Self-Driving Car Engineer Nanodegree Program

There are two main steps involved in driving the car on the highway. Firstly, in which lane to drive the car in, and secondly, if a certain lane is provide how to generate points so that the car follows the path.

This is mainly based on a finite state machine, who has only two states namely drive the same lane or change lane to left or change lane right. There has been no explicit prepare lane change state, Reason being that other cars in the simulator can handle their speed individually, and they can react well to Ego’s maneuvers. The only precaution is to check that there is enough path left for the car coming from behind in the new proposed lane, so that it can see Ego vehicle changing its lane. This avoids collision from behind which serves the purpose for the project.

Ego starts at 0 velocity in its own lane (Lane 1), and checks the path ahead over a distance LANE_HORIZON using the check_lane_fwd_behavior() function.If the distance is free from other cars, Ego picks up speed and reaches up to the reference velocity which is set to 49.60 MPH to completely avoid any speed violation.

I used the same cost function as taught in the lesson.This function calculate the costs of staying in the same lane or changing to left or right.

The cost function used here is

                    𝐽 = 1 − 𝑒−∆𝑑/∆𝑠
                    Δd is the lateral distance for changing a lane.
                    Δs is distance between Ego and a car front (or behind).

The state machine will first see the feasibility to move to lane 1 and then if desired move to lane 2. Because of this limitation, Δd will always be approximately 4m which is the lane length. Because of its fixed nature, Δd is set to 1 in the simulation.

In the check_cost() function, available distance in front and behind of Ego for the proposed lane is checked. If this distance is greater than LANE_HORIZON, ∆𝑠 will be set to a large number, indicating that there is enough room for the car to change lane; otherwise the actual distance ahead and back is used to calculate the cost. This cost is then compared later, with the cost of staying in the same lane by using the distance between Ego and its nearest car ahead. Based on the smallest cost, Ego either remains in the lane and adjusts speed or changes the lane.

Once the lane is decided, path points are generated using the spline algorithm discussed in the project video.The first two anchor points are from Ego’s old and current position. The next three points are drawn ahead of the car in the same or new lane based on the state machine. Using these anchors, spline algorithm is activated. Afterwards, over a distance of PATH_HORIZON, new future x-points are created using the car’s speed and simulation tick interval (20ms), and spline creates the corresponding y-coordinates. Using a combination of these new points and path points from previous iteration, a path is produced.

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).

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.

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.

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

["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.

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 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.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
    

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./