From 584be310ea2b195e2d0fb015a0d1a2c74eea58be Mon Sep 17 00:00:00 2001 From: Satoshi OTA <44889564+satoshi-ota@users.noreply.github.com> Date: Tue, 3 Sep 2024 17:29:54 +0900 Subject: [PATCH] feat(planning_data_analyzer): add new package (#103) * feat(planning_data_analyzer): add new package Signed-off-by: satoshi-ota * style(pre-commit): autofix * refactor: use map Signed-off-by: satoshi-ota * style(pre-commit): autofix * fix: print Signed-off-by: satoshi-ota * style(pre-commit): autofix * refactor: small change Signed-off-by: satoshi-ota * feat: weight grid search Signed-off-by: satoshi-ota * feat: add time delta Signed-off-by: satoshi-ota * style(pre-commit): autofix * fix: typo Signed-off-by: satoshi-ota * fix: ignore spell check for foxglove json Signed-off-by: satoshi-ota --------- Signed-off-by: satoshi-ota Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> --- .cspell-partial.json | 3 +- .../CMakeLists.txt | 21 + .../autoware_planning_data_analyzer/README.md | 20 + .../config/behavior_analyzer.json | 294 +++++++++++ .../config/behavior_analyzer.param.yaml | 22 + .../docs/planning_data_analyzer.png | Bin 0 -> 738776 bytes .../launch/behavior_analyzer.launch.xml | 28 ++ .../package.xml | 44 ++ .../src/data_structs.cpp | 428 ++++++++++++++++ .../src/data_structs.hpp | 340 +++++++++++++ .../src/node.cpp | 461 ++++++++++++++++++ .../src/node.hpp | 92 ++++ .../src/type_alias.hpp | 68 +++ .../src/utils.hpp | 272 +++++++++++ 14 files changed, 2092 insertions(+), 1 deletion(-) create mode 100644 planning/autoware_planning_data_analyzer/CMakeLists.txt create mode 100644 planning/autoware_planning_data_analyzer/README.md create mode 100644 planning/autoware_planning_data_analyzer/config/behavior_analyzer.json create mode 100644 planning/autoware_planning_data_analyzer/config/behavior_analyzer.param.yaml create mode 100644 planning/autoware_planning_data_analyzer/docs/planning_data_analyzer.png create mode 100644 planning/autoware_planning_data_analyzer/launch/behavior_analyzer.launch.xml create mode 100644 planning/autoware_planning_data_analyzer/package.xml create mode 100644 planning/autoware_planning_data_analyzer/src/data_structs.cpp create mode 100644 planning/autoware_planning_data_analyzer/src/data_structs.hpp create mode 100644 planning/autoware_planning_data_analyzer/src/node.cpp create mode 100644 planning/autoware_planning_data_analyzer/src/node.hpp create mode 100644 planning/autoware_planning_data_analyzer/src/type_alias.hpp create mode 100644 planning/autoware_planning_data_analyzer/src/utils.hpp diff --git a/.cspell-partial.json b/.cspell-partial.json index 5a1aa2b9..2c86df58 100644 --- a/.cspell-partial.json +++ b/.cspell-partial.json @@ -3,7 +3,8 @@ "**/perception/**", "sensing/tier4_pcl_extensions/include/**", "perception/bytetrack/lib/**", - "common/autoware_debug_tools/**" + "common/autoware_debug_tools/**", + "planning/autoware_planning_data_analyzer/**" ], "ignoreRegExpList": [], "words": ["dltype", "tvmgen", "quantizer", "imageio", "mimsave"] diff --git a/planning/autoware_planning_data_analyzer/CMakeLists.txt b/planning/autoware_planning_data_analyzer/CMakeLists.txt new file mode 100644 index 00000000..52f158fa --- /dev/null +++ b/planning/autoware_planning_data_analyzer/CMakeLists.txt @@ -0,0 +1,21 @@ +cmake_minimum_required(VERSION 3.14) +project(autoware_planning_data_analyzer) + +find_package(autoware_cmake REQUIRED) +autoware_package() + +ament_auto_add_library(${PROJECT_NAME} SHARED + src/node.cpp + src/data_structs.cpp +) + +rclcpp_components_register_node(${PROJECT_NAME} + PLUGIN "autoware::behavior_analyzer::BehaviorAnalyzerNode" + EXECUTABLE ${PROJECT_NAME}_node +) + +ament_auto_package( + INSTALL_TO_SHARE + config + launch +) diff --git a/planning/autoware_planning_data_analyzer/README.md b/planning/autoware_planning_data_analyzer/README.md new file mode 100644 index 00000000..a4f2cafd --- /dev/null +++ b/planning/autoware_planning_data_analyzer/README.md @@ -0,0 +1,20 @@ +# Planning Data Analyzer + +
+ +
+ +## Usage + +```sh +ros2 launch autoware_planning_data_analyzer behavior_analyzer.launch.xml bag_path:= +``` + +## Output + +| Name | Type | Description | +| ------------------------- | ------------------------------------------------- | --------------------------------------------------------------- | +| `~/output/manual_metrics` | `tier4_debug_msgs::msg::Float32MultiArrayStamped` | Metrics calculated from the driver's driving trajectory. | +| `~/output/system_metrics` | `tier4_debug_msgs::msg::Float32MultiArrayStamped` | Metrics calculated from the autoware output. | +| `~/output/manual_score` | `tier4_debug_msgs::msg::Float32MultiArrayStamped` | Driving scores calculated from the driver's driving trajectory. | +| `~/output/system_score` | `tier4_debug_msgs::msg::Float32MultiArrayStamped` | Driving scores calculated from the autoware output. | diff --git a/planning/autoware_planning_data_analyzer/config/behavior_analyzer.json b/planning/autoware_planning_data_analyzer/config/behavior_analyzer.json new file mode 100644 index 00000000..eb621dc2 --- /dev/null +++ b/planning/autoware_planning_data_analyzer/config/behavior_analyzer.json @@ -0,0 +1,294 @@ +{ + "configById": { + "Plot!2xxq5nc": { + "paths": [ + { + "timestampMethod": "receiveTime", + "value": "/behavior_analyzer/manual_metrics.data[40:59]", + "enabled": true, + "color": "#4e98e2", + "label": "longitudinal jerk (manual)", + "lineSize": 4 + }, + { + "timestampMethod": "receiveTime", + "value": "/behavior_analyzer/system_metrics.data[40:59]", + "enabled": true, + "color": "#f5774d", + "lineSize": 4, + "label": "longitudinal jerk (system)" + } + ], + "minYValue": -2, + "maxYValue": 2, + "showXAxisLabels": true, + "showYAxisLabels": true, + "showLegend": true, + "legendDisplay": "floating", + "showPlotValuesInLegend": false, + "isSynced": true, + "xAxisVal": "index", + "sidebarDimension": 240, + "minXValue": 0, + "maxXValue": 20 + }, + "Plot!2s3vi1i": { + "paths": [ + { + "timestampMethod": "receiveTime", + "value": "/behavior_analyzer/manual_score.data[1]", + "enabled": true, + "color": "#4e98e2", + "label": "longitudinal comfortabiilty (manual)", + "lineSize": 1.5, + "showLine": true + }, + { + "timestampMethod": "receiveTime", + "value": "/behavior_analyzer/system_score.data[1]", + "enabled": true, + "color": "#f5774d", + "lineSize": 1.5, + "label": "longitudinal comfortabiilty (system)" + } + ], + "minYValue": 0, + "maxYValue": 1, + "showXAxisLabels": true, + "showYAxisLabels": true, + "showLegend": true, + "legendDisplay": "floating", + "showPlotValuesInLegend": false, + "isSynced": true, + "xAxisVal": "timestamp", + "sidebarDimension": 240, + "followingViewWidth": 500 + }, + "Plot!na5bbq": { + "paths": [ + { + "timestampMethod": "receiveTime", + "value": "/behavior_analyzer/manual_score.data[0]", + "enabled": true, + "color": "#4e98e2", + "label": "lateral comfortabiilty (manual)", + "lineSize": 1.5, + "showLine": true + }, + { + "timestampMethod": "receiveTime", + "value": "/behavior_analyzer/system_score.data[0]", + "enabled": true, + "color": "#f5774d", + "lineSize": 1.5, + "label": "lateral comfortabiilty (system)" + } + ], + "minYValue": 0, + "maxYValue": 1, + "showXAxisLabels": true, + "showYAxisLabels": true, + "showLegend": true, + "legendDisplay": "floating", + "showPlotValuesInLegend": false, + "isSynced": true, + "xAxisVal": "timestamp", + "sidebarDimension": 240, + "followingViewWidth": 500 + }, + "Plot!1oow4ne": { + "paths": [ + { + "timestampMethod": "receiveTime", + "value": "/behavior_analyzer/manual_metrics.data[60:79]", + "enabled": true, + "color": "#4e98e2", + "label": "travel_distance (manual)", + "lineSize": 4 + }, + { + "timestampMethod": "receiveTime", + "value": "/behavior_analyzer/system_metrics.data[60:79]", + "enabled": true, + "color": "#f5774d", + "lineSize": 4, + "label": "travel_distance (system)" + } + ], + "minYValue": 0, + "maxYValue": 