Self-Driving Car Engineer Nanodegree Program
- check!
- check!
Speed limit is 50 mph.
I made sure that the car tries to stay close to this speed by setting a maximum value of 49.5 mph.
src/main.cpp line 200:
const double MAX_SPEED = 49.5;Whenever the speed is slower that MAX_SPEED, set the reference speed value a bit higher until the maximum is reached.
In order to avoid collisions with cars in front, I set the reference speed to a lower value whenever the ego car is too close.
src/main.cpp line 352 to 359:
if(too_close)
{
ref_vel -= .224;
}
else if(ref_vel < MAX_SPEED)
{
ref_vel += .224;
}As shown in the point above, adding a small amount to the reference velocity at each time step, made it possible to to avoid to exceed max acceleration and jerk,
Collisions can happen in various ways.
-
The first possible source of collisions is to bump into the car in front in the same lane.
In order to avoid this, I loop over all the sensor fusion vector - which provides all other cars' attributes.
for (int i=0; i<sensor_fusion.size(); ++i)
I can detect if a car is driving in the same lane as the ego car by checking if the position of that car is within +/- 2 meters away from the center of the lane where the ego car is driving
// d is lateral displacement of car i from the yellow line at the center of the road float d = sensor_fusion[i][6]; double my_safe_lane = 2+4*lane; if (d < (my_safe_lane+2) && d > (my_safe_lane-2))
If a car is detected in the same lane, I check if it's driving too close to the ego car:
if((check_car_s > car_s) && ((check_car_s - car_s) < 30))
set a flag to true
too_close = true;which will allow the logic described above to slow down the ego car.
-
Another possible source of collisions is when the ego car is changing lane.
The basic logic for changing lane is to set the variable
laneto the desired integer.The trajectory will then be generated using the spline library.
// Turn to the center lane target_lane = 1; if (is_lane_safe(target_lane, sensor_fusion, car_s, prev_size)) { lane = target_lane; }
Before setting the target lane to the desired lane, we check if that lane in particular is safe.
bool is_lane_safe(int target_lane, const vector<vector<double>> &sensor_fusion, const double car_s, const int prev_size) { target_lane = 2+4*target_lane; for (int i=0; i<sensor_fusion.size(); ++i) { // if there is a car in the target_lane float d = sensor_fusion[i][6]; if (d < ((double)target_lane+2) && d > ((double)target_lane-2)) { double vx = sensor_fusion[i][3]; double vy = sensor_fusion[i][4]; double check_speed = sqrt(vx*vx + vy*vy); double check_car_s = sensor_fusion[i][5]; check_car_s += ((double)prev_size*.02*check_speed); // if the car in the target lane is too close to ego car (in front and behind) if (fabs(check_car_s - car_s) < 30) { return false; } } } return true; }
The function above looks at all the cars in the target lane and makes sure that there is enough room in front and behind the ego car.
As main logic, the car never tries to change lanes, a part from when there is a slower car in front. When this happens, a change to the left lane is preferred (since it tends to be faster).
If the ego car is on the most left lane, or most right lane, the only choice is to try to go to the center lane:
// Try to turn to the center lane
if ((lane == 0) || (lane == 2))
{
target_lane = 1;
if (is_lane_safe(target_lane, sensor_fusion, car_s, prev_size))
{
lane = target_lane;
}
}If the ego car is driving in the center lane, then we first try to turn left, if this is not possible, the car attempts to turn right.
else if (lane == 1)
{
// Try left
target_lane = 0;
if (is_lane_safe(target_lane, sensor_fusion, car_s, prev_size))
{
lane = target_lane;
}
else
{
// Try right
target_lane = 2;
if (is_lane_safe(target_lane, sensor_fusion, car_s, prev_size))
{
lane = target_lane;
}
}
}The way the algorithm works for defining the trajectory for lane change is:
- I define 2 vectors that will be used to generate a spline.
// Create a list of widely spaced (x,y) waypoints evenly spaced at 30m // Later we will interpolate these waypoints with a spline and fill it in with more points that control... vector<double> ptsx; vector<double> ptsy;
- From the previous path get the last 2 points and add them to these vectors:
ptsx.push_back(ref_x_prev); ptsx.push_back(ref_x); ptsy.push_back(ref_y_prev); ptsy.push_back(ref_y);
- Given the lane where I want the car to drive (it could be the same as where the ego car currently is), I add evenly 30m spaced points ahead of the starting reference
vector<double> next_wp0 = getXY(car_s+30, (2+4*lane), map_waypoints_s, map_waypoints_x, map_waypoints_y); vector<double> next_wp1 = getXY(car_s+60, (2+4*lane), map_waypoints_s, map_waypoints_x, map_waypoints_y); vector<double> next_wp2 = getXY(car_s+90, (2+4*lane), map_waypoints_s, map_waypoints_x, map_waypoints_y); ptsx.push_back(next_wp0[0]); ptsx.push_back(next_wp1[0]); ptsx.push_back(next_wp2[0]); ptsy.push_back(next_wp0[1]); ptsy.push_back(next_wp1[1]); ptsy.push_back(next_wp2[1]);
- These points will be used to generate a spline that can be used to generate all the points needed for the trajectory
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 10 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.
- 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
["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.
-
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.
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.shorinstall-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
- Run either
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./
A well written README file can enhance your project and portfolio. Develop your abilities to create professional README files by completing this free course.