diff --git a/perception/autoware_lidar_transfusion/include/autoware/lidar_transfusion/utils.hpp b/perception/autoware_lidar_transfusion/include/autoware/lidar_transfusion/utils.hpp
index feea2216c5f78..f3a7cecf9ce00 100644
--- a/perception/autoware_lidar_transfusion/include/autoware/lidar_transfusion/utils.hpp
+++ b/perception/autoware_lidar_transfusion/include/autoware/lidar_transfusion/utils.hpp
@@ -36,7 +36,7 @@ struct Box3D
   float yaw;
 };
 
-enum NetworkIO { voxels = 0, num_points, coors, cls_score, dir_pred, bbox_pred, ENUM_SIZE };
+enum NetworkIO { voxels = 0, num_points, coors, cls_score, bbox_pred, dir_pred, ENUM_SIZE };
 
 // cspell: ignore divup
 template <typename T1, typename T2>
diff --git a/perception/autoware_lidar_transfusion/lib/network/network_trt.cpp b/perception/autoware_lidar_transfusion/lib/network/network_trt.cpp
index 3eacf005cff4e..48e8c1014199c 100644
--- a/perception/autoware_lidar_transfusion/lib/network/network_trt.cpp
+++ b/perception/autoware_lidar_transfusion/lib/network/network_trt.cpp
@@ -23,6 +23,20 @@
 namespace autoware::lidar_transfusion
 {
 
+inline NetworkIO nameToNetworkIO(const char * name)
+{
+  static const std::unordered_map<std::string_view, NetworkIO> name_to_enum = {
+    {"voxels", NetworkIO::voxels},        {"num_points", NetworkIO::num_points},
+    {"coors", NetworkIO::coors},          {"cls_score0", NetworkIO::cls_score},
+    {"bbox_pred0", NetworkIO::bbox_pred}, {"dir_cls_pred0", NetworkIO::dir_pred}};
+
+  auto it = name_to_enum.find(name);
+  if (it != name_to_enum.end()) {
+    return it->second;
+  }
+  throw std::runtime_error("Invalid input name: " + std::string(name));
+}
+
 std::ostream & operator<<(std::ostream & os, const ProfileDimension & profile)
 {
   std::string delim = "";
@@ -253,8 +267,14 @@ bool NetworkTRT::validateNetworkIO()
       << ". Actual size: " << engine->getNbIOTensors() << "." << std::endl;
     throw std::runtime_error("Failed to initialize TRT network.");
   }
+
+  // Initialize tensors_names_ with null values
+  tensors_names_.resize(NetworkIO::ENUM_SIZE, nullptr);
+
+  // Loop over the tensor names and place them in the correct positions
   for (int i = 0; i < NetworkIO::ENUM_SIZE; ++i) {
-    tensors_names_.push_back(engine->getIOTensorName(i));
+    const char * name = engine->getIOTensorName(i);
+    tensors_names_[nameToNetworkIO(name)] = name;
   }
 
   // Log the network IO
diff --git a/perception/autoware_lidar_transfusion/lib/postprocess/postprocess_kernel.cu b/perception/autoware_lidar_transfusion/lib/postprocess/postprocess_kernel.cu
index 905b4ea6f39f4..cfd3bffcc18c9 100644
--- a/perception/autoware_lidar_transfusion/lib/postprocess/postprocess_kernel.cu
+++ b/perception/autoware_lidar_transfusion/lib/postprocess/postprocess_kernel.cu
@@ -83,8 +83,8 @@ __global__ void generateBoxes3D_kernel(
   det_boxes3d[point_idx].y =
     box_output[point_idx + num_proposals] * num_point_values * voxel_size_y + min_y_range;
   det_boxes3d[point_idx].z = box_output[point_idx + 2 * num_proposals];
-  det_boxes3d[point_idx].width = expf(box_output[point_idx + 3 * num_proposals]);
-  det_boxes3d[point_idx].length = expf(box_output[point_idx + 4 * num_proposals]);
+  det_boxes3d[point_idx].length = expf(box_output[point_idx + 3 * num_proposals]);
+  det_boxes3d[point_idx].width = expf(box_output[point_idx + 4 * num_proposals]);
   det_boxes3d[point_idx].height = expf(box_output[point_idx + 5 * num_proposals]);
   det_boxes3d[point_idx].yaw =
     atan2f(dir_cls_output[point_idx], dir_cls_output[point_idx + num_proposals]);
diff --git a/perception/autoware_lidar_transfusion/lib/ros_utils.cpp b/perception/autoware_lidar_transfusion/lib/ros_utils.cpp
index dce4028759de5..ce2a8d1911941 100644
--- a/perception/autoware_lidar_transfusion/lib/ros_utils.cpp
+++ b/perception/autoware_lidar_transfusion/lib/ros_utils.cpp
@@ -48,12 +48,10 @@ void box3DToDetectedObject(
   obj.classification.emplace_back(classification);
 
   // pose and shape
-  // mmdet3d yaw format to ros yaw format
-  float yaw = box3d.yaw + autoware::universe_utils::pi / 2;
   obj.kinematics.pose_with_covariance.pose.position =
     autoware::universe_utils::createPoint(box3d.x, box3d.y, box3d.z);
   obj.kinematics.pose_with_covariance.pose.orientation =
-    autoware::universe_utils::createQuaternionFromYaw(yaw);
+    autoware::universe_utils::createQuaternionFromYaw(box3d.yaw);
   obj.shape.type = autoware_perception_msgs::msg::Shape::BOUNDING_BOX;
   obj.shape.dimensions =
     autoware::universe_utils::createTranslation(box3d.length, box3d.width, box3d.height);