diff --git a/common/autoware_auto_geometry/design/polygon_intersection_2d-design.md b/common/autoware_auto_geometry/design/polygon_intersection_2d-design.md
index 98b5d045f2d07..dc4b33b56b4bc 100644
--- a/common/autoware_auto_geometry/design/polygon_intersection_2d-design.md
+++ b/common/autoware_auto_geometry/design/polygon_intersection_2d-design.md
@@ -2,7 +2,7 @@
 
 Two convex polygon's intersection can be visualized on the image below as the blue area:
 
-<img src="convex_polygon_intersection.png">
+<!-- <img src="convex_polygon_intersection.png"> -->
 
 ## Purpose / Use cases
 
diff --git a/perception/lidar_apollo_instance_segmentation/README.md b/perception/lidar_apollo_instance_segmentation/README.md
index 121f6d60e8dce..43ecae0880136 100644
--- a/perception/lidar_apollo_instance_segmentation/README.md
+++ b/perception/lidar_apollo_instance_segmentation/README.md
@@ -9,7 +9,7 @@ based on CNN based model and obstacle clustering method.
 
 ## Inner-workings / Algorithms
 
-See the [original design](https://github.com/ApolloAuto/apollo/blob/master/docs/specs/3d_obstacle_perception.md) by Apollo.
+See the [original design](https://github.com/ApolloAuto/apollo/blob/r6.0.0/docs/specs/3d_obstacle_perception.md) by Apollo.
 
 ## Inputs / Outputs
 
diff --git a/perception/lidar_apollo_segmentation_tvm/design/lidar-segmentation-design.md b/perception/lidar_apollo_segmentation_tvm/design/lidar-segmentation-design.md
index ac0d17a7139a8..0443b372622ba 100644
--- a/perception/lidar_apollo_segmentation_tvm/design/lidar-segmentation-design.md
+++ b/perception/lidar_apollo_segmentation_tvm/design/lidar-segmentation-design.md
@@ -30,4 +30,4 @@ The package exports a boolean `lidar_apollo_segmentation_tvm_BUILT` cmake variab
 
 ## Reference
 
-Lidar segmentation is based off a core algorithm by [Apollo](https://github.com/ApolloAuto/apollo/blob/master/docs/specs/3d_obstacle_perception.md), with modifications from [TierIV] (<https://github.com/tier4/lidar_instance_segmentation_tvm>) for the TVM backend.
+Lidar segmentation is based off a core algorithm by [Apollo](https://github.com/ApolloAuto/apollo/blob/r6.0.0/docs/specs/3d_obstacle_perception.md), with modifications from [TIER IV] (<https://github.com/tier4/lidar_instance_segmentation_tvm>) for the TVM backend.
diff --git a/vehicle/accel_brake_map_calibrator/accel_brake_map_calibrator/README.md b/vehicle/accel_brake_map_calibrator/accel_brake_map_calibrator/README.md
index 3c17acb300a18..c7b6432686218 100644
--- a/vehicle/accel_brake_map_calibrator/accel_brake_map_calibrator/README.md
+++ b/vehicle/accel_brake_map_calibrator/accel_brake_map_calibrator/README.md
@@ -184,7 +184,7 @@ ros2 run accel_brake_map_calibrator actuation_cmd_publisher.py
 
 ## Calibration Method
 
-Two algorithms are selectable for the acceleration map update, [update_offset_four_cell_around](#update_offset_each_cell) and [update_offset_each_cell](update_offset_each_cell). Please see the link for datails.
+Two algorithms are selectable for the acceleration map update, [update_offset_four_cell_around](#update_offset_four_cell_around-1) and [update_offset_each_cell](#update_offset_each_cell). Please see the link for datails.
 
 ### Data Preprocessing
 
@@ -221,8 +221,8 @@ Data selection is determined by the following thresholds.
 $$
 \begin{align}
     \theta[n]=&
-    \theta[n-1]+\frac{p[n-1]x^{(n)}}{\lambda+p[n-1](x^{(n)})^2}(y^{(n)}-\theta[n-1]x^{(n)})\\
-    p[n]=&\frac{p[n-1]}{\lambda+p[n-1](x^{(n)})^2}
+    \theta[n-1]+\frac{p[n-1]x^{(n)}}{\lambda+p[n-1]{(x^{(n)})}^2}(y^{(n)}-\theta[n-1]x^{(n)})\\
+    p[n]=&\frac{p[n-1]}{\lambda+p[n-1]{(x^{(n)})}^2}
 \end{align}
 $$