150, + "showXAxisLabels": true, + "showYAxisLabels": true, + "showLegend": true, + "legendDisplay": "floating", + "showPlotValuesInLegend": false, + "isSynced": true, + "xAxisVal": "index", + "sidebarDimension": 240, + "minXValue": 0, + "maxXValue": 20 + }, + "Plot!3wovlah": { + "paths": [ + { + "timestampMethod": "receiveTime", + "value": "/behavior_analyzer/manual_score.data[2]", + "enabled": true, + "color": "#4e98e2", + "label": "efficiency (manual)", + "lineSize": 1.5, + "showLine": true + }, + { + "timestampMethod": "receiveTime", + "value": "/behavior_analyzer/system_score.data[2]", + "enabled": true, + "color": "#f5774d", + "lineSize": 1.5, + "label": "efficiency (system)" + } + ], + "minYValue": 0, + "maxYValue": 1, + "showXAxisLabels": true, + "showYAxisLabels": true, + "showLegend": true, + "legendDisplay": "floating", + "showPlotValuesInLegend": false, + "isSynced": true, + "xAxisVal": "timestamp", + "sidebarDimension": 240, + "followingViewWidth": 500 + }, + "Plot!4jh9z4a": { + "paths": [ + { + "timestampMethod": "receiveTime", + "value": "/behavior_analyzer/manual_metrics.data[80:99]", + "enabled": true, + "color": "#4e98e2", + "label": "minimum ttc (manual)", + "lineSize": 4 + }, + { + "timestampMethod": "receiveTime", + "value": "/behavior_analyzer/system_metrics.data[80:99]", + "enabled": true, + "color": "#f5774d", + "label": "minimum ttc (system)", + "lineSize": 4 + } + ], + "minYValue": 0, + "maxYValue": 20, + "showXAxisLabels": true, + "showYAxisLabels": true, + "showLegend": true, + "legendDisplay": "floating", + "showPlotValuesInLegend": false, + "isSynced": true, + "xAxisVal": "index", + "sidebarDimension": 240, + "minXValue": 0, + "maxXValue": 20 + }, + "Plot!37j6i8x": { + "paths": [ + { + "timestampMethod": "receiveTime", + "value": "/behavior_analyzer/manual_score.data[3]", + "enabled": true, + "color": "#4e98e2", + "label": "safety (manual)", + "lineSize": 1.5, + "showLine": true + }, + { + "timestampMethod": "receiveTime", + "value": "/behavior_analyzer/system_score.data[3]", + "enabled": true, + "color": "#f5774d", + "label": "safety (system)" + } + ], + "minYValue": 0, + "maxYValue": 1, + "showXAxisLabels": true, + "showYAxisLabels": true, + "showLegend": true, + "legendDisplay": "floating", + "showPlotValuesInLegend": false, + "isSynced": true, + "xAxisVal": "timestamp", + "sidebarDimension": 240, + "followingViewWidth": 500 + }, + "Plot!3qlhcp2": { + "paths": [ + { + "timestampMethod": "receiveTime", + "value": "/behavior_analyzer/manual_metrics.data[0:19]", + "enabled": true, + "color": "#4e98e2", + "label": "lateral acceleration (manual)", + "lineSize": 4 + }, + { + "timestampMethod": "receiveTime", + "value": "/behavior_analyzer/system_metrics.data[0:19]", + "enabled": true, + "color": "#f5774d", + "lineSize": 4, + "label": "lateral acceleration (system)" + } + ], + "minYValue": -2, + "maxYValue": 2, + "showXAxisLabels": true, + "showYAxisLabels": true, + "showLegend": true, + "legendDisplay": "floating", + "showPlotValuesInLegend": false, + "isSynced": true, + "xAxisVal": "index", + "sidebarDimension": 240, + "minXValue": 0, + "maxXValue": 20 + } + }, + "globalVariables": {}, + "userNodes": {}, + "playbackConfig": { + "speed": 1 + }, + "layout": { + "first": { + "first": { + "first": "Plot!2xxq5nc", + "second": "Plot!3qlhcp2", + "direction": "column" + }, + "second": { + "first": "Plot!2s3vi1i", + "second": "Plot!na5bbq", + "direction": "column" + }, + "direction": "row" + }, + "second": { + "first": { + "first": "Plot!1oow4ne", + "second": "Plot!3wovlah", + "direction": "row" + }, + "second": { + "first": "Plot!4jh9z4a", + "second": "Plot!37j6i8x", + "direction": "row" + }, + "direction": "column" + }, + "direction": "column", + "splitPercentage": 35.595105672969964 + } +} diff --git a/planning/autoware_planning_data_analyzer/config/behavior_analyzer.param.yaml b/planning/autoware_planning_data_analyzer/config/behavior_analyzer.param.yaml new file mode 100644 index 00000000..c57036ec 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rclcpp_components + rosbag2_cpp + std_srvs + tf2_msgs + tf2_ros + tier4_planning_msgs + visualization_msgs + + ament_lint_auto + autoware_lint_common + + + ament_cmake + + diff --git a/planning/autoware_planning_data_analyzer/src/data_structs.cpp b/planning/autoware_planning_data_analyzer/src/data_structs.cpp new file mode 100644 index 00000000..0bef5575 --- /dev/null +++ b/planning/autoware_planning_data_analyzer/src/data_structs.cpp @@ -0,0 +1,428 @@ +// Copyright 2024 TIER IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "data_structs.hpp" + +#include "utils.hpp" + +#include +#include +#include +#include +#include +#include +#include + +namespace autoware::behavior_analyzer +{ + +std::string TOPIC::TF = "/tf"; // NOLINT +std::string TOPIC::ODOMETRY = "/localization/kinematic_state"; // NOLINT +std::string TOPIC::ACCELERATION = "/localization/acceleration"; // NOLINT +std::string TOPIC::OBJECTS = "/perception/object_recognition/objects"; // NOLINT +std::string TOPIC::TRAJECTORY = "/planning/scenario_planning/trajectory"; // NOLINT +std::string TOPIC::STEERING = "/vehicle/status/steering_status"; // NOLINT + +template <> +bool Buffer::ready() const +{ + if (msgs.empty()) { + return false; + } + + return rclcpp::Time(msgs.back().stamp).nanoseconds() - + rclcpp::Time(msgs.front().stamp).nanoseconds() > + BUFFER_TIME; +} + +template <> +bool Buffer::ready() const +{ + if (msgs.empty()) { + return false; + } + + if (msgs.front().transforms.empty()) { + return false; + } + + if (msgs.back().transforms.empty()) { + return false; + } + + return rclcpp::Time(msgs.back().transforms.front().header.stamp).nanoseconds() - + rclcpp::Time(msgs.front().transforms.front().header.stamp).nanoseconds() > + BUFFER_TIME; +} + +template <> +void Buffer::remove_old_data(const rcutils_time_point_value_t now) +{ + if (msgs.empty()) { + return; + } + + const auto itr = std::remove_if(msgs.begin(), msgs.end(), [&now, this](const auto & msg) { + return rclcpp::Time(msg.stamp).nanoseconds() < now; + }); + msgs.erase(itr, msgs.end()); +} + +template <> +void Buffer::remove_old_data(const rcutils_time_point_value_t now) +{ + if (msgs.empty()) { + return; + } + + const auto itr = std::remove_if(msgs.begin(), msgs.end(), [&now, this](const auto & msg) { + return rclcpp::Time(msg.transforms.front().header.stamp).nanoseconds() < now; + }); + msgs.erase(itr, msgs.end()); +} + +template <> +auto Buffer::get(const rcutils_time_point_value_t now) const + -> std::optional +{ + const auto itr = std::find_if(msgs.begin(), msgs.end(), [&now, this](const auto & msg) { + return rclcpp::Time(msg.stamp).nanoseconds() > now; + }); + + if (itr == msgs.end()) { + return std::nullopt; + } + + return *itr; +} + +template <> +auto Buffer::get(const rcutils_time_point_value_t now) const -> std::optional +{ + const auto itr = std::find_if(msgs.begin(), msgs.end(), [&now, this](const auto & msg) { + return rclcpp::Time(msg.transforms.front().header.stamp).nanoseconds() > now; + }); + + if (itr == msgs.end()) { + return std::nullopt; + } + + return *itr; +} + +CommonData::CommonData( + const std::shared_ptr & bag_data, const vehicle_info_utils::VehicleInfo & vehicle_info, + const std::shared_ptr & parameters, const std::string & tag) +: vehicle_info{vehicle_info}, parameters{parameters}, tag{tag} +{ + objects_history.reserve(parameters->resample_num); + + const auto objects_buffer_ptr = std::dynamic_pointer_cast>( + bag_data->buffers.at("/perception/object_recognition/objects")); + for (size_t i = 0; i < parameters->resample_num; i++) { + const auto opt_objects = + objects_buffer_ptr->get(bag_data->timestamp + 1e9 * parameters->time_resolution * i); + if (!opt_objects.has_value()) { + break; + } + objects_history.push_back(opt_objects.value()); + } +} + +void CommonData::calculate() +{ + std::vector lateral_accel_values; + std::vector minimum_ttc_values; + std::vector longitudinal_jerk_values; + std::vector