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tier4-autoware-public-bot[bot]tier4-autoware-public-bot[bot]
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Merge pull request #784 from tier4/sync-awf-latest
chore: sync awf-latest
2 parents b5f6373 + 9c4cbd9 commit db2f6a4

17 files changed

+189
-20
lines changed

‎autoware_launch/config/control/control_validator/control_validator.param.yaml

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rolling_back_velocity: 0.5
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over_velocity_offset: 2.0
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over_velocity_ratio: 0.2
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overrun_stop_point_dist: 0.8
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nominal_latency: 0.01
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vel_lpf_gain: 0.9 # Time constant 0.33

‎autoware_launch/config/perception/traffic_light_recognition/crosswalk_traffic_light_estimator/crosswalk_traffic_light_estimator.param.yaml

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# Copyright 2023 UCI SORA Lab
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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/**:
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ros__parameters:
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use_last_detect_color: true
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last_detect_color_hold_time: 2.0
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last_colors_hold_time: 1.0

‎autoware_launch/config/perception/traffic_light_recognition/tensorrt_yolox/yolox_traffic_light_detector.param.yaml

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# cspell: ignore semseg
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/**:
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ros__parameters:
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score_threshold: 0.35
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nms_threshold: 0.7
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precision: "fp16" # Operation precision to be used on inference. Valid value is one of: [fp32, fp16, int8].
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calibration_algorithm: "Entropy" # Calibration algorithm to be used for quantization when precision==int8. Valid value is one of: [Entropy, (Legacy | Percentile), MinMax].
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dla_core_id: -1 # If positive ID value is specified, the node assign inference task to the DLA core.
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quantize_first_layer: false # If true, set the operating precision for the first (input) layer to be fp16. This option is valid only when precision==int8.
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quantize_last_layer: false # If true, set the operating precision for the last (output) layer to be fp16. This option is valid only when precision==int8.
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profile_per_layer: false # If true, profiler function will be enabled. Since the profile function may affect execution speed, it is recommended to set this flag true only for development purpose.
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clip_value: 6.0 # If positive value is specified, the value of each layer output will be clipped between [0.0, clip_value]. This option is valid only when precision==int8 and used to manually specify the dynamic range instead of using any calibration.
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preprocess_on_gpu: true # If true, pre-processing is performed on GPU.
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gpu_id: 0 # GPU ID to select CUDA Device
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calibration_image_list_path: "" # Path to a file which contains path to images. Those images will be used for int8 quantization.
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# default params of tensorrt_yolox package, only effective for semseg model
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is_roi_overlap_segment: false
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overlap_roi_score_threshold: 0.3
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is_publish_color_mask: false
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roi_overlay_segment_label:
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UNKNOWN : false
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CAR : false
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TRUCK : false
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BUS : false
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MOTORCYCLE : false
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BICYCLE : false
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PEDESTRIAN : false
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ANIMAL: false

‎autoware_launch/config/perception/traffic_light_arbiter/traffic_light_arbiter.param.yaml ‎autoware_launch/config/perception/traffic_light_recognition/traffic_light_arbiter/traffic_light_arbiter.param.yaml

File renamed without changes.

‎autoware_launch/config/perception/traffic_light_recognition/traffic_light_classifier/car_traffic_light_classifier.param.yaml

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/**:
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ros__parameters:
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approximate_sync: false
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precision: fp16
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mean: [123.675, 116.28, 103.53] # It depends on the data on which ML model is trained
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std: [58.395, 57.12, 57.375] # It depends on the data on which ML model is trained
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backlight_threshold: 0.85
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classifier_type: 1
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traffic_light_type: 0

‎autoware_launch/config/perception/traffic_light_recognition/traffic_light_classifier/pedestrian_traffic_light_classifier.param.yaml

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/**:
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ros__parameters:
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approximate_sync: false
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precision: fp16
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mean: [123.675, 116.28, 103.53] # It depends on the data on which ML model is trained
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std: [58.395, 57.12, 57.375] # It depends on the data on which ML model is trained
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backlight_threshold: 0.85
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classifier_type: 1
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traffic_light_type: 1

‎autoware_launch/config/perception/traffic_light_recognition/traffic_light_fine_detector/traffic_light_fine_detector.param.yaml

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/**:
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ros__parameters:
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precision: fp16
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score_thresh: 0.3
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nms_thresh: 0.65
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approximate_sync: false
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gpu_id: 0

‎autoware_launch/config/perception/traffic_light_recognition/traffic_light_map_based_detector/camera6_traffic_light_map_based_detector.param.yaml

