CarND-Path-Planning-Project

The present repository is my solution to the Udacity Path Planning Project.

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Model

State of the car

We want to decide if the car needs to remain in its lane, change lane, or is currently changing lane so that we finish our current movement. We can keep track of this state with 2 variables:

• g_changing_lane will be true if we have decided to change lane and go back to false when our change of lane is complete,
• g_target_lane will represent which lane we are trying to follow, which is useful to generate consistent trajectories.

Definition of trajectory

The generation of trajetory is based on the following principles:

• At each step, we keep the last 10 time frames (keep_points variable) from previous calculated trajectory, which is equivalent to 0.2 sec.
• We create a spline that uses:
  • the last 3 points from the trajectory we kept in order to have a smooth transition with minimal jerk,
  • a point ahead of us defined in the frenet coordinates for convenience, at target_horizon, which is either 15m when we are staying in the same lane or 35m when we are changing lane in order to minimize jerk and have a smooth change of lane (values choosen experimentally)
  • another ahead of us is used on the spline generation mainly for display purposes related to the target trajectory.
  • an exception is made at the start when we have no previous trajectory and in which case we use the current car position as well as a point ahead and behind us.
  • the spline maps x, y coordinates in the local car referential which results in a horizontal trajectory and ensures y can be defined as a function of x which cannot be done when there are several y values for a certain x.
• The next trajectory points use the created spline s_xy and the target speed (target_speed) to have a prediction over a total of 50 time frames (1 second).

Speed target

We always try to go as fast as possible by accelerating until we get close to the 50mph speed limit. We evaluate if we need to slow down through the too_close value which let us know we are getting too close to a car in our current lane.

This is achieved with the following logic:

• we extract one by one all the cars from the sensor fusion,
• we verify if they are in our lane,
• if they are in our lane, we check if they are in front of us and close, ie within critical_distance (6m),
• we also check we are still within a reasonable distance at time_horizon (in 1s) so that we consider current speed of cars,
• if this is the case, we need to slow down which we track through too_close variable,
• in function of the too_close variable, we either decrease or increase our speed based on our target acceleration (constants::TARGET_ACC),

Note: the fact that the acceleration is calculated over 0.2s (ie 10 time frames) made us choose to keep 10 time frames (keep_points), is integrated in the calculation of the change of velocity, and ensures that we won't exceed max acceleration by changing the speed only every 10 steps. It is however also a limitation from the simulator that we needed to address.

Lane change

We want to try to change lane when a car is in front of us.

For that purpose, we first follow the same logic as the one we used for the speed target and detecting if a car is too close to us. However we want to anticipate it before we have to slow down so we check ahead of time, when we are within 30m (prepare_lane_change) of the car in front of us.

If we detected we want to change lane, we perform the following steps:

• we define a gap through a safety distance ahead and behind us (safety_lane_change_distance_back and safety_lane_change_distance_front) that we use to check if our adjacent lanes are free (left_lane_free and right_lane_free),
• we check if we are already on the side of the road to see which lanes could be potentially available,
• we extract one by one all the cars from the sensor fusion,
• we verify if they are in an adjacent lane
• we verify whether they are within our defined gap
• we perform the same evaluation at time_horizon (in 1s) so that we consider current speed of cars,
• if a lane is free, we change the state of the car g_changing_lane and the target lane g_target_lane which will be used for the definition of the spline and consequently, the trajectory of our car.

Note: if we are currently changing lane (g_changing_lane), we do not try to change lane again, which could lead to unstable trajectories.

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Original Readme

Here below is the original readme of the project.

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Simulator.

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).

Goals

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.

Details

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.

Tips

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.

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Dependencies

• 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.sh or install-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
    

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.

Project Instructions and Rubric

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

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. 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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