travel_distance_values; + + for (size_t i = 0; i < parameters->resample_num - 1; i++) { + lateral_accel_values.push_back(lateral_accel(i)); + longitudinal_jerk_values.push_back(longitudinal_jerk(i)); + minimum_ttc_values.push_back(minimum_ttc(i)); + travel_distance_values.push_back(travel_distance(i)); + } + + { + lateral_accel_values.push_back(lateral_accel(parameters->resample_num - 1)); + longitudinal_jerk_values.push_back(0.0); + minimum_ttc_values.push_back(minimum_ttc(parameters->resample_num - 1)); + travel_distance_values.push_back(travel_distance(parameters->resample_num - 1)); + } + + values.emplace(METRIC::LATERAL_ACCEL, lateral_accel_values); + values.emplace(METRIC::LONGITUDINAL_JERK, longitudinal_jerk_values); + values.emplace(METRIC::MINIMUM_TTC, minimum_ttc_values); + values.emplace(METRIC::TRAVEL_DISTANCE, travel_distance_values); + + scores.emplace(SCORE::LONGITUDINAL_COMFORTABILITY, longitudinal_comfortability()); + scores.emplace(SCORE::LATERAL_COMFORTABILITY, lateral_comfortability()); + scores.emplace(SCORE::EFFICIENCY, efficiency()); + scores.emplace(SCORE::SAFETY, safety()); +} + +double CommonData::longitudinal_comfortability() const +{ + constexpr double TIME_FACTOR = 0.8; + + double score = 0.0; + + const auto min = 0.0; + const auto max = 0.5; + const auto normalize = [&min, &max](const double value) { + return (max - std::clamp(value, min, max)) / (max - min); + }; + + for (size_t i = 0; i < parameters->resample_num; i++) { + score += + normalize(std::pow(TIME_FACTOR, i) * std::abs(values.at(METRIC::LONGITUDINAL_JERK).at(i))); + } + + return score / parameters->resample_num; +} + +double CommonData::lateral_comfortability() const +{ + constexpr double TIME_FACTOR = 0.8; + + double score = 0.0; + + const auto min = 0.0; + const auto max = 0.5; + const auto normalize = [&min, &max](const double value) { + return (max - std::clamp(value, min, max)) / (max - min); + }; + + for (size_t i = 0; i < parameters->resample_num; i++) { + score += normalize(std::pow(TIME_FACTOR, i) * std::abs(values.at(METRIC::LATERAL_ACCEL).at(i))); + } + + return score / parameters->resample_num; +} + +double CommonData::efficiency() const +{ + constexpr double TIME_FACTOR = 0.8; + + double score = 0.0; + + const auto min = 0.0; + const auto max = 20.0; + const auto normalize = [&min, &max](const double value) { + return std::clamp(value, min, max) / (max - min); + }; + + for (size_t i = 0; i < parameters->resample_num; i++) { + score += normalize(std::pow(TIME_FACTOR, i) * values.at(METRIC::TRAVEL_DISTANCE).at(i) / 0.5); + } + + return score / parameters->resample_num; +} + +double CommonData::safety() const +{ + constexpr double TIME_FACTOR = 0.8; + + double score = 0.0; + + const auto min = 0.0; + const auto max = 5.0; + const auto normalize = [&min, &max](const double value) { + return std::clamp(value, min, max) / (max - min); + }; + + for (size_t i = 0; i < parameters->resample_num; i++) { + score += normalize(std::pow(TIME_FACTOR, i) * values.at(METRIC::MINIMUM_TTC).at(i)); + } + + return score / parameters->resample_num; +} + +double CommonData::total() const +{ + return parameters->w_lat_comfortability * scores.at(SCORE::LATERAL_COMFORTABILITY) + + parameters->w_lon_comfortability * scores.at(SCORE::LONGITUDINAL_COMFORTABILITY) + + parameters->w_efficiency * scores.at(SCORE::EFFICIENCY) + + parameters->w_safety * scores.at(SCORE::SAFETY); +} + +ManualDrivingData::ManualDrivingData( + const std::shared_ptr & bag_data, const vehicle_info_utils::VehicleInfo & vehicle_info, + const std::shared_ptr & parameters) +: CommonData(bag_data, vehicle_info, parameters, "manual") +{ + odometry_history.reserve(parameters->resample_num); + accel_history.reserve(parameters->resample_num); + steer_history.reserve(parameters->resample_num); + + const auto odometry_buffer_ptr = std::dynamic_pointer_cast>( + bag_data->buffers.at("/localization/kinematic_state")); + const auto acceleration_buffer_ptr = + std::dynamic_pointer_cast>( + bag_data->buffers.at("/localization/acceleration")); + const auto steering_buffer_ptr = std::dynamic_pointer_cast>( + bag_data->buffers.at("/vehicle/status/steering_status")); + + for (size_t i = 0; i < parameters->resample_num; i++) { + const auto opt_odometry = + odometry_buffer_ptr->get(bag_data->timestamp + 1e9 * parameters->time_resolution * i); + if (!opt_odometry.has_value()) { + break; + } + odometry_history.push_back(opt_odometry.value()); + + const auto opt_accel = + acceleration_buffer_ptr->get(bag_data->timestamp + 1e9 * parameters->time_resolution * i); + if (!opt_accel.has_value()) { + break; + } + accel_history.push_back(opt_accel.value()); + + const auto opt_steer = + steering_buffer_ptr->get(bag_data->timestamp + 1e9 * parameters->time_resolution * i); + if (!opt_steer.has_value()) { + break; + } + steer_history.push_back(opt_steer.value()); + } + + calculate(); +} + +double ManualDrivingData::lateral_accel(const size_t idx) const +{ + const auto radius = + vehicle_info.wheel_base_m / std::tan(steer_history.at(idx).steering_tire_angle); + const auto speed = odometry_history.at(idx).twist.twist.linear.x; + return speed * speed / radius; +} + +double ManualDrivingData::longitudinal_jerk(const size_t idx) const +{ + const double dt = rclcpp::Time(accel_history.at(idx + 1).header.stamp).nanoseconds() - + rclcpp::Time(accel_history.at(idx).header.stamp).nanoseconds(); + + return 1e9 * + (accel_history.at(idx + 1).accel.accel.linear.x - + accel_history.at(idx).accel.accel.linear.x) / + dt; +} + +double ManualDrivingData::minimum_ttc(const size_t idx) const +{ + const auto p_ego = odometry_history.at(idx).pose.pose; + const auto v_ego = utils::get_velocity_in_world_coordinate(odometry_history.at(idx)); + + return utils::time_to_collision(objects_history.at(idx), p_ego, v_ego); +} + +double ManualDrivingData::travel_distance(const size_t idx) const +{ + double distance = 0.0; + for (size_t i = 0L; i < idx; i++) { + distance += autoware::universe_utils::calcDistance3d( + odometry_history.at(i + 1).pose.pose, odometry_history.at(i).pose.pose); + } + return distance; +} + +TrajectoryData::TrajectoryData( + const std::shared_ptr & bag_data, const vehicle_info_utils::VehicleInfo & vehicle_info, + const std::shared_ptr & parameters, const std::string & tag, + const std::vector & points) +: CommonData(bag_data, vehicle_info, parameters, tag), points{points} +{ + calculate(); +} + +double TrajectoryData::lateral_accel(const size_t idx) const +{ + const auto radius = vehicle_info.wheel_base_m / std::tan(points.at(idx).front_wheel_angle_rad); + const auto speed = points.at(idx).longitudinal_velocity_mps; + return speed * speed / radius; +} + +double TrajectoryData::longitudinal_jerk(const size_t idx) const +{ + return (points.at(idx + 1).acceleration_mps2 - points.at(idx).acceleration_mps2) / 0.5; +} + +double TrajectoryData::minimum_ttc(const size_t idx) const +{ + const auto p_ego = points.at(idx).pose; + const auto v_ego = utils::get_velocity_in_world_coordinate(points.at(idx)); + + return utils::time_to_collision(objects_history.at(idx), p_ego, v_ego); +} + +double TrajectoryData::travel_distance(const size_t idx) const +{ + return autoware::motion_utils::calcSignedArcLength(points, 0L, idx); +} + +bool TrajectoryData::feasible() const +{ + const auto condition = [](const auto & p) { return p.longitudinal_velocity_mps > -1e-3; }; + return std::all_of(points.begin(), points.end(), condition); +} + +SamplingTrajectoryData::SamplingTrajectoryData( + const std::shared_ptr & bag_data, const vehicle_info_utils::VehicleInfo & vehicle_info, + const std::shared_ptr & parameters) +{ + const auto opt_odometry = std::dynamic_pointer_cast>( + bag_data->buffers.at("/localization/kinematic_state")) + ->get(bag_data->timestamp); + if (!opt_odometry.has_value()) { + throw std::logic_error("data is not enough."); + } + + const auto opt_accel = std::dynamic_pointer_cast>( + bag_data->buffers.at("/localization/acceleration")) + ->get(bag_data->timestamp); + if (!opt_accel.has_value()) { + throw std::logic_error("data is not enough."); + } + + const auto opt_trajectory = std::dynamic_pointer_cast>( + bag_data->buffers.at("/planning/scenario_planning/trajectory")) + ->get(bag_data->timestamp); + if (!opt_trajectory.has_value()) { + throw std::logic_error("data is not enough."); + } + data.emplace_back( + bag_data, vehicle_info, parameters, "autoware", + utils::resampling( + opt_trajectory.value(), opt_odometry.value().pose.pose, parameters->resample_num, + parameters->time_resolution)); + + for (const auto & sample : utils::sampling( + opt_trajectory.value(), opt_odometry.value().pose.pose, + opt_odometry.value().twist.twist.linear.x, opt_accel.value().accel.accel.linear.x, + vehicle_info, parameters)) { + data.emplace_back(bag_data, vehicle_info, parameters, "frenet", sample); + } + + std::vector stop_points(parameters->resample_num); + for (auto & stop_point : stop_points) { + stop_point.pose = opt_odometry.value().pose.pose; + } + data.emplace_back(bag_data, vehicle_info, parameters, "stop", stop_points); + + std::sort( + data.begin(), data.end(), [](const auto & a, const auto & b) { return a.total() > b.total(); }); + + std::remove_if(data.begin(), data.end(), [](const auto & d) { return !d.feasible(); }); +} +} // namespace autoware::behavior_analyzer diff --git a/planning/autoware_planning_data_analyzer/src/data_structs.hpp b/planning/autoware_planning_data_analyzer/src/data_structs.hpp new file mode 100644 index 00000000..94253796 --- /dev/null +++ b/planning/autoware_planning_data_analyzer/src/data_structs.hpp @@ -0,0 +1,340 @@ +// Copyright 2024 TIER IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef DATA_STRUCTS_HPP_ +#define DATA_STRUCTS_HPP_ + +#include "type_alias.hpp" + +#include + +#include +#include +#include +#include +#include +#include +#include +#include + +namespace autoware::behavior_analyzer +{ + +enum class METRIC { + LATERAL_ACCEL = 0, + LONGITUDINAL_ACCEL = 1, + LONGITUDINAL_JERK = 2, + TRAVEL_DISTANCE = 3, + MINIMUM_TTC = 4, + SIZE +}; + +enum class SCORE { + LATERAL_COMFORTABILITY = 0, + LONGITUDINAL_COMFORTABILITY = 1, + EFFICIENCY = 2, + SAFETY = 3, + SIZE +}; + +struct TOPIC +{ + static std::string TF; + static std::string ODOMETRY; + static std::string ACCELERATION; + static std::string OBJECTS; + static std::string TRAJECTORY; + static std::string STEERING; +}; + +struct FrenetPoint +{ + double length{0.0}; // longitudinal + double distance{0.0}; // lateral +}; + +struct TargetStateParameters +{ + std::vector lat_positions{}; + std::vector lat_velocities{}; + std::vector lat_accelerations{}; + std::vector lon_positions{}; + std::vector lon_velocities{}; + std::vector lon_accelerations{}; +}; + +struct Parameters +{ + size_t resample_num{20}; + double time_resolution{0.5}; + double w_lat_comfortability{1.0}; + double w_lon_comfortability{1.0}; + double w_efficiency{1.0}; + double w_safety{1.0}; + double dt{1.0}; + std::vector grid{}; + TargetStateParameters target_state{}; +}; + +struct BufferBase +{ + virtual bool ready() const = 0; + virtual void remove_old_data(const rcutils_time_point_value_t now) = 0; +}; + +template +struct Buffer : BufferBase +{ + std::vector msgs; + + const double BUFFER_TIME = 20.0 * 1e9; + + bool ready() const override + { + if (msgs.empty()) { + return false; + } + + return rclcpp::Time(msgs.back().header.stamp).nanoseconds() - + rclcpp::Time(msgs.front().header.stamp).nanoseconds() > + BUFFER_TIME; + } + + void remove_old_data(const rcutils_time_point_value_t now) override + { + const auto itr = std::remove_if(msgs.begin(), msgs.end(), [&now, this](const auto & msg) { + return rclcpp::Time(msg.header.stamp).nanoseconds() < now; + }); + msgs.erase(itr, msgs.end()); + } + + void append(const T & msg) { msgs.push_back(msg); } + + auto get(const rcutils_time_point_value_t now) const -> std::optional + { + const auto itr = std::find_if(msgs.begin(), msgs.end(), [&now, this](const auto & msg) { + return rclcpp::Time(msg.header.stamp).nanoseconds() > now; + }); + + if (itr == msgs.end()) { + return std::nullopt; + } + + return *itr; + } +}; + +template <> +bool Buffer::ready() const; + +template <> +bool Buffer::ready() const; + +template <> +void Buffer::remove_old_data(const rcutils_time_point_value_t now); + +template <> +void Buffer::remove_old_data(const rcutils_time_point_value_t now); + +template <> +auto Buffer::get(const rcutils_time_point_value_t now) const + -> std::optional; + +template <> +auto Buffer::get(const rcutils_time_point_value_t now) const -> std::optional; + +struct BagData +{ + explicit BagData(const rcutils_time_point_value_t timestamp) : timestamp{timestamp} + { + buffers.emplace(TOPIC::TF, std::make_shared>()); + buffers.emplace(TOPIC::ODOMETRY, std::make_shared>()); + buffers.emplace(TOPIC::ACCELERATION, std::make_shared>()); + buffers.emplace(TOPIC::TRAJECTORY, std::make_shared>()); + buffers.emplace(TOPIC::OBJECTS, std::make_shared>()); + buffers.emplace(TOPIC::STEERING, std::make_shared>()); + } + + std::map> buffers{}; + + rcutils_time_point_value_t timestamp; + + void update(const rcutils_time_point_value_t dt) + { + timestamp += dt; + remove_old_data(); + } + + void remove_old_data() + { + std::for_each(buffers.begin(), buffers.end(), [this](const auto & buffer) { + buffer.second->remove_old_data(timestamp); + }); + } + + bool ready() const + { + return std::all_of( + buffers.begin(), buffers.end(), [](const auto & buffer) { return buffer.second->ready(); }); + } +}; + +struct CommonData +{ + CommonData( + const std::shared_ptr & trimmed_data, + const vehicle_info_utils::VehicleInfo & vehicle_info, + const std::shared_ptr & parameters, const std::string & tag); + + void calculate(); + + double longitudinal_comfortability() const; + + double lateral_comfortability() const; + + double efficiency() const; + + double safety() const; + + double total() const; + + virtual double lateral_accel(const size_t idx) const = 0; + + virtual double longitudinal_jerk(const size_t idx) const = 0; + + virtual double minimum_ttc(const size_t idx) const = 0; + + virtual double travel_distance(const size_t idx) const = 0; + + virtual bool feasible() const = 0; + + std::vector objects_history; + + std::unordered_map> values; + std::unordered_map scores; + + vehicle_info_utils::VehicleInfo vehicle_info; + + std::shared_ptr parameters; + + std::string tag{""}; +}; + +struct ManualDrivingData : CommonData +{ + ManualDrivingData( + const std::shared_ptr & trimmed_data, + const vehicle_info_utils::VehicleInfo & vehicle_info, + const std::shared_ptr & parameters); + + double lateral_accel(const size_t idx) const override; + + double longitudinal_jerk(const size_t idx) const override; + + double minimum_ttc(const size_t idx) const override; + + double travel_distance(const size_t idx) const override; + + bool feasible() const override { return true; } + + std::vector odometry_history; + std::vector accel_history; + std::vector steer_history; +}; + +struct TrajectoryData : CommonData +{ + TrajectoryData( + const std::shared_ptr & trimmed_data, + const vehicle_info_utils::VehicleInfo & vehicle_info, + const std::shared_ptr & parameters, const std::string & tag, + const std::vector & points); + + double lateral_accel(const size_t idx) const override; + + double longitudinal_jerk(const size_t idx) const override; + + double minimum_ttc(const size_t idx) const override; + + double travel_distance(const size_t idx) const override; + + bool feasible() const override; + + std::vector points; +}; + +struct SamplingTrajectoryData +{ + SamplingTrajectoryData( + const std::shared_ptr & trimmed_data, + const vehicle_info_utils::VehicleInfo & vehicle_info, + const std::shared_ptr & parameters); + + auto best() const -> std::optional + { + if (data.empty()) return std::nullopt; + return data.front(); + } + + auto autoware() const -> std::optional + { + const