docs: update the list styles

perception/autoware_radar_crossing_objects_noise_filter/README.md

@@ -10,7 +10,7 @@ This package can filter the noise objects which cross to the ego vehicle.
 This package aim to filter the noise objects which cross from the ego vehicle.
 The reason why these objects are noise is as below.
 
-- 1. The objects with doppler velocity can be trusted more than those with vertical velocity to it.
+#### 1. The objects with doppler velocity can be trusted more than those with vertical velocity to it
 
 Radars can get velocity information of objects as doppler velocity, but cannot get vertical velocity to doppler velocity directory.
 Some radars can output the objects with not only doppler velocity but also vertical velocity by estimation.
@@ -22,7 +22,7 @@ Velocity estimation fails on static objects, resulting in ghost objects crossing
 
 ![vertical_velocity_objects](docs/vertical_velocity_objects.png)
 
-- 2. Turning around by ego vehicle affect the output from radar.
+#### 2. Turning around by ego vehicle affect the output from radar
 
 When the ego vehicle turns around, the radars outputting at the object level sometimes fail to estimate the twist of objects correctly even if [radar_tracks_msgs_converter](https://github.com/autowarefoundation/autoware.universe/tree/main/perception/autoware_radar_tracks_msgs_converter) compensates by the ego vehicle twist.
 So if an object detected by radars has circular motion viewing from base_link, it is likely that the speed is estimated incorrectly and that the object is a static object.

perception/autoware_radar_object_clustering/README.md

@@ -17,12 +17,12 @@ Therefore, by this package the multiple detection results are clustered into one
 
 ### Algorithm
 
-- 1. Sort by distance from `base_link`
+#### 1. Sort by distance from `base_link`
 
 At first, to prevent changing the result from depending on the order of objects in DetectedObjects, input objects are sorted by distance from `base_link`.
 In addition, to apply matching in closeness order considering occlusion, objects are sorted in order of short distance in advance.
 
-- 2. Clustering
+#### 2. Clustering
 
 If two radar objects are near, and yaw angle direction and velocity between two radar objects is similar (the degree of these is defined by parameters), then these are clustered.
 Note that radar characteristic affect parameters for this matching.
@@ -32,13 +32,13 @@ For example, if resolution of range distance or angle is low and accuracy of vel
 
 After grouping for all radar objects, if multiple radar objects are grouping, the kinematics of the new clustered object is calculated from average of that and label and shape of the new clustered object is calculated from top confidence in radar objects.
 
-- 3. Fixed label correction
+#### 3. Fixed label correction
 
 When the label information from radar outputs lack accuracy, `is_fixed_label` parameter is recommended to set `true`.
 If the parameter is true, the label of a clustered object is overwritten by the label set by `fixed_label` parameter.
 If this package use for faraway dynamic object detection with radar, the parameter is recommended to set to `VEHICLE`.
 
-- 4. Fixed size correction
+#### 4. Fixed size correction
 
 When the size information from radar outputs lack accuracy, `is_fixed_size` parameter is recommended to set `true`.
 If the parameter is true, the size of a clustered object is overwritten by the label set by `size_x`, `size_y`, and `size_z` parameters.

planning/autoware_path_optimizer/docs/mpt.md

@@ -360,9 +360,9 @@ $$
 To realize collision-free trajectory planning, we have to formulate constraints that the vehicle is inside the road and also does not collide with obstacles in linear equations.
 For linearity, we implemented some methods to approximate the vehicle shape with a set of circles, that is reliable and easy to implement.
 
-- 1. Bicycle Model
-- 2. Uniform Circles
-- 3. Fitting Uniform Circles
+1. Bicycle Model
+2. Uniform Circles
+3. Fitting Uniform Circles
 
 ![vehicle_circles](../media/vehicle_circles.svg)