS", "BICYCLE", "PEDESTRIAN"]
12+
paint_class_names: ["CAR", "BICYCLE", "PEDESTRIAN"]
13+
point_feature_size: 7 # x, y, z, time-lag and car, pedestrian, bicycle
14+
max_voxel_size: 40000
15+
point_cloud_range: [-121.6, -76.8, -3.0, 121.6, 76.8, 5.0]
16+
voxel_size: [0.32, 0.32, 8.0]
17+
downsample_factor: 1
18+
encoder_in_feature_size: 12
19+
yaw_norm_thresholds: [0.3, 0.3, 0.3, 0.3, 0.0]
20+
has_twist: false
21+
densification_params:
22+
world_frame_id: "map"
23+
num_past_frames: 0
24+
post_process_params:
25+
# post-process params
26+
circle_nms_dist_threshold: 0.3
27+
iou_nms_target_class_names: ["CAR"]
28+
iou_nms_search_distance_2d: 10.0
29+
iou_nms_threshold: 0.1
30+
score_threshold: 0.4
31+
omp_params:
32+
# omp params
33+
num_threads: 1

‎autoware_launch/config/perception/object_recognition/detection/object_range_splitter/object_range_splitter_radar.param.yaml

+3
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@@ -0,0 +1,3 @@
1+
/**:
2+
ros__parameters:
3+
split_range: 80.0

‎autoware_launch/config/perception/object_recognition/detection/object_range_splitter/object_range_splitter_radar_fusion.param.yaml

+3
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1+
/**:
2+
ros__parameters:
3+
split_range: 70.0

‎autoware_launch/config/perception/object_recognition/detection/object_velocity_splitter/object_velocity_splitter_radar.param.yaml

+3
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1+
/**:
2+
ros__parameters:
3+
velocity_threshold: 5.5

‎autoware_launch/config/perception/object_recognition/detection/object_velocity_splitter/object_velocity_splitter_radar_fusion.param.yaml

+3
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1+
/**:
2+
ros__parameters:
3+
velocity_threshold: 4.5

‎autoware_launch/config/perception/object_recognition/detection/radar_crossing_objects_noise_filter/radar_crossing_objects_noise_filter.param.yaml

+4
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1+
/**:
2+
ros__parameters:
3+
angle_threshold: 1.2210
4+
velocity_threshold: 1.5

‎autoware_launch/config/perception/object_recognition/prediction/map_based_prediction.param.yaml

+7
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@@ -13,6 +13,13 @@
1313
sigma_yaw_angle_deg: 5.0 #[angle degree]
1414
object_buffer_time_length: 2.0 #[s]
1515
history_time_length: 1.0 #[s]
16+
check_lateral_acceleration_constraints: false # whether to check if the predicted path complies with lateral acceleration constraints
17+
max_lateral_accel: 2.0 # [m/ss] max acceptable lateral acceleration for predicted vehicle paths
18+
min_acceleration_before_curve: -2.0 # [m/ss] min acceleration a vehicle might take to decelerate before a curve
19+
use_vehicle_acceleration: false # whether to consider current vehicle acceleration when predicting paths or not
20+
speed_limit_multiplier: 1.5 # When using vehicle acceleration. Set vehicle's maximum predicted speed as the legal speed limit in that lanelet times this value
21+
acceleration_exponential_half_life: 2.5 # [s] When using vehicle acceleration. The decaying acceleration model considers that the current vehicle acceleration will be halved after this many seconds
22+
use_crosswalk_signal: true
1623
# parameter for shoulder lane prediction
1724
prediction_time_horizon_rate_for_validate_shoulder_lane_length: 0.8
1825

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