refactor: correct spelling (#9528)

Signed-off-by: M. Fatih Cırıt <[email protected]>

.cspell.json

@@ -4,5 +4,5 @@
     "planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/scripts/**"
   ],
   "ignoreRegExpList": [],
-  "words": ["dltype", "tvmgen", "fromarray", "soblin"]
+  "words": ["dltype", "tvmgen", "fromarray", "soblin", "brkay54"]
 }

common/autoware_trajectory/include/autoware/trajectory/point.hpp

@@ -122,7 +122,7 @@ class Trajectory<geometry_msgs::msg::Point>
     using trajectory::detail::to_point;
     Eigen::Vector2d point(to_point(p).x, to_point(p).y);
     std::vector<double> distances_from_segments;
-    std::vector<double> lengthes_from_start_points;
+    std::vector<double> lengths_from_start_points;
 
     auto axis = detail::crop_bases(bases_, start_, end_);
 
@@ -149,7 +149,7 @@ class Trajectory<geometry_msgs::msg::Point>
       }
       if (constraints(length_from_start_point)) {
         distances_from_segments.push_back(distance_from_segment);
-        lengthes_from_start_points.push_back(length_from_start_point);
+        lengths_from_start_points.push_back(length_from_start_point);
       }
     }
     if (distances_from_segments.empty()) {
@@ -158,7 +158,7 @@ class Trajectory<geometry_msgs::msg::Point>
 
     auto min_it = std::min_element(distances_from_segments.begin(), distances_from_segments.end());
 
-    return lengthes_from_start_points[std::distance(distances_from_segments.begin(), min_it)] -
+    return lengths_from_start_points[std::distance(distances_from_segments.begin(), min_it)] -
            start_;
   }
 

common/autoware_trajectory/test/test_trajectory_container.cpp

@@ -106,9 +106,9 @@ TEST_F(TrajectoryTest, direction)
 
 TEST_F(TrajectoryTest, curvature)
 {
-  double curv = trajectory->curvature(0.0);
-  EXPECT_LT(-1.0, curv);
-  EXPECT_LT(curv, 1.0);
+  double curvature_val = trajectory->curvature(0.0);
+  EXPECT_LT(-1.0, curvature_val);
+  EXPECT_LT(curvature_val, 1.0);
 }
 
 TEST_F(TrajectoryTest, restore)

control/autoware_trajectory_follower_node/config/plot_juggler_trajectory_follower.xml

@@ -258,7 +258,7 @@
    <parameters delimiter="0" time_axis=""/>
   </plugin>
   <plugin ID="DataLoad MCAP"/>
-  <plugin ID="DataLoad ROS2 bags">
+  <plugin ID="DataLoad ROS 2 bags">
    <use_header_stamp value="false"/>
    <discard_large_arrays value="true"/>
    <max_array_size value="100"/>
@@ -268,7 +268,7 @@
   </plugin>
   <plugin ID="DataLoad ULog"/>
   <plugin ID="MQTT Subscriber (Mosquitto)"/>
-  <plugin ID="ROS2 Topic Subscriber">
+  <plugin ID="ROS 2 Topic Subscriber">
    <use_header_stamp value="false"/>
    <discard_large_arrays value="false"/>
    <max_array_size value="100"/>
@@ -286,7 +286,7 @@
    <scripts/>
   </plugin>
   <plugin ID="CSV Exporter"/>
-  <plugin ID="ROS2 Topic Re-Publisher"/>
+  <plugin ID="ROS 2 Topic Re-Publisher"/>
  </Plugins>
  <!-- - - - - - - - - - - - - - - -->
  <previouslyLoaded_Datafiles/>

perception/autoware_shape_estimation/lib/tensorrt_shape_estimator.cpp

@@ -140,7 +140,7 @@ void TrtShapeEstimator::preprocess(const DetectedObjectsWithFeature & input)
       }
 
       int iter_count = static_cast<int>(input_pc_size) / point_size_of_cloud;
-      int remainer_count = static_cast<int>(input_pc_size) % point_size_of_cloud;
+      int remaining_points_count = static_cast<int>(input_pc_size) % point_size_of_cloud;
 
       for (int j = 1; j < iter_count; j++) {
         for (int k = 0; k < point_size_of_cloud; k++) {
@@ -155,7 +155,7 @@ void TrtShapeEstimator::preprocess(const DetectedObjectsWithFeature & input)
         }
       }
 
-      for (int j = 0; j < remainer_count; j++) {
+      for (int j = 0; j < remaining_points_count; j++) {
         input_pc_h_[i * input_chan * input_pc_size + 0 + iter_count * point_size_of_cloud + j] =
           input_pc_h_[i * input_chan * input_pc_size + j];
 

planning/autoware_costmap_generator/src/costmap_generator.cpp

@@ -324,8 +324,8 @@ bool CostmapGenerator::isActive()
   if (param_->activate_by_scenario) {
     if (!scenario_) return false;
     const auto & s = scenario_->activating_scenarios;
-    return std::any_of(s.begin(), s.end(), [](const auto scen) {
-      return scen == tier4_planning_msgs::msg::Scenario::PARKING;
+    return std::any_of(s.begin(), s.end(), [](const auto scenario) {
+      return scenario == tier4_planning_msgs::msg::Scenario::PARKING;
     });
   }
 

planning/autoware_freespace_planning_algorithms/test/src/test_freespace_planning_algorithms.cpp

@@ -231,9 +231,9 @@ std::unique_ptr<fpa::AbstractPlanningAlgorithm> configure_astar(bool use_multi)
     obstacle_distance_weight,
     goal_lat_distance_weight};
 
