Merge pull request autowarefoundation#412 from tier4/sync-awf-upstream

chore: sync tier4/autoware_launch:awf-latest

.github/labeler.yaml

@@ -0,0 +1,33 @@
+"type:ci":
+  - .github/**/*
+  - "*.json"
+  - "*.yaml"
+  - "*.cfg"
+  - .clang-format
+  - .gitignore
+  - .prettierignore
+"type:documentation":
+  - "**/*.md"
+  - "**/*.rst"
+  - "**/*.jpg"
+  - "**/*.png"
+  - "**/*.svg"
+"component:control":
+  - "**/*control*"
+"component:localization":
+  - "**/*localization*"
+"component:map":
+  - "**/*map*"
+"component:perception":
+  - "**/*perception*"
+"component:planning":
+  - "**/*planning*"
+"component:sensing":
+  - "**/*sensing*"
+"component:simulation":
+  - "**/*simulator*"
+"component:system":
+  - "**/*system*"
+"component:ui":
+  - "**/*.rviz"
+  - "**/rviz"

.github/workflows/pr-labeler.yaml

@@ -0,0 +1,16 @@
+name: pr-labeler
+on:
+  pull_request_target:
+    types:
+      - opened
+      - edited
+      - synchronize
+
+jobs:
+  label:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/labeler@v4
+        with:
+          repo-token: ${{ secrets.GITHUB_TOKEN }}
+          configuration-path: .github/labeler.yaml

.pre-commit-config.yaml

@@ -34,7 +34,7 @@ repos:
       - id: yamllint
 
   - repo: https://github.com/tier4/pre-commit-hooks-ros
-    rev: v0.8.0
+    rev: v0.9.0
     hooks:
       - id: flake8-ros
       - id: prettier-xacro

autoware_launch/config/control/autonomous_emergency_braking/autonomous_emergency_braking.param.yaml

@@ -1,22 +1,38 @@
 /**:
   ros__parameters:
-    publish_debug_pointcloud: false
+    # Ego path calculation
     use_predicted_trajectory: true
-    use_imu_path: true
+    use_imu_path: false
+    min_generated_path_length: 0.5
+    imu_prediction_time_horizon: 1.5
+    imu_prediction_time_interval: 0.1
+    mpc_prediction_time_horizon: 1.5
+    mpc_prediction_time_interval: 0.1
+
+    # Debug
+    publish_debug_pointcloud: false
+
+    # Point cloud partitioning
     detection_range_min_height: 0.0
     detection_range_max_height_margin: 0.0
     voxel_grid_x: 0.05
     voxel_grid_y: 0.05
     voxel_grid_z: 100000.0
-    min_generated_path_length: 0.5
+
+    # Point cloud cropping
     expand_width: 0.1
+    path_footprint_extra_margin: 4.0
+
+    # Point cloud clustering
+    cluster_tolerance: 0.1 #[m]
+    minimum_cluster_size: 10
+    maximum_cluster_size: 10000
+
+    # RSS distance collision check
     longitudinal_offset: 2.0
     t_response: 1.0
     a_ego_min: -3.0
     a_obj_min: -1.0
-    imu_prediction_time_horizon: 1.5
-    imu_prediction_time_interval: 0.1
-    mpc_prediction_time_horizon: 1.5
-    mpc_prediction_time_interval: 0.1
-    collision_keeping_sec: 0.0
+    collision_keeping_sec: 2.0
+    previous_obstacle_keep_time: 1.0
     aeb_hz: 10.0

autoware_launch/config/localization/ekf_localizer.param.yaml

@@ -13,13 +13,13 @@
       pose_additional_delay: 0.0
       pose_measure_uncertainty_time: 0.01
       pose_smoothing_steps: 5
-      pose_gate_dist: 10000.0
+      pose_gate_dist: 49.5 # corresponds to significance level = 10^-10
 