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/**:
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ros__parameters:
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max_vibration_pitch: 0.01745329251 # -0.5 ~ 0.5 deg
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max_vibration_yaw: 0.01745329251 # -0.5 ~ 0.5 deg
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max_vibration_height: 0.5 # -0.25 ~ 0.25 m
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max_vibration_width: 0.5 # -0.25 ~ 0.25 m
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max_vibration_depth: 0.5 # -0.25 ~ 0.25 m
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max_detection_range: 200.0
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min_timestamp_offset: -0.3
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max_timestamp_offset: 0.0
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timestamp_sample_len: 0.02
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car_traffic_light_max_angle_range: 40.0
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pedestrian_traffic_light_max_angle_range: 80.0

‎autoware_launch/config/perception/traffic_light_recognition/traffic_light_map_based_detector/camera7_traffic_light_map_based_detector.param.yaml

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/**:
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ros__parameters:
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max_vibration_pitch: 0.01745329251 # -0.5 ~ 0.5 deg
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max_vibration_yaw: 0.01745329251 # -0.5 ~ 0.5 deg
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max_vibration_height: 0.5 # -0.25 ~ 0.25 m
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max_vibration_width: 0.5 # -0.25 ~ 0.25 m
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max_vibration_depth: 0.5 # -0.25 ~ 0.25 m
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max_detection_range: 200.0
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min_timestamp_offset: -0.04
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max_timestamp_offset: 0.0
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timestamp_sample_len: 0.02
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car_traffic_light_max_angle_range: 40.0
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pedestrian_traffic_light_max_angle_range: 80.0

‎autoware_launch/config/perception/traffic_light_recognition/traffic_light_map_based_detector/traffic_light_traffic_light_map_based_detector.param.yaml

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/**:
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ros__parameters:
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max_vibration_pitch: 0.01745329251 # -0.5 ~ 0.5 deg
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max_vibration_yaw: 0.01745329251 # -0.5 ~ 0.5 deg
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max_vibration_height: 0.5 # -0.25 ~ 0.25 m
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max_vibration_width: 0.5 # -0.25 ~ 0.25 m
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max_vibration_depth: 0.5 # -0.25 ~ 0.25 m
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max_detection_range: 200.0
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min_timestamp_offset: -0.3
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max_timestamp_offset: 0.0
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timestamp_sample_len: 0.02
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car_traffic_light_max_angle_range: 40.0
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pedestrian_traffic_light_max_angle_range: 80.0

‎autoware_launch/config/perception/traffic_light_multi_camera_fusion/traffic_light_multi_camera_fusion.param.yaml ‎autoware_launch/config/perception/traffic_light_recognition/traffic_light_multi_camera_fusion/traffic_light_multi_camera_fusion.param.yaml

File renamed without changes.

‎autoware_launch/config/perception/traffic_light_recognition/traffic_light_occlusion_predictor/traffic_light_occlusion_predictor.param.yaml

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/**:
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ros__parameters:
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azimuth_occlusion_resolution_deg: 0.15 # degree
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elevation_occlusion_resolution_deg: 0.08 # degree
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max_valid_pt_dist: 50.0
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# the node would wait at most max_wait_t seconds for cloud whose timestamp different with the image is less than max_image_cloud_delay.
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# If no cloud with timestamp difference smaller than max_image_cloud_delay is found, the node would publish occlusion ratio of all
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# traffic lights as 0
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max_image_cloud_delay: 0.5
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max_wait_t: 0.05
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max_occlusion_ratio: 50

‎autoware_launch/config/perception/traffic_light_recognition/traffic_light_selector/traffic_light_selector.param.yaml

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/**:
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ros__parameters:
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max_iou_threshold: -0.5

‎autoware_launch/config/perception/traffic_light_recognition/traffic_light_visualization/traffic_light_roi_visualizer.param.yaml

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/**:
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ros__parameters:
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use_image_transport: true

‎autoware_launch/launch/autoware.launch.xml

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<arg name="rviz_respawn" default="true"/>
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<!-- Perception -->
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<arg name="perception_mode" default="lidar" description="select perception mode. camera_lidar_radar_fusion, camera_lidar_fusion, lidar_radar_fusion, lidar, radar"/>
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<arg name="traffic_light_recognition/use_ml_detector" default="true" description="enable traffic light fine detection"/>
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<arg name="traffic_light_recognition/use_high_accuracy_detection" default="true" description="enable to use high accuracy detection for traffic light recognition"/>
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<!-- Auto mode setting-->
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<arg name="enable_all_modules_auto_mode" default="false" description="enable all module's auto mode"/>
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<arg name="is_simulation" default="false" description="Autoware's behavior will change depending on whether this is a simulation or not."/>

