Path Planing - Udacity Self-Driving Car Nanodegree project
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Self-Driving Car Engineer Nanodegree Program

Implementation for windows by Jordi Tudela

Original Repository


Model Documentation


The objective of this project is to develop a model that, given a comprensively parsed set of sensor data, it is capable of driving a car through a populated 3-lane highway without braking traffic regulation (ie speed limit) nor causing any major disconfort to its occupants (ie no large acceleration or jerk).

  • Follow lane
  • Prepare to change lane
  • Change lane


Follow Lane

Follow lane is the main movement function and its goal is to mantain the car in its current lane at the maximum speed possible. That speed will tend toward the maximum speed allowed in the highway if there is no car directly in front of it but, if there is one, will adapt its speed to avoid a collision (the speed will be proportional to the gap between the cars with a maximum value equal to the velocity of the car in front).

Prepare to change lane

This state will trigger when there is a car in front. It will still be using the same movement function as Follow Lane as it was more convenient to code both movements in the same function (in both cases, the car must follow the current lane) but it will acctively look for a viable gap in the immediate side lanes.

If it finds a lane qhere there are no cars close behing and there is enough free space in front, it will initiate a lane change.

Change Lane

The objective of this state is perfrom a change lane manouver. Still being bound by the velocity of the car in front of the original lane, it will start to lean towards the target lane. Note: if a car on the new lane comming from behing gets to close, the car will revert its manouver and get back to the original lane to avoid a possible collision.

Path Smoothing

To avoid an excessive noisy path, I am using a weighted average between the previously computed path and the path computed at the current step using a ratio dependent on a cubic equation. This ratio has been manually tuned to give more importance to the previously computed path for points close to the car (less noise) and more importante to the new path for points far away from the car.

Additional Information

Every implemented instruction has been thoroughly commented in the source code



Simulator. You can download the Term3 Simulator BETA which contains the Path Planning Project from the releases tab.

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 50 m/s^3.

The map of the highway is in data/highway_map.txt

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.

Basic Build Instructions

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

Here is the data provided from the Simulator to the C++ Program

Main car's localization Data (No Noise)

["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

Previous path data given to the Planner

//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

Previous path's end s and d values

["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 Data, a list of all other car's attributes on the same side of the road. (No Noise)

["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.


  1. 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.

  2. 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, the spline function is in a single hearder file is really easy to use.


Editor Settings

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)

Code Style

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