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-# Sprint 0: Research Samuel Kühnel
-
-## Pylot
-
-## Planning
-
-- 4 different options
-- **Waypoint Planner**: Auto follows predefined waypoints. It recognizes traffic lights and stops at obstacles, but cannot avoid them
-- **Freenet-Optimal-Trajecotry-Planner**: CPP code with Python wrapper ([GitHub](https://github.com/erdos-project/frenet_optimal_trajectory_planner))
-→ Predefined line that is used for orientation → Can avoid obstacles!
-- **RRT\*-Planner**: RRT* algorithm to plan the path ([GitHub](https://github.com/erdos-project/rrt_star_planner))
-  - Creates random nodes
-  - Adds nodes to the graph that are not blocked by objects on the road
-  - Generally terminates as soon as a node is found in the target area
-  - RRT*: Searches for the shortest path
-- **Hybrid A\* planner**: Hybrid A* algorithm for route planning ([GitHub](https://github.com/erdos-project/hybrid_astar_planner))
-  - Calculates the shortest path between two nodes from a graph
-  - Similar to Dijkstra's algorithm
-  - Nodes are estimated based on their costs and promising nodes are selected first
-  - Hybrid A* algorithm: Not always optimal solution, but in the neighborhood of the optimal solution.
-
-## PAF 21-2
-
-### Perception
-
-### Obstacle detection
-
-- Detect objects via semantic lidar sensor
-- Provides x and y coordinates, as well as distance value
-- Additional information on position change in a time interval → Calculation of speed possible
-- Detects the object and returns either the value "Vehicle" or "Pedestrian"
-
-### TrafficLightDetection
-
-![diagramm.png](https://github.com/ll7/paf21-2/raw/main/docs/imgs/trafficlightdetection_diagram.jpg)
-
-- **FusionCamera** saves images from **RGBCamera** and **DepthCamera** with timestamp and then synchronizes with **SegmentationCamera**
-- Neural network based on [ResNet18](https://pytorch.org/hub/pytorch_vision_resnet/) (predefined PyTorch network)
-- Generally only traffic lights up to 100m distance
-- Canny algorithm to filter contours
-
-### Problems and solutions
-
-- Red background distorts traffic light phase detection → **Solution**: Narrow section of the traffic light image for phase detection
-- Yellow painted traffic lights distort traffic light phase detection → **Solution**: Filter out red and green sections beforehand using masks and convert remaining image to grayscale and add masks again.
-- **Problem without solution**: European traffic lights can sometimes not be recognized at the stop line.
-
-## Resumee
-
-### Perception
-
-- Status quo: LIDAR Sensor and Big Neural Network
-- Possible focus: Using smaller (pretrained) models to improve overall performance fast
-- Taking known issues into account
-
-### Planning
-
-- Currently decision tree for evaluating the current position
-- Trying out different heuristics → already given as repo