     twist_measurement:
       # for twist measurement
       twist_additional_delay: 0.0
       twist_smoothing_steps: 2
-      twist_gate_dist: 10000.0
+      twist_gate_dist: 46.1 # corresponds to significance level = 10^-10
 
     process_noise:
       # for process model

autoware_launch/config/localization/ndt_scan_matcher/ndt_scan_matcher.param.yaml

@@ -48,7 +48,7 @@
       # The number of initial random trials in the TPE (Tree-Structured Parzen Estimator).
       # This value should be equal to or less than 'initial_estimate_particles_num' and more than 0.
       # If it is equal to 'initial_estimate_particles_num', the search will be the same as a full random search.
-      n_startup_trials: 20
+      n_startup_trials: 100
 
 
     validation:

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

@@ -2,4 +2,5 @@
   ros__parameters:
     fuse_unknown_only: true
     min_cluster_size: 2
+    max_cluster_size: 20
     cluster_2d_tolerance: 0.5

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

@@ -1,27 +1,20 @@
 /**:
   ros__parameters:
-    class_names: ["CAR", "TRUCK", "BUS", "BICYCLE", "PEDESTRIAN"]
-    point_feature_size: 4
-    max_voxel_size: 40000
-    point_cloud_range: [-76.8, -76.8, -4.0, 76.8, 76.8, 6.0]
-    voxel_size: [0.32, 0.32, 10.0]
-    downsample_factor: 1
-    encoder_in_feature_size: 9
-    # post-process params
-    circle_nms_dist_threshold: 0.5
-    iou_nms_target_class_names: ["CAR"]
-    iou_nms_search_distance_2d: 10.0
-    iou_nms_threshold: 0.1
-    yaw_norm_thresholds: [0.3, 0.3, 0.3, 0.3, 0.0]
-    score_threshold: 0.35
-    has_variance: false
-    has_twist: false
-    trt_precision: fp16
-    densification_num_past_frames: 1
-    densification_world_frame_id: map
 
     # weight files
     encoder_onnx_path: "$(var model_path)/pts_voxel_encoder_$(var model_name).onnx"
     encoder_engine_path: "$(var model_path)/pts_voxel_encoder_$(var model_name).engine"
     head_onnx_path: "$(var model_path)/pts_backbone_neck_head_$(var model_name).onnx"
     head_engine_path: "$(var model_path)/pts_backbone_neck_head_$(var model_name).engine"
+    trt_precision: fp16
+    post_process_params:
+      # post-process params
+      circle_nms_dist_threshold: 0.5
+      iou_nms_target_class_names: ["CAR"]
+      iou_nms_search_distance_2d: 10.0
+      iou_nms_threshold: 0.1
+      yaw_norm_thresholds: [0.3, 0.3, 0.3, 0.3, 0.0]
+      score_threshold: 0.35
+    densification_params:
+      world_frame_id: map
+      num_past_frames: 1

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

@@ -1,27 +1,20 @@
 /**:
   ros__parameters:
-    class_names: ["CAR", "TRUCK", "BUS", "BICYCLE", "PEDESTRIAN"]
-    point_feature_size: 4
-    max_voxel_size: 40000
-    point_cloud_range: [-76.8, -76.8, -4.0, 76.8, 76.8, 6.0]
-    voxel_size: [0.32, 0.32, 10.0]
-    downsample_factor: 2
-    encoder_in_feature_size: 9
-    # post-process params
-    circle_nms_dist_threshold: 0.5
-    iou_nms_target_class_names: ["CAR"]
-    iou_nms_search_distance_2d: 10.0
-    iou_nms_threshold: 0.1
-    yaw_norm_thresholds: [0.3, 0.3, 0.3, 0.3, 0.0]
-    score_threshold: 0.35
-    has_variance: false
-    has_twist: false
-    trt_precision: fp16
-    densification_num_past_frames: 1
-    densification_world_frame_id: map
 