auto itr = std::find_if(data.begin(), data.end(), [](const auto & trajectory) { + return trajectory.tag == "autoware"; + }); + if (itr == data.end()) return std::nullopt; + return *itr; + } + + std::vector data; +}; + +struct DataSet +{ + DataSet( + const std::shared_ptr & trimmed_data, + const vehicle_info_utils::VehicleInfo & vehicle_info, + const std::shared_ptr & parameters) + : manual{ManualDrivingData(trimmed_data, vehicle_info, parameters)}, + sampling{SamplingTrajectoryData(trimmed_data, vehicle_info, parameters)} + { + } + + auto loss() const -> std::optional + { + const auto best = sampling.best(); + if (!best.has_value()) { + return std::nullopt; + } + + if (manual.odometry_history.size() != best.value().points.size()) { + return std::nullopt; + } + + double mse = 0.0; + for (size_t i = 0; i < manual.odometry_history.size(); i++) { + const auto & p1 = manual.odometry_history.at(i).pose.pose; + const auto & p2 = best.value().points.at(i); + mse = (mse * i + autoware::universe_utils::calcSquaredDistance2d(p1, p2)) / (i + 1); + } + + return mse; + } + + ManualDrivingData manual; + SamplingTrajectoryData sampling; +}; + +} // namespace autoware::behavior_analyzer + +#endif // DATA_STRUCTS_HPP_ diff --git a/planning/autoware_planning_data_analyzer/src/node.cpp b/planning/autoware_planning_data_analyzer/src/node.cpp new file mode 100644 index 00000000..bea157be --- /dev/null +++ b/planning/autoware_planning_data_analyzer/src/node.cpp @@ -0,0 +1,461 @@ +// Copyright 2024 TIER IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "node.hpp" + +#include + +namespace autoware::behavior_analyzer +{ +using autoware::universe_utils::createDefaultMarker; +using autoware::universe_utils::createMarkerColor; +using autoware::universe_utils::createMarkerScale; +using autoware::universe_utils::Point2d; +using autoware::universe_utils::Polygon2d; + +BehaviorAnalyzerNode::BehaviorAnalyzerNode(const rclcpp::NodeOptions & node_options) +: Node("path_selector_node", node_options) +{ + using namespace std::literals::chrono_literals; + timer_ = rclcpp::create_timer( + this, get_clock(), 100ms, std::bind(&BehaviorAnalyzerNode::on_timer, this)); + + timer_->cancel(); + + vehicle_info_ = autoware::vehicle_info_utils::VehicleInfoUtils(*this).getVehicleInfo(); + + pub_marker_ = create_publisher("~/marker", 1); + pub_odometry_ = create_publisher(TOPIC::ODOMETRY, rclcpp::QoS(1)); + pub_objects_ = create_publisher(TOPIC::OBJECTS, rclcpp::QoS(1)); + pub_trajectory_ = create_publisher(TOPIC::TRAJECTORY, rclcpp::QoS(1)); + pub_tf_ = create_publisher(TOPIC::TF, rclcpp::QoS(1)); + + pub_manual_metrics_ = + create_publisher("~/manual_metrics", rclcpp::QoS{1}); + pub_system_metrics_ = + create_publisher("~/system_metrics", rclcpp::QoS{1}); + pub_manual_score_ = create_publisher("~/manual_score", rclcpp::QoS{1}); + pub_system_score_ = create_publisher("~/system_score", rclcpp::QoS{1}); + + srv_play_ = this->create_service( + "play", + std::bind(&BehaviorAnalyzerNode::play, this, std::placeholders::_1, std::placeholders::_2), + rclcpp::ServicesQoS().get_rmw_qos_profile()); + + srv_rewind_ = this->create_service( + "rewind", + std::bind(&BehaviorAnalyzerNode::rewind, this, std::placeholders::_1, std::placeholders::_2), + rclcpp::ServicesQoS().get_rmw_qos_profile()); + + srv_weight_ = this->create_service( + "weight_grid_search", + std::bind(&BehaviorAnalyzerNode::weight, this, std::placeholders::_1, std::placeholders::_2), + rclcpp::ServicesQoS().get_rmw_qos_profile()); + + reader_.open(declare_parameter("bag_path")); + + bag_data_ = std::make_shared( + duration_cast(reader_.get_metadata().starting_time.time_since_epoch()).count()); + + parameters_ = std::make_shared(); + parameters_->resample_num = declare_parameter("resample_num"); + parameters_->time_resolution = declare_parameter("time_resolution"); + parameters_->w_lat_comfortability = declare_parameter("weight.lat_comfortability"); + parameters_->w_lon_comfortability = declare_parameter("weight.lon_comfortability"); + parameters_->w_efficiency = declare_parameter("weight.efficiency"); + parameters_->w_safety = declare_parameter("weight.safety"); + parameters_->dt = declare_parameter("grid_seach.dt"); + parameters_->grid = declare_parameter>("grid_seach.grid"); + parameters_->target_state.lat_positions = + declare_parameter>("target_state.lateral_positions"); + parameters_->target_state.lat_velocities = + declare_parameter>("target_state.lateral_velocities"); + parameters_->target_state.lat_accelerations = + declare_parameter>("target_state.lateral_accelerations"); + parameters_->target_state.lon_positions = + declare_parameter>("target_state.longitudinal_positions"); + parameters_->target_state.lon_velocities = + declare_parameter>("target_state.longitudinal_velocities"); + parameters_->target_state.lon_accelerations = + declare_parameter>("target_state.longitudinal_accelerations"); +} + +void BehaviorAnalyzerNode::update(const std::shared_ptr & bag_data, const double dt) const +{ + rosbag2_storage::StorageFilter filter; + filter.topics.emplace_back(TOPIC::TF); + filter.topics.emplace_back(TOPIC::ODOMETRY); + filter.topics.emplace_back(TOPIC::ACCELERATION); + filter.topics.emplace_back(TOPIC::OBJECTS); + filter.topics.emplace_back(TOPIC::STEERING); + filter.topics.emplace_back(TOPIC::TRAJECTORY); + reader_.set_filter(filter); + + bag_data->update(dt * 1e9); + + while (reader_.has_next()) { + const auto next_data = reader_.read_next(); + rclcpp::SerializedMessage serialized_msg(*next_data->serialized_data); + + if (bag_data->ready()) { + break; + } + + if (next_data->topic_name == TOPIC::TF) { + rclcpp::Serialization serializer; + const auto deserialized_message = std::make_shared(); + serializer.deserialize_message(&serialized_msg, deserialized_message.get()); + std::dynamic_pointer_cast>(bag_data->buffers.at(TOPIC::TF)) + ->append(*deserialized_message); + } + + if (next_data->topic_name == TOPIC::ODOMETRY) { + rclcpp::Serialization serializer; + const auto deserialized_message = std::make_shared(); + serializer.deserialize_message(&serialized_msg, deserialized_message.get()); + std::dynamic_pointer_cast>(bag_data->buffers.at(TOPIC::ODOMETRY)) + ->append(*deserialized_message); + } + + if (next_data->topic_name == TOPIC::ACCELERATION) { + rclcpp::Serialization serializer; + const auto deserialized_message = std::make_shared(); + serializer.deserialize_message(&serialized_msg, deserialized_message.get()); + std::dynamic_pointer_cast>( + bag_data->buffers.at(TOPIC::ACCELERATION)) + ->append(*deserialized_message); + } + + if (next_data->topic_name == TOPIC::OBJECTS) { + rclcpp::Serialization serializer; + const auto deserialized_message = std::make_shared(); + serializer.deserialize_message(&serialized_msg, deserialized_message.get()); + std::dynamic_pointer_cast>(bag_data->buffers.at(TOPIC::OBJECTS)) + ->append(*deserialized_message); + } + + if (next_data->topic_name == TOPIC::STEERING) { + rclcpp::Serialization serializer; + const auto deserialized_message = std::make_shared(); + serializer.deserialize_message(&serialized_msg, deserialized_message.get()); + std::dynamic_pointer_cast>(bag_data->buffers.at(TOPIC::STEERING)) + ->append(*deserialized_message); + } + + if (next_data->topic_name == TOPIC::TRAJECTORY) { + rclcpp::Serialization serializer; + const auto deserialized_message = std::make_shared(); + serializer.deserialize_message(&serialized_msg, deserialized_message.get()); + std::dynamic_pointer_cast>(bag_data->buffers.at(TOPIC::TRAJECTORY)) + ->append(*deserialized_message); + } + } +} + +void BehaviorAnalyzerNode::play( + const SetBool::Request::SharedPtr req, SetBool::Response::SharedPtr res) +{ + std::lock_guard lock(mutex_); + if (req->data) { + timer_->reset(); + RCLCPP_INFO(get_logger(), "start evaluation."); + } else { + timer_->cancel(); + RCLCPP_INFO(get_logger(), "stop evaluation."); + } + res->success = true; +} + +void BehaviorAnalyzerNode::rewind( + [[maybe_unused]] const