Self-Driving Car Engineer Nanodegree Program
The path planning project spans quite a large number of concepts shown in the classrooms. Disciplines like search, prediction and finite-state machine based behavior planning algorithms are all huge fields work on their own. With the concepts presented in the classroom being very interesting yet ambitious, discomfort raised in me realizing that they would all be covered in this project.
Luckily, a code walkthrough was presented by David Silver and Aaron Brown, that offered a very pragmatic approach to the problem. It was a little sad not to implement a clean, object-oriented piece of software that reflects the more scientific algorithms shown by the Mercedes-Benz lecturers, but I decided to follow David and Aaron's path for most of the way. The benefit-cost analysis just turned out too well for their approach. Especially the application of the spline function to follow the highway route as well as to realize lane changes returned quite impressive results concerning jerk minimization.
At some points though, I wanted or even had to leave their code walkthrough and implement my own ideas.
David and Aaron adapt the vehicles target speed to traffic ahead in a rather simple way, aiming at a fixed distance to the leading vehicle. Instead of this, I wrote down a couple premises that relate to real world driving practice and put up two formulas to deal with this question.
- Premises:
- Keep a safe distance to vehicle ahead (driving schools in Germany teach d_min = v [km/h] * 0.5 [h*m/km], which equals 1.8 seconds)
- Avoid decceleration of more than 3 m/s^2 (compare to rubric's total maximum of 10 m/s^2)
- Approach:
- Calculate the residual distance to the vehicle ahead
- Calculate the required time to deccelerate from own velocity to the velocity of the vehicle ahead
- Calculate how much distance own vehicle and vehicle ahead travels during that time
- Form a single equation v_max = (v_traffic,dist_traffic,min_dist_in_s,a_max)
- Permute the equation to calculate the v_min (considering the traffic behind you, needed to avoid cutting people on lane changes)
My handwritten notes deal with the physics and algebra of the problem:
The presented equations are then used to calculate the maximum or minimum velocities in each lane with respect to the vehicles ahead or behind the own vehicle. By comparing these speeds to the own velocities, changes to the adjacent lanes are considered as safe or unsafe.
The applied, very simple version of a behavior planner would execute a lane change if
- it is safe to change to that lane (v_min < v_ref < v_max for that lane)
- the maximum possible speed at that lane is higher than in the current lane
- a minimum time of 2 seconds was spent in the current lane (avoid hecticness)
Again, a handwritten sketch can serve for illustration. In the situation depicted, the vehicle has reached the maximum possible speed in the upper lane. As the bottom lane allows a higher speed (vehicle ahead is further and faster), the behavior planner would initiate the lane change.
You can download the Term3 Simulator which contains the Path Planning Project from the [releases tab (https://github.com/udacity/self-driving-car-sim/releases).
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.
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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