     # weight files
     encoder_onnx_path: "$(var model_path)/pts_voxel_encoder_$(var model_name).onnx"
     encoder_engine_path: "$(var model_path)/pts_voxel_encoder_$(var model_name).engine"
     head_onnx_path: "$(var model_path)/pts_backbone_neck_head_$(var model_name).onnx"
     head_engine_path: "$(var model_path)/pts_backbone_neck_head_$(var model_name).engine"
+    trt_precision: fp16
+    post_process_params:
+      # post-process params
+      circle_nms_dist_threshold: 0.5
+      iou_nms_target_class_names: ["CAR"]
+      iou_nms_search_distance_2d: 10.0
+      iou_nms_threshold: 0.1
+      yaw_norm_thresholds: [0.3, 0.3, 0.3, 0.3, 0.0]
+      score_threshold: 0.35
+    densification_params:
+      world_frame_id: map
+      num_past_frames: 1

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

@@ -1,33 +1,21 @@
 /**:
   ros__parameters:
-    trt_precision: fp16
     encoder_onnx_path: "$(var model_path)/pts_voxel_encoder_$(var model_name).onnx"
     encoder_engine_path: "$(var model_path)/pts_voxel_encoder_$(var model_name).engine"
     head_onnx_path: "$(var model_path)/pts_backbone_neck_head_$(var model_name).onnx"
     head_engine_path: "$(var model_path)/pts_backbone_neck_head_$(var model_name).engine"
-
-    model_params:
-      class_names: ["CAR", "TRUCK", "BUS", "BICYCLE", "PEDESTRIAN"]
-      paint_class_names: ["CAR", "BICYCLE", "PEDESTRIAN"]
-      point_feature_size: 7 # x, y, z, time-lag and car, pedestrian, bicycle
-      max_voxel_size: 40000
-      point_cloud_range: [-121.6, -76.8, -3.0, 121.6, 76.8, 5.0]
-      voxel_size: [0.32, 0.32, 8.0]
-      downsample_factor: 1
-      encoder_in_feature_size: 12
-      yaw_norm_thresholds: [0.3, 0.3, 0.3, 0.3, 0.0]
-      has_variance: false
-      has_twist: false
-    densification_params:
-      world_frame_id: "map"
-      num_past_frames: 0
+    trt_precision: fp16
     post_process_params:
       # post-process params
       circle_nms_dist_threshold: 0.3
       iou_nms_target_class_names: ["CAR"]
       iou_nms_search_distance_2d: 10.0
       iou_nms_threshold: 0.1
-      score_threshold: 0.35
+      score_threshold: 0.45
+      yaw_norm_thresholds: [0.3, 0.3, 0.3, 0.3, 0.0]
+    densification_params:
+      world_frame_id: "map"
+      num_past_frames: 1
     omp_params:
       # omp params
       num_threads: 1

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

@@ -27,6 +27,9 @@
       timeout_set_for_no_intention_to_walk: [1.0, 0.0] # [s] values of map
     # parameter for shoulder lane prediction
     prediction_time_horizon_rate_for_validate_shoulder_lane_length: 0.8
+    use_crosswalk_user_history:
+       match_lost_and_appeared_users: false
+       remember_lost_users: false
 
     # parameters for lc prediction
     lane_change_detection:

autoware_launch/config/perception/occupancy_grid_map/multi_lidar_pointcloud_based_occupancy_grid_map.param.yaml

@@ -12,6 +12,10 @@
         map_length_x: 150.0 # [m]
         map_length_y: 150.0 # [m]
 
+      # downsample input pointcloud
+      downsample_input_pointcloud: true
+      downsample_voxel_size: 0.25 # [m]
+
       # each grid map parameters
       ogm_creation_config:
         height_filter:
@@ -20,7 +24,7 @@
           max_height: 2.0
         enable_single_frame_mode: true
         # use sensor pointcloud to filter obstacle pointcloud
-        filter_obstacle_pointcloud_by_raw_pointcloud: true
+        filter_obstacle_pointcloud_by_raw_pointcloud: false
 
         grid_map_type: "OccupancyGridMapFixedBlindSpot"
         OccupancyGridMapFixedBlindSpot:

autoware_launch/config/perception/occupancy_grid_map/pointcloud_based_occupancy_grid_map.param.yaml