Trigger::Request::SharedPtr req, Trigger::Response::SharedPtr res) +{ + std::lock_guard lock(mutex_); + reader_.seek(0); + + bag_data_.reset(); + bag_data_ = std::make_shared( + duration_cast(reader_.get_metadata().starting_time.time_since_epoch()).count()); + + res->success = true; +} + +void BehaviorAnalyzerNode::weight( + [[maybe_unused]] const Trigger::Request::SharedPtr req, Trigger::Response::SharedPtr res) +{ + std::lock_guard lock(mutex_); + RCLCPP_INFO(get_logger(), "start weight grid seach."); + + double minimum_mse = std::numeric_limits::max(); + double best_w0 = 0.0; + double best_w1 = 0.0; + double best_w2 = 0.0; + double best_w3 = 0.0; + + for (const auto & w_lat_comfortability : parameters_->grid) { + for (const auto & w_lon_comfortability : parameters_->grid) { + for (const auto & w_efficiency : parameters_->grid) { + for (const auto & w_safety : parameters_->grid) { + const auto mse = + search(w_lat_comfortability, w_lon_comfortability, w_efficiency, w_safety); + if (mse < minimum_mse) { + minimum_mse = mse; + best_w0 = w_lat_comfortability; + best_w1 = w_lon_comfortability; + best_w2 = w_efficiency; + best_w3 = w_safety; + } + std::cout << "---result---" << std::endl; + std::cout << " MSE:" << mse << " [w0]:" << w_lat_comfortability + << " [w1]:" << w_lon_comfortability << " [w2]:" << w_efficiency + << " [w3]:" << w_safety << std::endl; + std::cout << "MIN MSE:" << minimum_mse << " [w0]:" << best_w0 << " [w1]:" << best_w1 + << " [w2]:" << best_w2 << " [w3]:" << best_w3 << std::endl; + } + } + } + } + RCLCPP_INFO(get_logger(), "finish weight grid seach."); + + res->success = true; +} + +double BehaviorAnalyzerNode::search( + const double w0, const double w1, const double w2, const double w3) const +{ + reader_.seek(0); + const auto bag_data = std::make_shared( + duration_cast(reader_.get_metadata().starting_time.time_since_epoch()).count()); + + auto p_tmp = parameters_; + p_tmp->w_lat_comfortability = w0; + p_tmp->w_lon_comfortability = w1; + p_tmp->w_efficiency = w2; + p_tmp->w_safety = w3; + + double mse = 0.0; + while (reader_.has_next()) { + update(bag_data, parameters_->dt); + + const auto data_set = std::make_shared(bag_data, vehicle_info_, p_tmp); + const auto mean_square_error = data_set->loss(); + if (mean_square_error.has_value()) { + mse += mean_square_error.value(); + } + } + + return mse; +} + +void BehaviorAnalyzerNode::analyze(const std::shared_ptr & bag_data) const +{ + const auto data_set = std::make_shared(bag_data, vehicle_info_, parameters_); + + const auto opt_tf = std::dynamic_pointer_cast>(bag_data->buffers.at(TOPIC::TF)) + ->get(bag_data->timestamp); + if (opt_tf.has_value()) { + pub_tf_->publish(opt_tf.value()); + } + + const auto opt_objects = + std::dynamic_pointer_cast>(bag_data->buffers.at(TOPIC::OBJECTS)) + ->get(bag_data->timestamp); + if (opt_objects.has_value()) { + pub_objects_->publish(opt_objects.value()); + } + + const auto opt_trajectory = + std::dynamic_pointer_cast>(bag_data->buffers.at(TOPIC::TRAJECTORY)) + ->get(bag_data->timestamp); + if (opt_trajectory.has_value()) { + pub_trajectory_->publish(opt_trajectory.value()); + } + + metrics(data_set); + + score(data_set); + + visualize(data_set); + + print(data_set); +} + +void BehaviorAnalyzerNode::metrics(const std::shared_ptr & data_set) const +{ + { + Float32MultiArrayStamped msg{}; + + msg.stamp = now(); + msg.data.resize(static_cast(METRIC::SIZE) * parameters_->resample_num); + + const auto set_metrics = [&msg, this](const auto & data, const auto metric_type) { + const auto offset = static_cast(metric_type) * parameters_->resample_num; + const auto metric = data.values.at(metric_type); + std::copy(metric.begin(), metric.end(), msg.data.begin() + offset); + }; + + set_metrics(data_set->manual, METRIC::LATERAL_ACCEL); + set_metrics(data_set->manual, METRIC::LONGITUDINAL_JERK); + set_metrics(data_set->manual, METRIC::TRAVEL_DISTANCE); + set_metrics(data_set->manual, METRIC::MINIMUM_TTC); + + pub_manual_metrics_->publish(msg); + } + + const auto autoware_trajectory = data_set->sampling.autoware(); + if (autoware_trajectory.has_value()) { + Float32MultiArrayStamped msg{}; + + msg.stamp = now(); + msg.data.resize(static_cast(METRIC::SIZE) * parameters_->resample_num); + + const auto set_metrics = [&msg, this](const auto & data, const auto metric_type) { + const auto offset = static_cast(metric_type) * parameters_->resample_num; + const auto metric = data.values.at(metric_type); + std::copy(metric.begin(), metric.end(), msg.data.begin() + offset); + }; + + set_metrics(autoware_trajectory.value(), METRIC::LATERAL_ACCEL); + set_metrics(autoware_trajectory.value(), METRIC::LONGITUDINAL_JERK); + set_metrics(autoware_trajectory.value(), METRIC::TRAVEL_DISTANCE); + set_metrics(autoware_trajectory.value(), METRIC::MINIMUM_TTC); + + pub_system_metrics_->publish(msg); + } +} + +void BehaviorAnalyzerNode::score(const std::shared_ptr & data_set) const +{ + { + Float32MultiArrayStamped msg{}; + + msg.stamp = now(); + msg.data.resize(static_cast(SCORE::SIZE)); + + const auto set_reward = [&msg](const auto & data, const auto score_type) { + msg.data.at(static_cast(score_type)) = static_cast(data.scores.at(score_type)); + }; + + set_reward(data_set->manual, SCORE::LONGITUDINAL_COMFORTABILITY); + set_reward(data_set->manual, SCORE::LATERAL_COMFORTABILITY); + set_reward(data_set->manual, SCORE::EFFICIENCY); + set_reward(data_set->manual, SCORE::SAFETY); + + pub_manual_score_->publish(msg); + } + + const auto autoware_trajectory = data_set->sampling.autoware(); + if (autoware_trajectory.has_value()) { + Float32MultiArrayStamped msg{}; + + msg.stamp = now(); + msg.data.resize(static_cast(SCORE::SIZE)); + + const auto set_reward = [&msg](const auto & data, const auto score_type) { + msg.data.at(static_cast(score_type)) = static_cast(data.scores.at(score_type)); + }; + + set_reward(autoware_trajectory.value(), SCORE::LONGITUDINAL_COMFORTABILITY); + set_reward(autoware_trajectory.value(), SCORE::LATERAL_COMFORTABILITY); + set_reward(autoware_trajectory.value(), SCORE::EFFICIENCY); + set_reward(autoware_trajectory.value(), SCORE::SAFETY); + + pub_system_score_->publish(msg); + } +} + +void BehaviorAnalyzerNode::visualize(const std::shared_ptr & data_set) const +{ + MarkerArray msg; + + size_t i = 0; + for (const auto & point : data_set->manual.odometry_history) { + Marker marker = createDefaultMarker( + "map", rclcpp::Clock{RCL_ROS_TIME}.now(), "manual", i++, Marker::ARROW, + createMarkerScale(0.7, 0.3, 0.3), createMarkerColor(1.0, 0.0, 0.0, 0.999)); + marker.pose = point.pose.pose; + msg.markers.push_back(marker); + } + + for (const auto & trajectory : data_set->sampling.data) { + Marker marker = createDefaultMarker( + "map", rclcpp::Clock{RCL_ROS_TIME}.now(), "candidates", i++, Marker::LINE_STRIP, + createMarkerScale(0.05, 0.0, 0.0), createMarkerColor(0.0, 0.0, 1.0, 0.999)); + if (!trajectory.feasible()) { + for (const auto & point : trajectory.points) { + marker.points.push_back(point.pose.position); + marker.colors.push_back(createMarkerColor(0.1, 0.1, 0.1, 0.5)); + } + } else { + for (const auto & point : trajectory.points) { + marker.points.push_back(point.pose.position); + marker.colors.push_back(createMarkerColor(0.0, 0.0, 1.0, 0.999)); + } + } + msg.markers.push_back(marker); + } + + const auto best_trajectory = data_set->sampling.best(); + if (best_trajectory.has_value()) { + Marker marker = createDefaultMarker( + "map", rclcpp::Clock{RCL_ROS_TIME}.now(), "best", i++, Marker::LINE_STRIP, + createMarkerScale(0.2, 0.0, 0.0), createMarkerColor(1.0, 1.0, 1.0, 0.999)); + for (const auto & point : best_trajectory.value().points) { + marker.points.push_back(point.pose.position); + } + msg.markers.push_back(marker); + } + + const auto autoware_trajectory = data_set->sampling.autoware(); + if (autoware_trajectory.has_value()) { + for (const auto & point : autoware_trajectory.value().points) { + Marker marker = createDefaultMarker( + "map", rclcpp::Clock{RCL_ROS_TIME}.now(), "system", i++, Marker::ARROW, + createMarkerScale(0.7, 0.3, 0.3), createMarkerColor(1.0, 1.0, 0.0, 0.999)); + marker.pose = point.pose; + msg.markers.push_back(marker); + } + } + + pub_marker_->publish(msg); +} + +void BehaviorAnalyzerNode::print(const std::shared_ptr & data_set) const +{ + const auto autoware_trajectory = data_set->sampling.autoware(); + if (!autoware_trajectory.has_value()) { + return; + } + + const auto best_trajectory = data_set->sampling.best(); + if (!best_trajectory.has_value()) { + return; + } + + std::cout << "---result---" << std::endl; + std::cout << "[HUMAN] SCORE:" << data_set->manual.total() << std::endl; + std::cout << "[AUTOWARE] SCORE:" << autoware_trajectory.value().total() << std::endl; + std::cout << "[SAMPLING] BEST SCORE:" << best_trajectory.value().total() << "(" + << best_trajectory.value().tag << ")" << std::endl; +} + +void BehaviorAnalyzerNode::on_timer() +{ + std::lock_guard lock(mutex_); + update(bag_data_, 0.1); + analyze(bag_data_); +} +} // namespace autoware::behavior_analyzer + +#include +RCLCPP_COMPONENTS_REGISTER_NODE(autoware::behavior_analyzer::BehaviorAnalyzerNode) diff --git a/planning/autoware_planning_data_analyzer/src/node.hpp b/planning/autoware_planning_data_analyzer/src/node.hpp new file mode 100644 index 00000000..137f9b2c --- /dev/null +++ b/planning/autoware_planning_data_analyzer/src/node.hpp @@ -0,0 +1,92 @@ +// Copyright 2024 TIER IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef NODE_HPP_ +#define NODE_HPP_ + +#include "data_structs.hpp" +#include "rosbag2_cpp/reader.hpp" +#include "type_alias.hpp" + +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include + +namespace autoware::behavior_analyzer +{ +class BehaviorAnalyzerNode : public rclcpp::Node +{ +public: + explicit BehaviorAnalyzerNode(const rclcpp::NodeOptions & node_options); + +private: + void on_timer(); + + void play(const SetBool::Request::SharedPtr req, SetBool::Response::SharedPtr res); + + void rewind(const Trigger::Request::SharedPtr req, Trigger::Response::SharedPtr res); + + void weight(const Trigger::Request::SharedPtr req, Trigger::Response::SharedPtr res); + + void update(const std::shared_ptr & bag_data, const double dt) const; + + void analyze(const std::shared_ptr & bag_data) const; + + void metrics(const std::shared_ptr & data_set) const; + + void score(const std::shared_ptr & data_set) const; + + void visualize(const std::shared_ptr & data_set) const; + + void print(const std::shared_ptr & data_set) const; + + double search(const double w0, const double w1, const double w2, const double w3) const; + + rclcpp::TimerBase::SharedPtr timer_; + rclcpp::Publisher::SharedPtr pub_marker_; + rclcpp::Publisher::SharedPtr pub_odometry_; + rclcpp::Publisher::SharedPtr pub_objects_; + rclcpp::Publisher::SharedPtr pub_trajectory_; + rclcpp::Publisher::SharedPtr pub_tf_; + rclcpp::Publisher::SharedPtr pub_manual_metrics_; + rclcpp::Publisher::SharedPtr pub_system_metrics_; + rclcpp::Publisher::SharedPtr pub_manual_score_; + rclcpp::Publisher::SharedPtr pub_system_score_; + rclcpp::Service::SharedPtr srv_play_; + rclcpp::Service::SharedPtr srv_rewind_; + rclcpp::Service::SharedPtr srv_weight_; + + vehicle_info_utils::VehicleInfo vehicle_info_; + + std::shared_ptr bag_data_; + + std::shared_ptr parameters_; + + mutable std::mutex mutex_; + + mutable rosbag2_cpp::Reader reader_; +}; +} // namespace autoware::behavior_analyzer + +#endif // NODE_HPP_ diff --git a/planning/autoware_planning_data_analyzer/src/type_alias.hpp b/planning/autoware_planning_data_analyzer/src/type_alias.hpp new file mode 100644 index 00000000..53b0e443 --- /dev/null +++ b/planning/autoware_planning_data_analyzer/src/type_alias.hpp @@ -0,0 +1,68 @@ +// Copyright 2024 TIER IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef TYPE_ALIAS_HPP_ +#define TYPE_ALIAS_HPP_ + +#include +#include + +#include "autoware_map_msgs/msg/lanelet_map_bin.hpp" +#include "autoware_perception_msgs/msg/predicted_objects.hpp" +#include "autoware_planning_msgs/msg/lanelet_route.hpp" +#include "autoware_planning_msgs/msg/trajectory.hpp" +#include "autoware_planning_msgs/msg/trajectory_point.hpp" +#include "autoware_vehicle_msgs/msg/steering_report.hpp" +#include "geometry_msgs/msg/accel_with_covariance_stamped.hpp" +#include "nav_msgs/msg/odometry.hpp" +#include "tier4_debug_msgs/msg/float32_multi_array_stamped.hpp" +#include "visualization_msgs/msg/marker.hpp" +#include "visualization_msgs/msg/marker_array.hpp" +#include +#include +#include + +namespace autoware::behavior_analyzer +{ +// std +using std::chrono::duration_cast; +using std::chrono::nanoseconds; +using std::chrono::seconds; + +// autoware +using autoware_map_msgs::msg::LaneletMapBin; +using autoware_perception_msgs::msg::PredictedObjectKinematics; +using autoware_perception_msgs::msg::PredictedObjects; +using autoware_planning_msgs::msg::LaneletRoute; +using autoware_planning_msgs::msg::Trajectory; +using autoware_planning_msgs::msg::TrajectoryPoint; +using autoware_vehicle_msgs::msg::SteeringReport; +using route_handler::RouteHandler; + +// ros2 +using geometry_msgs::msg::AccelWithCovarianceStamped; +using geometry_msgs::msg::Point; +using geometry_msgs::msg::Pose; +using geometry_msgs::msg::Twist; +using geometry_msgs::msg::Vector3; +using nav_msgs::msg::Odometry; +using std_srvs::srv::SetBool; +using std_srvs::srv::Trigger; +using tf2_msgs::msg::TFMessage; +using tier4_debug_msgs::msg::Float32MultiArrayStamped; +using visualization_msgs::msg::Marker; +using visualization_msgs::msg::MarkerArray; +} // namespace autoware::behavior_analyzer + +#endif // TYPE_ALIAS_HPP_ diff --git a/planning/autoware_planning_data_analyzer/src/utils.hpp b/planning/autoware_planning_data_analyzer/src/utils.hpp new file mode 100644 index 00000000..caf0c217 --- /dev/null +++ b/planning/autoware_planning_data_analyzer/src/utils.hpp @@ -0,0 +1,272 @@ +// Copyright 2024 TIER IV, Inc. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#ifndef UTILS_HPP_ +#define UTILS_HPP_ + +#include "autoware/motion_utils/trajectory/interpolation.hpp" +#include "autoware/motion_utils/trajectory/trajectory.hpp" +#include "autoware_frenet_planner/frenet_planner.hpp" +#include "autoware_path_sampler/prepare_inputs.hpp" +#include "autoware_path_sampler/utils/trajectory_utils.hpp" +#include "data_structs.hpp" +#include "type_alias.hpp" + +#include +#include +#include +#include +#include +#include +#include + +namespace autoware::behavior_analyzer::utils +{ +Point vector2point(const geometry_msgs::msg::Vector3 & v) +{ + return autoware::universe_utils::createPoint(v.x, v.y, v.z); +} + +tf2::Vector3 from_msg(const Point & p) +{ + return tf2::Vector3(p.x, p.y, p.z); +} + +tf2::Vector3 get_velocity_in_world_coordinate(const PredictedObjectKinematics & kinematics) +{ + const auto pose = kinematics.initial_pose_with_covariance.pose; + const auto v_local = kinematics.initial_twist_with_covariance.twist.linear; + const auto v_world = autoware::universe_utils::transformPoint(vector2point(v_local), pose); + + return from_msg(v_world) - from_msg(pose.position); +} + +tf2::Vector3 get_velocity_in_world_coordinate(const Odometry & odometry) +{ + const auto pose = odometry.pose.pose; + const auto v_local = odometry.twist.twist.linear; + const auto v_world = autoware::universe_utils::transformPoint(vector2point(v_local), pose); + + return from_msg(v_world) - from_msg(pose.position); +} + +tf2::Vector3 get_velocity_in_world_coordinate(const TrajectoryPoint & point) +{ + const auto pose = point.pose; + const auto v_local = + geometry_msgs::build().x(point.longitudinal_velocity_mps).y(0.0).z(0.0); + const auto v_world = autoware::universe_utils::transformPoint(vector2point(v_local), pose); + + return from_msg(v_world) - from_msg(pose.position); +} + +double time_to_collision( + const PredictedObjects & objects, const Pose & p_ego, const