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+# Research: Why does the car hit obstacles
+
+## Summary of current obstacle detection in perception:
+- [Object detection](#Object-detection)
+- [Distance calculation](#Distance-calculation)
+- [Publishing of Outputs](#Publishing-of-Outputs)
+
+For more details, please refer to the current documentation.
+
+## Object detection:
+Active sensors:
+- Center camera
+- LIDAR
+
+Inactive sensors:
+- Cameras: right, left, back
+- Radar
+
+## Distance calculation
+- The vision_node receives depth-images from the lidar_distance_node for the specified camera angle
+
+- According to the distance-to-objects documentation, the LIDAR sensor's 3D values are projected onto a 2D image that matches the exact dimensions of the camera image. 
+Numpy formulas are used to reconstruct the depth image, with distances in meters represented as pixel values (grayscale image). 
+If no distance is found in the depth image, “infinity” is returned for this bounding box.
+
+- The LIDAR sensor may flicker; a higher spin rate of the LIDAR provieds fully reconstructed depth images but with lower resolution for the LIDAR points
+
+## Publishing of Outputs:
+- Class_index
+- Min_X
+- Min_Abs_y
+
+## Dependencies of detected and hit obstacles:
+
+- Are all obstacles detected? No
+- Does successful object detection depend on:
+    - Speed of the car? No
+    - Size of obstacles? No
+    - Type of obstacle? No
+    - Dynamic/static obstacle? No
+
+## Important aspects found during research:
+
+- Sometimes objects are not detected (sometimes e.g.: cyclists, construction site signs, open car doors)
+- Most object that are hit are detected
+- Sometimes distance calculation is incorrect (distance is set to infinity just before the collision)
+- Distance in x is alway > ~0.5 (is that a problem?), sometimes distance to a collided car is greater than the distance to a parked car on the side of the road
+- Significant issues in dead-end situations: -> 1.2 seconds of reversing -> the car often immediately collides with the obstacle again