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CarND-Controls-MPC

Self-Driving Car Engineer Nanodegree Program

Rubric Points

  • The Model: Student describes their model in detail. This includes the state, actuators and update equations.

The kinematic model includes the vehicle's x and y coordinates, orientation angle (psi), and velocity, as well as the cross-track error, and psi error (epsi). The actuator outputs are acceleration (throttle) and delta (steering angle). The model combines the state and actuations from the previous timestep to calculate the state for the current timestep based on the equations below:

model

  • Timestep Length and Elapsed Duration (N & dt): Student discusses the reasoning behind the chosen N (timestep length) and dt (elapsed duration between timesteps) values. Additionally the student details the previous values tried.

The values chosen for N and dt are 10 and 0.1, respectively. These values were recommended to me after many failed attempts and when I went to the Slack group for help. These values mean that the optimizer is considering a one-second duration in which to determine a corrective trajectory. Adjusting either N or dt (even by small amounts) often produced very different behavior. I tried a multitude of other values, with N ranging from 5 up to 25, and dt ranging from 0.05 to 0.25.

  • Polynomial Fitting and MPC Preprocessing: A polynomial is fitted to waypoints. If the student preprocesses waypoints, the vehicle state, and/or actuators prior to the MPC procedure it is described.

The waypoints are preprocessed by transforming them from world coordinates to vehicle coordinates This simplifies the polynomial regression because the vehicle's x and y coordinates are now (0, 0) with a zero-angle.

  • Model Predictive Control with Latency: The student implements Model Predictive Control that handles a 100 millisecond latency. Student provides details on how they deal with latency.

One method to fight latency was to really just limit the speed. Also, since the actuations are applied one timestep later and the delay is the same amount as the timestep length, the equations have been altered to account for the delay. To create a slower round around sharp corners, I added a penalty for the velocity * steer angle.


Dependencies

Basic Build Instructions

  1. Clone this repo.
  2. Make a build directory: mkdir build && cd build
  3. Compile: cmake .. && make
  4. Run it: ./mpc.

Build with Docker-Compose

The docker-compose can run the project into a container and exposes the port required by the simulator to run.

  1. Clone this repo.
  2. Build image: docker-compose build
  3. Run Container: docker-compose up
  4. On code changes repeat steps 2 and 3.

Tips

  1. The MPC is recommended to be tested on examples to see if implementation behaves as desired. One possible example is the vehicle offset of a straight line (reference). If the MPC implementation is correct, it tracks the reference line after some timesteps(not too many).
  2. The lake_track_waypoints.csv file has waypoints of the lake track. This could fit polynomials and points and see of how well your model tracks curve. NOTE: This file might be not completely in sync with the simulator so your solution should NOT depend on it.
  3. For visualization this C++ matplotlib wrapper could be helpful.)
  4. Tips for setting up your environment are available here
  5. VM Latency: Some students have reported differences in behavior using VM's ostensibly a result of latency. Please let us know if issues arise as a result of a VM environment.

Editor Settings

We have kept editor configuration files out of this repo to keep it as simple and environment agnostic as possible. However, we recommend using the following settings:

  • indent using spaces
  • set tab width to 2 spaces (keeps the matrices in source code aligned)

Code Style

Please (do your best to) stick to Google's C++ style guide.

Project Instructions and Rubric

Note: regardless of the changes you make, your project must be buildable using cmake and make!

More information is only accessible by people who are already enrolled in Term 2 of CarND. If you are enrolled, see the project page for instructions and the project rubric.

Hints!

  • You don't have to follow this directory structure, but if you do, your work will span all of the .cpp files here. Keep an eye out for TODOs.

Call for IDE Profiles Pull Requests

Help your fellow students!

We decided to create Makefiles with cmake to keep this project as platform agnostic as possible. We omitted IDE profiles to ensure students don't feel pressured to use one IDE or another.

However! I'd love to help people get up and running with their IDEs of choice. If you've created a profile for an IDE you think other students would appreciate, we'd love to have you add the requisite profile files and instructions to ide_profiles/. For example if you wanted to add a VS Code profile, you'd add:

  • /ide_profiles/vscode/.vscode
  • /ide_profiles/vscode/README.md

The README should explain what the profile does, how to take advantage of it, and how to install it.

Frankly, I've never been involved in a project with multiple IDE profiles before. I believe the best way to handle this would be to keep them out of the repo root to avoid clutter. Most profiles will include instructions to copy files to a new location to get picked up by the IDE, but that's just a guess.

One last note here: regardless of the IDE used, every submitted project must still be compilable with cmake and make./

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