CarND-Path-Planning-Project

Self-Driving Car Engineer Nanodegree Program

Path Planning

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/tag/T3_v1.2).

To run the simulator on Mac/Linux, first make the binary file executable with the following command:

sudo chmod u+x {simulator_file_name}

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. The car should try to go as close as possible to the 50 MPH speed limit, which means passing slower traffic when possible. 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 10 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.

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
    

Implementation

The implementation includes

• Addition of car_control.cpp
• Modification of main.cpp

Let's see these file in detail now.

car_control.cpp Implementation

This file is responsible for handling Vehicle class two functions check_safe_distance and predict_vehicle_future_s.

1. Vehicle class This class handles data coming from sensor_fusion and has variables named as s, d, vx, vy, lane, speed. Depending on the d value of car and lane width which is 4 meters, its lane is calculated.

  if (d < 0) {
    lane = -1;
  }
  else if ((0 <= d) & (d < 4))
  {
    lane = 0;
  }
  else if ((0 <= 4) & (d < 8))
  {
    lane = 1;
  }
  else if ((0 <= 8) & (d < 12))
  {
    lane = 2;
  }

Also, the speed is calculated using vx and vy values.

speed = sqrt(vx * vx + vy * vy);

2. check_safe_distance function This function accepts ego car's s value and detected vehicle's s value and returns

   • true: if ego car is behind detected vehicle AND distance between them is more than the safe_distance
   • false: otherwise

3. predict_vehicle_future_s function This predicts the detected vehicle's s value in future. This is helpful, when the ego car is about to change the lane but there's a fast moving vehicle in the same lane where ego car will be shifting. Using this function, the ego car will predict that, if it switches the lane to that particular lane where a fast moving car is approaching from back, it will abort lane switch. Instead, it will slow down its speed to avoid collition to the car in front of her.

if (lane_change && left_lane_free) {
    lane -= 1;
    ref_vel -= 0.224 * 1.5;
}

main.cpp Implementations

In this file, we've taken 4 states for our path planning's state machine.

1. too_close: this checks if the car detected via sensor_fusion is too close (< 30m close)
2. left_lane_free, right_lane_free: if above condition holds true, it will check whether the left or right lane is free and will set the value accordingly
3. prepare_lane_change: based on above both conditions true, will prepare to lane change
4. lane_change: when done preparing lane change, lane will be changed

For speed control, we're creating 3 waypoints 30m aparts from each other. Using spline, we're traversing through these points to create our total 50 waypoints for car to move forward.

vector<double> next_wp0 = getXY(car_s + 30, (lane_width * lane + lane_width / 2), map_waypoints_s, map_waypoints_x, map_waypoints_y);
vector<double> next_wp1 = getXY(car_s + 60, (lane_width * lane + lane_width / 2), map_waypoints_s, map_waypoints_x, map_waypoints_y);
vector<double> next_wp2 = getXY(car_s + 90, (lane_width * lane + lane_width / 2), map_waypoints_s, map_waypoints_x, map_waypoints_y);

Valgrind

Here, I've taken care of memory leaks. Have validated the path_planning binary with valgrind tool and found that there's no memleaks possible with this code. See attached valgrind logs named valgrind_logs.txt.

==13585== LEAK SUMMARY:
==13585==    definitely lost: 0 bytes in 0 blocks
==13585==    indirectly lost: 0 bytes in 0 blocks
==13585==      possibly lost: 0 bytes in 0 blocks
==13585==    still reachable: 734,462 bytes in 219 blocks
==13585==         suppressed: 0 bytes in 0 blocks