tf2::Vector3 & v_ego) +{ + if (objects.objects.empty()) { + return std::numeric_limits::max(); + } + + std::vector time_to_collisions(objects.objects.size()); + + for (const auto & object : objects.objects) { + const auto p_object = object.kinematics.initial_pose_with_covariance.pose; + const auto v_ego2object = + autoware::universe_utils::point2tfVector(p_ego.position, p_object.position); + + const auto v_object = get_velocity_in_world_coordinate(object.kinematics); + const auto v_relative = tf2::tf2Dot(v_ego2object.normalized(), v_ego) - + tf2::tf2Dot(v_ego2object.normalized(), v_object); + + time_to_collisions.push_back(v_ego2object.length() / v_relative); + } + + const auto itr = std::remove_if( + time_to_collisions.begin(), time_to_collisions.end(), + [](const auto & value) { return value < 1e-3; }); + time_to_collisions.erase(itr, time_to_collisions.end()); + + std::sort(time_to_collisions.begin(), time_to_collisions.end()); + + return time_to_collisions.front(); +} + +auto convertToTrajectoryPoints( + const autoware::sampler_common::Trajectory & trajectory, + const vehicle_info_utils::VehicleInfo & vehicle_info) -> std::vector +{ + std::vector traj_points; + for (auto i = 0UL; i < trajectory.points.size(); ++i) { + TrajectoryPoint p; + p.pose.position.x = trajectory.points[i].x(); + p.pose.position.y = trajectory.points[i].y(); + auto quat = tf2::Quaternion(); + quat.setRPY(0.0, 0.0, trajectory.yaws[i]); + p.pose.orientation.w = quat.w(); + p.pose.orientation.x = quat.x(); + p.pose.orientation.y = quat.y(); + p.pose.orientation.z = quat.z(); + p.longitudinal_velocity_mps = trajectory.longitudinal_velocities.at(i); + p.lateral_velocity_mps = trajectory.lateral_velocities.at(i); + p.front_wheel_angle_rad = vehicle_info.wheel_base_m * trajectory.curvatures.at(i); + traj_points.push_back(p); + } + return traj_points; +} + +template +auto convertToFrenetPoint(const T & points, const Point & search_point_geom, const size_t seg_idx) + -> FrenetPoint +{ + FrenetPoint frenet_point; + + const double longitudinal_length = + autoware::motion_utils::calcLongitudinalOffsetToSegment(points, seg_idx, search_point_geom); + frenet_point.length = + autoware::motion_utils::calcSignedArcLength(points, 0, seg_idx) + longitudinal_length; + frenet_point.distance = + autoware::motion_utils::calcLateralOffset(points, search_point_geom, seg_idx); + + return frenet_point; +} + +auto prepareSamplingParameters( + const autoware::sampler_common::Configuration & initial_state, const double base_length, + const autoware::sampler_common::transform::Spline2D & path_spline, + [[maybe_unused]] const double trajectory_length, const TargetStateParameters & parameters) + -> autoware::frenet_planner::SamplingParameters +{ + autoware::frenet_planner::SamplingParameters sampling_parameters; + + // calculate target lateral positions + sampling_parameters.resolution = 0.5; + const auto max_s = path_spline.lastS(); + autoware::frenet_planner::SamplingParameter p; + p.target_duration = 10.0; + for (const auto lon_acceleration : parameters.lon_accelerations) { + p.target_state.longitudinal_acceleration = lon_acceleration; + p.target_state.longitudinal_velocity = + initial_state.velocity + lon_acceleration * p.target_duration; + p.target_state.position.s = std::min( + max_s, path_spline.frenet(initial_state.pose).s + + std::max( + 0.0, initial_state.velocity * p.target_duration + + 0.5 * lon_acceleration * std::pow(p.target_duration, 2.0) - base_length)); + for (const auto lat_position : parameters.lat_positions) { + p.target_state.position.d = lat_position; + for (const auto lat_velocity : parameters.lat_velocities) { + p.target_state.lateral_velocity = lat_velocity; + for (const auto lat_acceleration : parameters.lat_accelerations) { + p.target_state.lateral_acceleration = lat_acceleration; + sampling_parameters.parameters.push_back(p); + } + } + } + if (p.target_state.position.s == max_s) break; + } + return sampling_parameters; +} + +auto resampling( + const Trajectory & trajectory, const Pose & p_ego, const size_t resample_num, + const double time_resolution) -> std::vector +{ + const auto ego_seg_idx = autoware::motion_utils::findFirstNearestSegmentIndexWithSoftConstraints( + trajectory.points, p_ego, 10.0, M_PI_2); + + std::vector output; + const auto vehicle_pose_frenet = + convertToFrenetPoint(trajectory.points, p_ego.position, ego_seg_idx); + + double length = 0.0; + for (size_t i = 0; i < resample_num; i++) { + const auto pose = autoware::motion_utils::calcInterpolatedPose( + trajectory.points, vehicle_pose_frenet.length + length); + const auto p_trajectory = autoware::motion_utils::calcInterpolatedPoint(trajectory, pose); + output.push_back(p_trajectory); + + const auto pred_accel = p_trajectory.acceleration_mps2; + const auto pred_velocity = p_trajectory.longitudinal_velocity_mps; + + length += + pred_velocity * time_resolution + 0.5 * pred_accel * time_resolution * time_resolution; + } + + return output; +} + +auto sampling( + const Trajectory & trajectory, const Pose & p_ego, const double v_ego, const double a_ego, + const vehicle_info_utils::VehicleInfo & vehicle_info, + const std::shared_ptr & parameters) -> std::vector> +{ + const auto reference_trajectory = + autoware::path_sampler::preparePathSpline(trajectory.points, true); + + autoware::sampler_common::Configuration current_state; + current_state.pose = {p_ego.position.x, p_ego.position.y}; + current_state.heading = tf2::getYaw(p_ego.orientation); + current_state.velocity = v_ego; + + current_state.frenet = reference_trajectory.frenet(current_state.pose); + // current_state.pose = reference_trajectory.cartesian(current_state.frenet.s); + current_state.heading = reference_trajectory.yaw(current_state.frenet.s); + current_state.curvature = reference_trajectory.curvature(current_state.frenet.s); + + const auto trajectory_length = autoware::motion_utils::calcArcLength(trajectory.points); + const auto sampling_parameters = prepareSamplingParameters( + current_state, 0.0, reference_trajectory, trajectory_length, parameters->target_state); + + autoware::frenet_planner::FrenetState initial_frenet_state; + initial_frenet_state.position = reference_trajectory.frenet(current_state.pose); + initial_frenet_state.longitudinal_velocity = v_ego; + initial_frenet_state.longitudinal_acceleration = a_ego; + const auto s = initial_frenet_state.position.s; + const auto d = initial_frenet_state.position.d; + // Calculate Velocity and acceleration parametrized over arc length + // From appendix I of Optimal Trajectory Generation for Dynamic Street Scenarios in a Frenet + // Frame + const auto frenet_yaw = current_state.heading - reference_trajectory.yaw(s); + const auto path_curvature = reference_trajectory.curvature(s); + const auto delta_s = 0.001; + initial_frenet_state.lateral_velocity = (1 - path_curvature * d) * std::tan(frenet_yaw); + const auto path_curvature_deriv = + (reference_trajectory.curvature(s + delta_s) - path_curvature) / delta_s; + const auto cos_yaw = std::cos(frenet_yaw); + if (cos_yaw == 0.0) { + initial_frenet_state.lateral_acceleration = 0.0; + } else { + initial_frenet_state.lateral_acceleration = + -(path_curvature_deriv * d + path_curvature * initial_frenet_state.lateral_velocity) * + std::tan(frenet_yaw) + + ((1 - path_curvature * d) / (cos_yaw * cos_yaw)) * + (current_state.curvature * ((1 - path_curvature * d) / cos_yaw) - path_curvature); + } + + const auto sampling_frenet_trajectories = autoware::frenet_planner::generateTrajectories( + reference_trajectory, initial_frenet_state, sampling_parameters); + + std::vector> output; + output.reserve(sampling_frenet_trajectories.size()); + + for (const auto & trajectory : sampling_frenet_trajectories) { + output.push_back(convertToTrajectoryPoints( + trajectory.resampleTimeFromZero(parameters->time_resolution), vehicle_info)); + } + + return output; +} +} // namespace autoware::behavior_analyzer::utils + +#endif // UTILS_HPP_