/CarND-Path-Planning-Project-P11

Path planner that is able to navigate a car safely around traffic on a virtual highway. Udacity Self-driving car nanodegree - Project 11

Primary LanguageC++

Motion Path Planning

Highway Driving

Self-Driving Car Engineer Nanodegree Program

By: Eqbal Eki

Overview

The goal of this project is to implement a path planning system which can safely navigate around a virtual highway with other traffic that is driving +-10 MPH of the 50 MPH speed limit.

Path planning is an interesting problem. In essence, it’s the process of finding a safe, comfortable and efficient (in that order) path through a set of dynamic maneuverable objects to reach our goal.

Usually, if we want to find a route between a source and a destination, and we want to find the fastest / shortest path, there are tons of algorithms to do this: (ex: Dijkstra’s, Breadth First Search, A*, etc…).

However in a road scene, whether it be a highway or urban roads, one of the most important difference is that the scene is not static, so our planning needs to be a dynamic one too.

Note we need to track the other objects with Sensor Fusion(dynamic such as other cars, pedestrians, bikes, etc…., and static such as trees, curbs, lamp posts).

The planning algorithm makes use of States and Jerk Minimized Trajectories in Frenet space.

Data

We should design a path planner for a ~6.5km long track goes in a circle and is filled by a random vehicles. Each vehicle travels at a random speed and avoids collisions with slower traffic by reducing speed or changing lanes.

Provided are waypoints along the track as well as synthetic localization and sensor fusion data sent from the simulator

In ./data folder we can find highway_map.csv file which includes data in X/Y format world space coordinates as well as Frenet S/D.

Sensor fusion data contains position and velocity information of the twelve closest vehicles in travel direction.

Check out the plot below which represent data in X/Y coordinates taken from highway_map.csv:

Structure

  • Planner class which encapsulates instances of BehaviorPlanner and PathPlanner.

  • BehaviorPlanner class which works with Planner to decode the WebSocket inputs to collect the current path plan, composed of a sequence of waypoints in global Cartesian coordinates (x, y).

  • Car class which has the current car State, composed of global Cartesian coordinates (x, y), orientation o, Frenet coordinates (s, d), speed v and current lane index.

  • Obstacles class that containing the Cartesian and Frenet coordinates, speeds and lane index of all vehicles on the same side of the road as the car.

  • HighwayMap class is used to keep track of all vehicles along the highway.

Notes

  • Updated path plans are sent back to the simulator through the WebSocket interface.

  • Because the Path Planner must react quickly to changing road conditions, the plan covers a period of only 0.3s.

  • Each turn the first 0.1s worth of waypoints from the current path plan is kept, and a new plan is constructed from the last kept waypoint onwards.

  • The first step of the planning process is to update Car and Obstacles objects to reflect expected conditions 0.1s into the future.

  • The BehaviorPlanner then updates its internal Finite State Machine (FSM) according to predicted readings and its own rules (illustrated in FSM section)

  • The car uses a perfect controller and will visit every (x,y) point it receives in a list every .02 seconds.

  • In reality dealing with the entire world at once is not practical. Instead, I am generating a local space from the closest 30 waypoints.

System Architecture & Flow

This project implements a Planner (composed of a Behavior Planner and a Path Planner) to create smooth, safe paths for an autonomous car to drive along.

It communicates with Udacity's simulator through a WebSocket interface, receiving as input:

  • Car state
  • Obstacle data
  • Current path plan

And sending back a new sequence of waypoints describing the updated path plan. The diagram below summarizes the system architecture:

FSM

The system has the following states:

  • START state, which determines the initial lane and the switches into the CRUISING state. In this state the car moves at the reference speed of 20m/s (close to 44MPH); it also tries to keep the car on the middle lane, from where it can easily move to avoid slower cars. If it finds a slower car ahead, it initially switches to the TAILING state.

  • TAILING state where it will try to pair its speed to the cars, while watching for an opportunity to change lanes. When this comes the FSM selects a destination lane and switches to CHANGING_LANES state, returning to CRUISING state once the movement is complete.

Once the current behavior (composed of a reference speed v and a polynomial route roughly indicating whether to keep or change lanes) is determined, it's dispatched along the current State to the PathPlanner.

I used the CppAD interface to Ipopt to compute a sequence of waypoints approaching the route at the reference speed, while respecting acceleration limits.

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.

Dependencies

  • cmake >= 3.5
  • All OSes: click here for installation instructions
  • make >= 4.1
  • gcc/g++ >= 5.4
  • 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
  • Ipopt
    • Mac: brew install ipopt --with-openblas
    • Linux
      • You will need a version of Ipopt 3.12.1 or higher. The version available through apt-get is 3.11.x. If you can get that version to work great but if not there's a script install_ipopt.sh that will install Ipopt. You just need to download the source from the Ipopt releases page or the Github releases page.
      • Then call install_ipopt.sh with the source directory as the first argument, ex: bash install_ipopt.sh Ipopt-3.12.1.
    • Windows: TODO. If you can use the Linux subsystem and follow the Linux instructions.
  • CppAD
    • Mac: brew install cppad
    • Linux sudo apt-get install cppad or equivalent.
    • Windows: TODO. If you can use the Linux subsystem and follow the Linux instructions.
  • Eigen. This is already part of the repo so you shouldn't have to worry about it.
  • Simulator. Find the latest version here.

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./