@@ -8,6 +8,10 @@
       min_height: -1.0
       max_height: 2.0
 
+    # downsample input pointcloud
+    downsample_input_pointcloud: true
+    downsample_voxel_size: 0.125 # [m]
+
     enable_single_frame_mode: false
     # use sensor pointcloud to filter obstacle pointcloud
     filter_obstacle_pointcloud_by_raw_pointcloud: false

autoware_launch/config/planning/scenario_planning/lane_driving/behavior_planning/behavior_path_planner/avoidance/avoidance.param.yaml

@@ -132,6 +132,10 @@
           th_shiftable_ratio: 0.8                       # [-]
           min_road_shoulder_width: 0.5                  # [m] FOR DEVELOPER
 
+        # for merging/deviating vehicle
+        merging_vehicle:
+          th_overhang_distance: 0.0                     # [m]
+
         # params for avoidance of vehicle type objects that are ambiguous as to whether they are parked.
         avoidance_for_ambiguous_vehicle:
           enable: true                                  # [-]
@@ -290,4 +294,3 @@
       # for debug
       debug:
         marker: false
-        console: false

autoware_launch/config/planning/scenario_planning/lane_driving/behavior_planning/behavior_path_planner/behavior_path_planner.param.yaml

@@ -1,6 +1,5 @@
 /**:
   ros__parameters:
-    verbose: false
     max_iteration_num: 100
 
     traffic_light_signal_timeout: 1.0

autoware_launch/config/planning/scenario_planning/lane_driving/behavior_planning/behavior_path_planner/dynamic_avoidance/dynamic_avoidance.param.yaml

@@ -12,9 +12,9 @@
         bus: true
         trailer: true
         unknown: false
-        bicycle: false
+        bicycle: true
         motorcycle: true
-        pedestrian: false
+        pedestrian: true
 
         max_obstacle_vel: 100.0 # [m/s]
         min_obstacle_vel: 0.0 # [m/s]
@@ -40,6 +40,7 @@
           max_overtaking_object_angle: 1.05
           min_oncoming_object_vel: 1.0
           max_oncoming_object_angle: 0.523
+          max_pedestrian_crossing_vel: 0.8
 
         front_object:
           max_object_angle: 0.785
@@ -55,6 +56,10 @@
           min_longitudinal_polygon_margin: 3.0 # [m]
 
         lat_offset_from_obstacle: 1.0 # [m]
+        margin_distance_around_pedestrian: 2.0 # [m]
+        predicted_path:
+          end_time_to_consider: 3.0 # [s]
+          threshold_confidence: 0.0 # [] not probability
         max_lat_offset_to_avoid: 0.5 # [m]
         max_time_for_object_lat_shift: 0.0 # [s]
         lpf_gain_for_lat_avoid_to_offset: 0.9 # [-]

autoware_launch/config/planning/scenario_planning/lane_driving/behavior_planning/behavior_velocity_planner/crosswalk.param.yaml

@@ -43,6 +43,7 @@
         ego_pass_later_margin_x: [0.0] # [[s]] time to vehicle margin vector for object pass first situation (the module judges that ego don't have to stop at TTV + MARGIN < TTC condition)
         ego_pass_later_margin_y: [13.0] # [[s]] time to collision margin vector for object pass first situation (the module judges that ego don't have to stop at TTV + MARGIN < TTC condition)
         ego_pass_later_additional_margin: 0.5 # [s] additional time margin for object pass first situation to suppress chattering
+        ego_min_assumed_speed: 2.0 # [m/s] assumed speed to calculate the time to collision point
 
         no_stop_decision: # parameters to determine if it is safe to attempt stopping before the crosswalk
           max_offset_to_crosswalk_for_yield: 0.0 # [m] maximum offset from ego's front to crosswalk for yield. Positive value means in front of the crosswalk.