‎autoware_launch/launch/components/tier4_perception_component.launch.xml

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<arg name="detected_objects_validation_method" default="obstacle_pointcloud" description="options: obstacle_pointcloud, occupancy_grid (must be used with laserscan_based_occupancy_grid_map)"/>
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<arg name="data_path" default="$(env HOME)/autoware_data" description="packages data and artifacts directory path"/>
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<arg name="lidar_detection_model" default="centerpoint" description="If the model name is not declared, the default model in perception.launch.xml will be used"/>
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<arg name="all_traffic_light_camera" default="[camera6, camera7]" description="choose camera which use for traffic light recognition"/>
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<arg name="traffic_light_recognition/ml_detection_model_type" default="fine_detection_model" description="select ml model for TL detection: fine_detection_model or whole_image_detection_model"/>
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<arg name="traffic_light_recognition/camera_namespaces" default="[camera6, camera7]" description="choose camera which use for traffic light recognition"/>
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<arg name="traffic_light_recognition/use_high_accuracy_detection" default="true" description="select whether to use high accuracy detector for traffic light recognition"/>
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<arg
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name="traffic_light_recognition/high_accuracy_detection_type"
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default="fine_detection"
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description="select high accuracy detector type for traffic light recognition: fine_detection or whole_image_detection"
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/>
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<include file="$(find-pkg-share tier4_perception_launch)/launch/perception.launch.xml">
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<!-- Options to Switch Launch Function/Module -->
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<arg name="data_path" value="$(var data_path)"/>
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<!-- general options to switch launch function/module -->
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<arg name="mode" value="$(var perception_mode)"/>
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<arg name="lidar_detection_model" value="$(var lidar_detection_model)"/>
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<arg name="vehicle_param_file" value="$(find-pkg-share $(var vehicle_model)_description)/config/vehicle_info.param.yaml"/>
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<arg name="use_obstacle_segmentation_single_frame_filter" value="$(var use_obstacle_segmentation_single_frame_filter)"/>
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<arg name="use_obstacle_segmentation_time_series_filter" value="$(var use_obstacle_segmentation_time_series_filter)"/>
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<arg name="data_path" value="$(var data_path)"/>
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<!-- traffic light recognition options to switch launch function/module -->
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<arg name="use_traffic_light_recognition" value="$(var use_traffic_light_recognition)"/>
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<arg name="traffic_light_recognition/use_ml_detector" value="$(var traffic_light_recognition/use_ml_detector)"/>
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<arg name="traffic_light_recognition/ml_detection_model_type" value="$(var traffic_light_recognition/ml_detection_model_type)"/>
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<arg name="traffic_light_recognition/fusion_only" value="false"/>
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<arg name="all_traffic_light_camera" value="$(var all_traffic_light_camera)"/>
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<arg name="traffic_light_recognition/camera_namespaces" value="$(var traffic_light_recognition/camera_namespaces)"/>
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<arg name="traffic_light_recognition/use_high_accuracy_detection" value="$(var traffic_light_recognition/use_high_accuracy_detection)"/>
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<arg name="traffic_light_recognition/high_accuracy_detection_type" value="$(var traffic_light_recognition/high_accuracy_detection_type)"/>
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<!-- object recognition parameters -->
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<arg
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<arg name="occupancy_grid_map_updater_param_path" value="$(find-pkg-share autoware_launch)/config/perception/occupancy_grid_map/$(var occupancy_grid_map_updater)_updater.param.yaml"/>
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<!-- traffic light recognition parameters -->
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<arg name="traffic_light_arbiter_param_path" value="$(find-pkg-share autoware_launch)/config/perception/traffic_light_arbiter/traffic_light_arbiter.param.yaml"/>
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<arg name="traffic_light_recognition/whole_image_detector_model_path" value="$(var data_path)/tensorrt_yolox"/>
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<arg name="traffic_light_recognition/whole_image_detector_model_name" value="yolox_s_car_ped_tl_detector_960_960_batch_1.onnx"/>
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<arg
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name="each_traffic_light_map_based_detector_param_path"
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value="$(find-pkg-share autoware_launch)/config/perception/traffic_light_recognition/traffic_light_map_based_detector/TRAFFIC_LIGHT_RECOGNITION_CAMERA_NAMESPACE_traffic_light_map_based_detector.param.yaml"
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/>
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<arg
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name="traffic_light_fine_detector_param_path"
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value="$(find-pkg-share autoware_launch)/config/perception/traffic_light_recognition/traffic_light_fine_detector/traffic_light_fine_detector.param.yaml"
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/>
