refactor

Signed-off-by: Yuki Takagi <yuki.takagi@tier4.jp>

Author: yuki-takagi-66

perception/map_based_prediction/README.md

@@ -206,18 +206,6 @@ If the target object is inside the road or crosswalk, this module outputs one or
   <img src="images/inside_road.svg" width=90%>
 </div>
 
-\*1:
-The crosswalk user's intention to walk is calculated in the same way as `Cases without traffic lights`.
-
-<div align="center">
-    <table>
-        <tr>
-            <td><img src="./docs/without_traffic_light.svg" width="600"></td>
-            <td><img src="./docs/with_traffic_light.svg" width="600"></td>
-        </tr>
-    </table>
-</div>
-
 ## Inputs / Outputs
 
 ### Input

perception/map_based_prediction/config/map_based_prediction.param.yaml

@@ -21,7 +21,7 @@
     acceleration_exponential_half_life: 2.5 # [s] When using vehicle acceleration. The decaying acceleration model considers that the current vehicle acceleration will be halved after this many seconds
     crosswalk_with_signal:
       use_crosswalk_signal: true
-      threshold_speed_as_stopping: 0.25 # [m/s] velocity threshold for the module to judge whether the objects is stopped
+      threshold_velocity_assumed_as_stopping: 0.25 # [m/s] velocity threshold for the module to judge whether the objects is stopped
       # If the pedestrian does not move for X seconds against green signal, the module judge that the pedestrian has no intention to walk.
       distance_set_for_no_intention_to_walk: [1.0, 5.0] # [m] ascending order, keys of map
       timeout_set_for_no_intention_to_walk: [1.0, 0.0] # [s] values of map

perception/map_based_prediction/include/map_based_prediction/map_based_prediction_node.hpp

@@ -192,7 +192,7 @@ class MapBasedPredictionNode : public rclcpp::Node
   double acceleration_exponential_half_life_;
 
   bool use_crosswalk_signal_;
-  double threshold_speed_as_stopping_;
+  double threshold_velocity_assumed_as_stopping_;
   std::vector<double> distance_set_for_no_intention_to_walk_;
   std::vector<double> timeout_set_for_no_intention_to_walk_;
 

perception/map_based_prediction/src/map_based_prediction_node.cpp

@@ -789,8 +789,8 @@ MapBasedPredictionNode::MapBasedPredictionNode(const rclcpp::NodeOptions & node_
     declare_parameter<double>("acceleration_exponential_half_life");
 
   use_crosswalk_signal_ = declare_parameter<bool>("crosswalk_with_signal.use_crosswalk_signal");
-  threshold_speed_as_stopping_ =
-    declare_parameter<double>("crosswalk_with_signal.threshold_speed_as_stopping");
+  threshold_velocity_assumed_as_stopping_ =
+    declare_parameter<double>("crosswalk_with_signal.threshold_velocity_assumed_as_stopping");
   distance_set_for_no_intention_to_walk_ = declare_parameter<std::vector<double>>(
     "crosswalk_with_signal.distance_set_for_no_intention_to_walk");
   timeout_set_for_no_intention_to_walk_ = declare_parameter<std::vector<double>>(
@@ -2298,7 +2298,7 @@ bool MapBasedPredictionNode::calcIntentionToCrossWithTrafficSgnal(
   if (
     signal_color == TrafficSignalElement::GREEN &&
     tier4_autoware_utils::calcNorm(object.kinematics.twist_with_covariance.twist.linear) <
-      threshold_speed_as_stopping_) {
+      threshold_velocity_assumed_as_stopping_) {
     stopped_times_against_green_.try_emplace(key, this->get_clock()->now());
 
     const auto timeout_no_intention_to_walk = [&]() {

planning/behavior_velocity_crosswalk_module/README.md

@@ -97,7 +97,7 @@ In the case of the upper figure, `ego_pass_later_margin_x` is `{0, 1, 2}` and `e
 In the same way, the boundary of B and C is calculated from `ego_pass_first_margin_x` and `ego_pass_first_margin_y`.
 In the case of the upper figure, `ego_pass_first_margin_x` is `{3, 5}` and `ego_pass_first_margin_y` is `{0, 1}`.
 
-Of course, if the red signal is indicating to the corresponding croswalk, the ego do not yield against the pedestians.
+If the red signal is indicating to the corresponding crosswalk, the ego do not yield against the pedestrians.
 
 <div align="center">
     <table>