Self-Driving Car Engineer Nanodegree Program
In this project, we need to implement a path planning algorithms to drive a car on a highway on a simulator provided by Udacity(the simulator could be downloaded here). The simulator sends car telemetry information (car's position and velocity) and sensor fusion information about the rest of the cars in the highway (Ex. car id, velocity, position). It expects a set of points spaced in time at 0.02 seconds representing the car's trajectory. The communication between the simulator and the path planner is done using WebSocket. The path planner uses the uWebSockets WebSocket implementation to handle this communication. Udacity provides a seed project to start from on this project (here).
In this project your goal is to safely navigate around a virtual highway with other traffic that is driving +-10 MPH of the 50 MPH speed limit. You will be provided the car's localization and sensor fusion data, there is also a sparse map list of waypoints around the highway. The car should try to go as close as possible to the 50 MPH speed limit, which means passing slower traffic when possible, note that other cars will try to change lanes too. The car should avoid hitting other cars at all cost as well as driving inside of the marked road lanes at all times, unless going from one lane to another. The car should be able to make one complete loop around the 6946m highway. Since the car is trying to go 50 MPH, it should take a little over 5 minutes to complete 1 loop. Also the car should not experience total acceleration over 10 m/s^2 and jerk that is greater than 10 m/s^3.
With reference to Rubric:
No changes were made in the cmake configuration. A new file was added src/spline.h. It is the Cubic Spline interpolation implementation: a single .h file you can use splines instead of polynomials. It was a great suggestion from the classroom QA video. It works great.
I ran the simulator for 12 miles without incidents:
No speed limit red message was seen.
Max jerk red message was not seen.
No collisions.
The car stays in its lane most of the time but when it changes lane because of traffic or to return to the center lane.
The car change lanes when the there is a slow car in front of it, and it is safe to change lanes (no other cars around) or when it is safe to return the center lane.
Based on the provided code from the seed project, the path planning algorithms start at src/main.cpp line 110 to the line 335. The code could be separated into different functions to show the overall process, but I prefer to have everything in a single place to avoid jumping to different parts of the file or other files. In a more complicated environment and different requirements, more structure could be used. For now, comments are provided to improve the code readability.
The code consist of three parts:
Prediction line 116 to line 174
This part of the code deal with the telemetry and sensor fusion data. It intents to reason about the environment. In the case, we want to know three aspects of it:
- Is there a car in front of us blocking the traffic.
- Is there a car to the right of us making a lane change not safe.
- Is there a car to the left of us making a lane change not safe.
These questions are answered by calculating the lane each other car is and the position it will be at the end of the last plan trajectory. A car is considered "dangerous" when its distance to our car is less than 30 meters in front or behind us.
Behavior line 177 to line 210
This part decides what to do:
- If we have a car in front of us, do we change lanes?
- Do we speed up or slow down?
Based on the prediction of the situation we are in, this code increases the speed, decrease speed, or make a lane change when it is safe. Instead of increasing the speed at this part of the code, a diff_speed is created to be used for speed changes when generating the trajectory in the last part of the code. This approach makes the car more responsive acting faster to changing situations like a car in front of it trying to apply breaks to cause a collision.
Trajectory line 216 to line 335
This code does the calculation of the trajectory based on the speed and lane output from the behavior, car coordinates and past path points.
First, the last two points of the previous trajectory (or the car position if there are no previous trajectory, are used in conjunction three points at a far distance to initialize the spline calculation. To make the work less complicated to the spline calculation based on those points, the coordinates are transformed (shift and rotation) to local car coordinates.
In order to ensure more continuity on the trajectory (in addition to adding the last two point of the pass trajectory to the spline adjustment), the pass trajectory points are copied to the new trajectory. The rest of the points are calculated by evaluating the spline and transforming the output coordinates to not local coordinates. The speed change is decided on the behavior part of the code, but it is used in that part to increase/decrease speed on every trajectory points instead of doing it for the complete trajectory.
Each waypoint in the list contains [x,y,s,dx,dy] values. x and y are the waypoint's map coordinate position, the s value is the distance along the road to get to that waypoint in meters, the dx and dy values define the unit normal vector pointing outward of the highway loop.
The highway's waypoints loop around so the frenet s value, distance along the road, goes from 0 to 6945.554.
- Clone this repo.
- Make a build directory:
mkdir build && cd build - Compile:
cmake .. && make - Run it:
./path_planning.
Here is the data provided from the Simulator to the C++ Program
["x"] The car's x position in map coordinates
["y"] The car's y position in map coordinates
["s"] The car's s position in frenet coordinates
["d"] The car's d position in frenet coordinates
["yaw"] The car's yaw angle in the map
["speed"] The car's speed in MPH
//Note: Return the previous list but with processed points removed, can be a nice tool to show how far along the path has processed since last time.
["previous_path_x"] The previous list of x points previously given to the simulator
["previous_path_y"] The previous list of y points previously given to the simulator
["end_path_s"] The previous list's last point's frenet s value
["end_path_d"] The previous list's last point's frenet d value
["sensor_fusion"] A 2d vector of cars and then that car's [car's unique ID, car's x position in map coordinates, car's y position in map coordinates, car's x velocity in m/s, car's y velocity in m/s, car's s position in frenet coordinates, car's d position in frenet coordinates.
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The car uses a perfect controller and will visit every (x,y) point it recieves in the list every .02 seconds. The units for the (x,y) points are in meters and the spacing of the points determines the speed of the car. The vector going from a point to the next point in the list dictates the angle of the car. Acceleration both in the tangential and normal directions is measured along with the jerk, the rate of change of total Acceleration. The (x,y) point paths that the planner recieves should not have a total acceleration that goes over 10 m/s^2, also the jerk should not go over 50 m/s^3. (NOTE: As this is BETA, these requirements might change. Also currently jerk is over a .02 second interval, it would probably be better to average total acceleration over 1 second and measure jerk from that.
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There will be some latency between the simulator running and the path planner returning a path, with optimized code usually its not very long maybe just 1-3 time steps. During this delay the simulator will continue using points that it was last given, because of this its a good idea to store the last points you have used so you can have a smooth transition. previous_path_x, and previous_path_y can be helpful for this transition since they show the last points given to the simulator controller with the processed points already removed. You would either return a path that extends this previous path or make sure to create a new path that has a smooth transition with this last path.
A really helpful resource for doing this project and creating smooth trajectories was using http://kluge.in-chemnitz.de/opensource/spline/, the spline function is in a single hearder file is really easy to use.
- cmake >= 3.5
- All OSes: click here for installation instructions
- make >= 4.1
- Linux: make is installed by default on most Linux distros
- Mac: install Xcode command line tools to get make
- Windows: Click here for installation instructions
- gcc/g++ >= 5.4
- Linux: gcc / g++ is installed by default on most Linux distros
- Mac: same deal as make - [install Xcode command line tools]((https://developer.apple.com/xcode/features/)
- Windows: recommend using MinGW
- uWebSockets
- Run either
install-mac.shorinstall-ubuntu.sh. - If you install from source, checkout to commit
e94b6e1, i.e.git clone https://github.com/uWebSockets/uWebSockets cd uWebSockets git checkout e94b6e1
- Run either
We've purposefully kept editor configuration files out of this repo in order 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)
Please (do your best to) stick to Google's C++ style guide.
Note: regardless of the changes you make, your project must be buildable using cmake and make!
Help your fellow students!
We decided to create Makefiles with cmake to keep this project as platform agnostic as possible. Similarly, we omitted IDE profiles in order to ensure that 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 that 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. My expectation is that 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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