Python version

Our code is tested with Python 3.5.2

List of components

• frictionmap: This package contains the functions related to the creation and handling of friction maps along the race track.
• helper_funcs_glob: This package contains some helper functions used in several other functions when calculating the global race trajectory.
• inputs: This folder contains the vehicle dynamics information, the reference track csvs and friction maps.
• opt_mintime_traj: This package contains the functions required to find the time-optimal trajectory.
• params: This folder contains a parameter file with optimization and vehicle parameters.

Trajectory Planning Helpers repository

Lots of the required functions for trajectory planning are cumulated in our trajectory planning helpers repository. It can be found on https://github.com/TUMFTM/trajectory_planning_helpers. They can be quite useful for other projects as well.

Dependencies

Use the provided requirements.txt in the root directory of this repo, in order to install all required modules.
pip3 install -r /path/to/requirements.txt

Creating your own friction map

The script main_gen_frictionmap.py can be used to create your own friction map for any race track file supplied in the input folder. The resulting maps are stored in the inputs/frictionmaps folder. These friction maps can be used within the minimum time optimization. In principle, they can also be considered within the velocity profile calculation of the minimum curvature planner. However, this is currently not supported from our side.

Running the code

• Step 1: (optional) Adjust the parameter file that can be found in the params folder (required file).
• Step 2: (optional) Adjust the ggv diagram and ax_max_machines files in inputs/veh_dyn_info (if used).
• Step 3: (optional) Add your own reference track file in inputs/tracks (required file).
• Step 4: (optional) Add your own friction map files in inputs/frictionmaps (if used).
• Step 5: Adjust the parameters in the upper part of main_globaltraj.py and execute it to start the trajectory generation process. The calculated race trajectory is stored in outputs/traj_race_cl.csv.

IMPORTANT: For further information on the minimum time optimization have a look into the according Readme.md which can be found in the opt_mintime_traj folder!

Wording and conventions

We tried to keep a consistant wording for the variable names:

• path -> [x, y] Describes any array containing x,y coordinates of points (i.e. point coordinates).\
• refline -> [x, y] A path that is used as reference line during our calculations.\
• reftrack -> [x, y, w_tr_right, w_tr_left] An array that contains not only the reference line information but also right and left track widths. In our case it contains the race track that is used as a basis for the raceline optimization.

Normal vectors usually point to the right in the direction of driving. Therefore, we get the track boundaries by multiplication as follows: norm_vector * w_tr_right, -norm_vector * w_tr_left.

Trajectory interface definition

The output csv contains the global race trajectory. The array is of size [no_points x 7] where no_points depends on stepsize and track length. The seven columns are structured as follows:

• s_m: float32, meter. Curvi-linear distance along the raceline.
• x_m: float32, meter. X-coordinate of raceline point.
• y_m: float32, meter. Y-coordinate of raceline point.
• psi_rad: float32, rad. Heading of raceline in current point from -pi to +pi rad. Zero is north (along y-axis).
• kappa_radpm: float32, rad/meter. Curvature of raceline in current point.
• vx_mps: float32, meter/second. Target velocity in current point.
• ax_mps2: float32, meter/second². Target acceleration in current point. We assume this acceleration to be constant from current point until next point.

References

• Minimum Curvature Trajectory Planning
  Heilmeier, Wischnewski, Hermansdorfer, Betz, Lienkamp, Lohmann
  Minimum Curvature Trajectory Planning and Control for an Autonomous Racecar
  DOI: 10.1080/00423114.2019.1631455
  Contact person: Alexander Heilmeier.

• Time-Optimal Trajectory Planning
  Christ, Wischnewski, Heilmeier, Lohmann
  Time-Optimal Trajectory Planning for a Race Car Considering Variable Tire-Road Friction Coefficients
  DOI: 10.1080/00423114.2019.1704804
  Contact person: Fabian Christ.

• Friction Map Generation
  Hermansdorfer, Betz, Lienkamp
  A Concept for Estimation and Prediction of the Tire-Road Friction Potential for an Autonomous Racecar
  DOI: 10.1109/ITSC.2019.8917024
  Contact person: Leonhard Hermansdorfer.