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<arg name="yolox_traffic_light_detector_param_path" value="$(find-pkg-share autoware_launch)/config/perception/traffic_light_recognition/tensorrt_yolox/yolox_traffic_light_detector.param.yaml"/>
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<arg
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name="car_traffic_light_classifier_param_path"
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value="$(find-pkg-share autoware_launch)/config/perception/traffic_light_recognition/traffic_light_classifier/car_traffic_light_classifier.param.yaml"
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/>
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<arg
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name="pedestrian_traffic_light_classifier_param_path"
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value="$(find-pkg-share autoware_launch)/config/perception/traffic_light_recognition/traffic_light_classifier/pedestrian_traffic_light_classifier.param.yaml"
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/>
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<arg
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name="traffic_light_roi_visualizer_param_path"
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value="$(find-pkg-share autoware_launch)/config/perception/traffic_light_recognition/traffic_light_visualization/traffic_light_roi_visualizer.param.yaml"
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/>
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<arg name="traffic_light_selector_param_path" value="$(find-pkg-share autoware_launch)/config/perception/traffic_light_recognition/traffic_light_selector/traffic_light_selector.param.yaml"/>
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<arg
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name="traffic_light_occlusion_predictor_param_path"
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value="$(find-pkg-share autoware_launch)/config/perception/traffic_light_recognition/traffic_light_occlusion_predictor/traffic_light_occlusion_predictor.param.yaml"
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/>
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<arg
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name="traffic_light_multi_camera_fusion_param_path"
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value="$(find-pkg-share autoware_launch)/config/perception/traffic_light_multi_camera_fusion/traffic_light_multi_camera_fusion.param.yaml"
231+
value="$(find-pkg-share autoware_launch)/config/perception/traffic_light_recognition/traffic_light_multi_camera_fusion/traffic_light_multi_camera_fusion.param.yaml"
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/>
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<arg name="traffic_light_arbiter_param_path" value="$(find-pkg-share autoware_launch)/config/perception/traffic_light_recognition/traffic_light_arbiter/traffic_light_arbiter.param.yaml"/>
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<arg
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name="crosswalk_traffic_light_estimator_param_path"
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value="$(find-pkg-share autoware_launch)/config/perception/traffic_light_recognition/crosswalk_traffic_light_estimator/crosswalk_traffic_light_estimator.param.yaml"
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/>
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<arg name="traffic_light_fine_detector_model_path" value="$(var data_path)/traffic_light_fine_detector"/>
205-
<arg name="traffic_light_fine_detector_model_name" value="tlr_car_ped_yolox_s_batch_6"/>
206-
<arg name="traffic_light_classifier_model_path" value="$(var data_path)/traffic_light_classifier"/>
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<arg name="car_traffic_light_classifier_model_name" value="traffic_light_classifier_mobilenetv2_batch_6"/>
208-
<arg name="pedestrian_traffic_light_classifier_model_name" value="ped_traffic_light_classifier_mobilenetv2_batch_6"/>
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<arg name="traffic_light_recognition/whole_image_detection/model_path" value="$(var data_path)/tensorrt_yolox/yolox_s_car_ped_tl_detector_960_960_batch_1.onnx"/>
239+
<arg name="traffic_light_recognition/whole_image_detection/label_path" value="$(var data_path)/tensorrt_yolox/car_ped_tl_detector_labels.txt"/>
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<arg name="traffic_light_recognition/fine_detection/model_path" value="$(var data_path)/traffic_light_fine_detector/tlr_car_ped_yolox_s_batch_6.onnx"/>
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<arg name="traffic_light_recognition/fine_detection/label_path" value="$(var data_path)/traffic_light_fine_detector/tlr_labels.txt"/>
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<arg name="traffic_light_recognition/classification/car/model_path" value="$(var data_path)/traffic_light_classifier/traffic_light_classifier_mobilenetv2_batch_6.onnx"/>
243+
<arg name="traffic_light_recognition/classification/car/label_path" value="$(var data_path)/traffic_light_classifier/lamp_labels.txt"/>
244+
<arg name="traffic_light_recognition/classification/pedestrian/model_path" value="$(var data_path)/traffic_light_classifier/ped_traffic_light_classifier_mobilenetv2_batch_6.onnx"/>
245+
<arg name="traffic_light_recognition/classification/pedestrian/label_path" value="$(var data_path)/traffic_light_classifier/lamp_labels_ped.txt"/>
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</include>
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</launch>

‎autoware_launch/launch/e2e_simulator.launch.xml

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<!-- Sensing -->
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<arg name="launch_sensing_driver" value="$(var launch_sensing_driver)"/>
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<!-- Perception-->
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<arg name="traffic_light_recognition/use_ml_detector" value="false"/>
79-
<arg name="all_traffic_light_camera" value="$(var traffic_light_namespace)"/>
78+
<arg name="traffic_light_recognition/use_high_accuracy_detection" value="false"/>
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<arg name="traffic_light_recognition/camera_namespaces" value="$(var traffic_light_namespace)"/>
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<!-- Tools -->
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<arg name="rviz" value="$(var rviz)"/>
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<arg name="rviz_config" value="$(var rviz_config)"/>

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