-  auto clock_shrd_ptr = std::make_shared<rclcpp::Clock>(RCL_ROS_TIME);
-  auto algo = std::make_unique<fpa::AstarSearch>(
-    planner_common_param, vehicle_shape, astar_param, clock_shrd_ptr);
+  auto clock_ptr = std::make_shared<rclcpp::Clock>(RCL_ROS_TIME);
+  auto algo =
+    std::make_unique<fpa::AstarSearch>(planner_common_param, vehicle_shape, astar_param, clock_ptr);
   return algo;
 }
 
@@ -247,9 +247,9 @@ std::unique_ptr<fpa::AbstractPlanningAlgorithm> configure_rrtstar(bool informed,
   const double max_planning_time = 200;
   const auto rrtstar_param = fpa::RRTStarParam{update, informed, max_planning_time, mu, margin};
 
-  auto clock_shrd_ptr = std::make_shared<rclcpp::Clock>(RCL_ROS_TIME);
-  auto algo = std::make_unique<fpa::RRTStar>(
-    planner_common_param, vehicle_shape, rrtstar_param, clock_shrd_ptr);
+  auto clock_ptr = std::make_shared<rclcpp::Clock>(RCL_ROS_TIME);
+  auto algo =
+    std::make_unique<fpa::RRTStar>(planner_common_param, vehicle_shape, rrtstar_param, clock_ptr);
   return algo;
 }
 

planning/autoware_path_smoother/src/elastic_band.cpp

@@ -34,9 +34,9 @@ Eigen::SparseMatrix<double> makePMatrix(const int num_points)
 {
   std::vector<Eigen::Triplet<double>> triplet_vec;
   const auto assign_value_to_triplet_vec =
-    [&](const double row, const double colum, const double value) {
-      triplet_vec.push_back(Eigen::Triplet<double>(row, colum, value));
-      triplet_vec.push_back(Eigen::Triplet<double>(row + num_points, colum + num_points, value));
+    [&](const double row, const double column, const double value) {
+      triplet_vec.push_back(Eigen::Triplet<double>(row, column, value));
+      triplet_vec.push_back(Eigen::Triplet<double>(row + num_points, column + num_points, value));
     };
 
   for (int r = 0; r < num_points; ++r) {

planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/src/scene_intersection_prepare_data.cpp

@@ -938,7 +938,7 @@ std::vector<lanelet::ConstLineString3d> IntersectionModule::generateDetectionLan
   if (detection_lanelets.empty()) {
     // NOTE(soblin): due to the above filtering detection_lanelets may be empty or do not contain
     // conflicting_detection_lanelets
-    // OK to return empty detction_divsions
+    // OK to return empty detection_divisions
     return detection_divisions;
   }
 

planning/behavior_velocity_planner/autoware_behavior_velocity_intersection_module/test/test_util.cpp

@@ -89,7 +89,7 @@ TEST(TestUtil, retrievePathsBackward)
 }
 
 /*
-  TOOD(Mamoru Sobue): instantiating intersection_module and PlannerData is a messy
+  TODO(Mamoru Sobue): instantiating intersection_module and PlannerData is a messy
 class TestWithMap : public ::testing::Test
 {
 protected:

planning/sampling_based_planner/autoware_sampler_common/test/test_transform.cpp

@@ -103,9 +103,9 @@ TEST(splineTransform, benchFrenet)
       EXPECT_NEAR(frenet_naive.d, frenet_lut.d, precision);
     }
     std::cout << "size = " << size << std::endl;
-    std::cout << "\tnaive: " << std::chrono::duration_cast<std::chrono::milliseconds>(naive).count()
-              << "ms\n";
-    std::cout << "\tlut  : " << std::chrono::duration_cast<std::chrono::milliseconds>(lut).count()
+    std::cout << "\t naive: "
+              << std::chrono::duration_cast<std::chrono::milliseconds>(naive).count() << "ms\n";
+    std::cout << "\t lut  : " << std::chrono::duration_cast<std::chrono::milliseconds>(lut).count()
               << "ms\n";
   }
 }

simulator/autoware_carla_interface/README.md

@@ -136,14 +136,14 @@ The maps provided by the Carla Simulator ([Carla Lanelet2 Maps](https://bitbucke
 - Options to Modify the Map
 
   - A. Create a New Map from Scratch
-  - Use the [Tier4 Vector Map Builder](https://tools.tier4.jp/feature/vector_map_builder_ll2/) to create a new map.
+  - Use the [TIER IV Vector Map Builder](https://tools.tier4.jp/feature/vector_map_builder_ll2/) to create a new map.
 
   - B. Modify the Existing Carla Lanelet2 Maps
   - Adjust the longitude and latitude of the [Carla Lanelet2 Maps](https://bitbucket.org/carla-simulator/autoware-contents/src/master/maps/) to align with the PCD (origin).
     - Use this [tool](https://github.com/mraditya01/offset_lanelet2/tree/main) to modify the coordinates.
-    - Snap Lanelet with PCD and add the traffic lights using the [Tier4 Vector Map Builder](https://tools.tier4.jp/feature/vector_map_builder_ll2/).
+    - Snap Lanelet with PCD and add the traffic lights using the [TIER IV Vector Map Builder](https://tools.tier4.jp/feature/vector_map_builder_ll2/).
 
-- When using the Tier4 Vector Map Builder, you must convert the PCD format from `binary_compressed` to `ascii`. You can use `pcl_tools` for this conversion.
+- When using the TIER IV Vector Map Builder, you must convert the PCD format from `binary_compressed` to `ascii`. You can use `pcl_tools` for this conversion.
 - For reference, an example of Town01 with added traffic lights at one intersection can be downloaded [here](https://drive.google.com/drive/folders/1QFU0p3C8NW71sT5wwdnCKXoZFQJzXfTG?usp=sharing).
 
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