Self-Driving Car Engineer Nanodegree Program
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.
PathPlanner in action:
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.
In order to make the code of the implementation concise and easy to understand, the path planner is implemented in a single class called PathPlanner. The PathPlanner will generate/update the trajectory in each iteration and send it to the simulator. The trajectory is chosen based on a set of cost functions and the action with the lowest cost is executed. In the next paragraph each step of the path generation will be explained in more detail.
- Generating a smooth trajectory that is following the current lane.
Because the simulator represents a perfect controller which will fallow exactly all the points sent to it, special attention needs to the paid to the smooth transition between points generated by the PathPlanner. A first approach would be to used the available map waypoints; however, they are too sparse and can not be used directly for the trajectory. In order to overcome this limitation, a spline interpolation between the map points was used. We create a set of anchor waypoints in a fixed distance from the vehicle: 1m behind and 50,70,90m ahead. Then these anchor waypoints are transformed to the vehicle coordinate frame and a spline interpolation is generated. In order to keep the desired vehicle velocity, we need to space the trajectory points accordingly. If we have a fixed the number of points in the trajectory, we can use the fact that the simulator is running at 50fps to calculate how to space the points to match the desired velocity.
- Keeping a safe distance from vehicles ahead.
In each iteration, the simulator is providing sensor fusion data with the positions and velocities of all nearby vehicles. This information is used to adjust the desired velocity which will not cause collision with the upcoming vehicle.
- Lane changing.
The lane change action can be separated into two parts. First, check weather a lane change is possible at all by looking at other vehicle's positions on adjacent lanes. If a lane change is not possible we will exclude it from the possible actions. On the other hand, if a lane change is possible, then we can use the spline interpolation to create a smooth trajectory to the desired lane.
- Behavior planing.
Usually, at each time instance the vehicle is able to preform several actions for example, keep the current lane, perform a lane change etc. In order to represent these actions we have decided to use a state machine with a single state called KL or keep the current lane. All other actions represent temporal transitions into other lane keeping states. For example if the vehicle was in the middle lane and we decide to change to the right lane the maneuver will represent a temporal action and after the lane change we will continue keeping the current (right) lane. The possible actions are LCL, LCR, DLCL, DLCR - lane change left and right, and double lane change. The double lane change was added because the vehicle will keep the current lane indefinitely if it has a lower cost than a lane change, but perhaps, if we perform 2 consecutive lane changes we will get a lower cost and we will be able to pass the traffic.
- Cost generation.
As mentioned above the vehicle can perform several actions in a given time instance. However, these task are not equally ranked, and some actions are better than others. As our goal is to overtake the oncoming traffic each actions will be evaluated using a cost function consisting of 3 parts.
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Quadratic cost based on the ahead vehicle velocity - prefers to go to a lane with higher velocity
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Difference between current and a possible lane - prefers to stay at current lane
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Quadratic cost based on the distance to the vehicle ahead - prefers to go to lane with a wider gap
After evaluating the costs for each action the PathPlanner will generate a new trajectory based on the action with the lowest cost.
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).
- 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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