CarND-Path-Planning-Project

Self-Driving Car Engineer Nanodegree Program. Term 3. Project 1

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 the goal is to safely navigate around a virtual highway with other traffic that is driving +-10 MPH of the 50 MPH speed limit. I was 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

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

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

["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

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.
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.

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
```

Reflections

Path planning

Path represents a set of points, which should be executed by the simulator with a time difference 0.02 sec.
Normally the code sends 100 points of the planned path (which corresponds to 2 seconds of movement).
Simulator uses the received path to move forward and send back the rest of unused path points to the code. The code uses all the points in the new path and calculate new one so the length of the path points is 100.

There might be a case of unexpected lane shift by the vehicle ahead, where emergency breaking is required.
In this situation car can't slow down fast enough to avoid collision. To manage this situation, the code uses just 10 path points, received from the simulator, and re-calculate the rest from scratch. It makes possible emergency breaking.

The car moves straight forward with the maximum allowed speed until there is a car ahead (or car which is shifting to the current lane unexpectedly).
At this point of time car considers to change the lane. Currently, the car moves to the first available line, where there are no vehicles 10 meters behind and 40 meters ahead.

To realize a shift, I'm using a spline, which should transfer a car from the current lane into target lane in 4 seconds.
As a major imrovements in a next version, behavior planner, which estimates the cost of all possible shifts, should be implemented.
Next reasonable improvement might be generating a set of splines for the shift to choose the optimal time and trajectory of the spline.

I've mentioned that I'm considering other vehicles in the path planning. I'm storing last 2 states of sensor fusion information, sent by the simulator and track vehicle's velocity and acceleration in s and d axis.
This let me plan the state of the vehicle in the future as well as detect unexpected lane shift, which might cause a collision.

I'm using Frenet coordinates to plan the entire path. That requires the transformation from Frenet to Cartesian coordinates very accurate.
Instead of using the given getXY function, which linearly interpolate (x,y) values given (s,d), I'm using splines, cloned from http://github.com/ttk592/spline.
This is a one .h file lybrary with the straightforward interface. Besides creating splines, derivatives of first and second orders are available.

It worth mentioning that s not always accurately represent distance, driven by the car. This is especially noticable on the curve.
To avoid violation of the speed limit, I estimate velocity as a derivative:
dx/dt = dx/ds * ds/dt; dy/dt = dy/ds * ds/dt; v = ds/dt * sqrt((dx/ds)^2 + (dy/ds)^2)

I would like to thank Udacity and Mercedes Benz for creating this beautiful project and lesson materials for it as well as Self-Driving Car Nanodegree students for very interesting and smart thoughts and ideas.

olegleyz/SDCND-T3P1-PathPlanning