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Self Driving Car Engineer Project 10 - Model Predictive Control

Benjamin Söllner, 13 Oct 2017

Fun Project Header Image

Screenshot of Car Driving Across Predicted Path

This C++ project of the Udacity Self Driving Car Engineer Nanodegree implements a Predictive Controller to steer a vehicle on a given track. The track information as well as the control information (throttle + steering) are exchanged via a websocket interface with the Udacity Self Driving Car Simulator. The corresponding project page can be found here.

The predictive controller calculates a polynomial through the waypoints that are closest to the vehicle and then carculates cross-track-error and bearing error for the current vehicle position as well as for any vehicle position that is on the predicted trajectory of the vehicle given a set of actuator inputs along the next N timesteps of duration dt.

The predictive controller then aims to minimize the cross-track-error, the bearing error as well as the magnitude of the steering inputs across all timesteps N.

The video thumbnail

(note that the latency captured in the video is significantly larger than without recording turned on, hence there is some oversteering)


The Model - State, Actuators and Update Equations

The actuators are represented as delta (steering) and a (throttle). The vehicle state at any predicted time is represented as follows:

  • x ... the x-position current time
  • y ... the y-position
  • psi ... the bearing
  • v ... the velocity along the x axis
  • cte ... the cross-track-error orthogonal to the polygon drawn by the waypoints
  • epsi ... the bearing error in relation to the tangent applied to the polygon of waypoints.

All coordinates are in relation to the vehicle coordinate system at the current time.

The actuators for the motion model are:

  • delta ... the steering
  • a ... the throttle

From the current vehicle state at timestep t0, the state at the next timestep t1 (with t1-t0=dt) can be calculated as follows:

x[t1] = x[t0] + v[t0] * cos(psi[t0]) * dt
y[t1] = y[t0] + v[t0] * sin(psi[t]) * dt
psi[t1] = psi[t0] + v[t0] / Lf * delta[t] * dt
v[t1] = v[t0] + a[t0] * dt
cte[t1] = f(x[t0]) - y[t0] + v[t0] * sin(epsi[t]) * dt
// ... with f(x)=ax^3+bx^2+c being the polygon representing the waypoint path
epsi[t1] = psi[t0] - psides[t0] + v[t0] * delta[t0] / Lf * dt
// ... with psides[t0] being the "designated bearing" at point t0, calculated
//     by atan(c) with c from f(x)=ax^3+bx^2+c.

The vehicle and motion model is then applied in an ipopt-Solver.

  • For the motion model, each constraint g(t1)=h(t0) is applied as a constraint g(t1)-h(t0) that is optimized towards zero (upper and lower bound) thereby pushing the vehicle to it's optimal state on its predicted trajectory
  • For the vehicle model (inital state), each state g(t1)=s is applied as a constraint g(t1) that is optimized towards s (upper and lower bound) thereby asserting that the optimization indeed accounts for the vehicle starting at the state it is currently at
  • For the actuators, meaningful upper and lower bounds are chosen (-0.436 .. 0.436 for delta, -1.0 .. 1.0 for a) so that the actuators stay within physical possible limits
  • The cost is not limited by upper and lower limits.

However, the cost is still mimized by the optimizer, and the following values are summed up into the cost:

  • all squared ctes across all N points, with an additional cte_penalty factor of 5 each
  • all squared epsis across all N points
  • all squared differences to the reference velocity (v-v_ref) across all N points
  • all steering inputs delta across all N points with an additional steering_penalty factor of 2000 each
  • all throttle inputs a across all N points
  • all squared differences of steering inputs delta[t1]-delta[t0] between subsequent steering actuations (N-1 data points)
  • all squared differences of steering inputs a[t1]-a[t0] between subsequent throttle actuations (N-1 data points)

By constraining the ipopt optimizer like described above, and furthermore minimizing the cost, the right set of actuations and resulting trajectory can be found at each timestep.

Various Tests of Timestep Length and Elapsed Duration (N & dt)

N is the amount of steps predicted by the predictive controller and dt is the time distance between those steps. Various values for N and dt have been explored. N stayed in the ballpark of 10-30 and dt in the ballpark of some_factor * anticipated_latency. N=15 and dt=0.15 was found to be working quite good.

dt too low (N=15, dt=0.05)

If dt is too low, the car drifts off the controlled trajectory, similarly than a too low D-coefficienct in a PID controller. This can eventually lead to fatal behaviour.

Screenshot with dt Too Low

N too high, dt too low (N=25, dt=25)

The previous behaviour can be somewhat counteracted by increasing N. However, increasing N increases computation time and thereby latency, ultimately leading to sluggish and unprecise control behaviour. Notice how in the screenshot below, the car only actuates after the first part of the predicted trajectory has already passed.

Screenshot with N Too High

dt too high (N=20, dt=0.5)

If dt is too high, especially in a case where it involves a predicted trajectory with too many curves, the optimization becomes unstable and the predicted path sort-of wiggely. My guess is that the second-order polynomial simply is not sophisticated enough any more to mimic the subsequent road turns:

Screenshot with dt Too High

This can lead to the car oversteering and ultimately leaving the track:

Screenshot with dt Too High - With Car Off Track

Choosing the Optimal N & dt

I chose N=15 and dt=0.15.

  • Lower dt is generally better (finer resolution) but must be counteracted by a higher N in order to result in a smooth controlled path
  • Too high N led to higher computational load and therefore latency, letting the car correct too late and leading to a shaky, "seasickening" car
  • Overall, a good trade-off between a large "horizon" N*dt and the computational effort must be found. A large horizon leads to more precise control of the car and avoids sharp curves, thereby increasing the possible reference velocity.

Polynomial Fitting and MPC Preprocessing

Polynomial fitting was accomplished by transforming the waypoint coordinates into the vehicle coordinate system (see main.cpp):

// Convert waypoints from global to car coordinate system
// Eigen::VectorXd representation needed for MPC
Eigen::VectorXd wayptsx_car(wayptsx.size());
Eigen::VectorXd wayptsy_car(wayptsy.size());
// vector<double> representation needed for output of polynom to simulator (later)
vector<double> wayptsx_car_v(wayptsx.size());
vector<double> wayptsy_car_v(wayptsx.size());
for (unsigned int i = 0; i < wayptsx.size(); i++) {
    double x = (wayptsx[i]-px) * cos(psi) + (wayptsy[i]-py) * sin(psi);
    double y = -(wayptsx[i]-px) * sin(psi) + (wayptsy[i]-py) * cos(psi);
    wayptsx_car[i] = x;
    wayptsx_car_v[i] = x;
    wayptsy_car[i] = y;
    wayptsy_car_v[i] = y;

... and then using the polyfit(...) function to fit a polynomial. The polynomial was used in the predictive controller to calculate the errors cte and epsi.

// Fit polynomial through waypoints
const int poly_order = 2;
Eigen::VectorXd coeff = polyfit(wayptsx_car, wayptsy_car, poly_order);

// Prepare state vector, for cte and epsi use polynome
// we just created
Eigen::VectorXd state(6);
// x,y - car is at center of its own coordinate system in
//       initial state
state[0] = 0.0;
state[1] = 0.0;
// psi - car points straight forward
state[2] = 0.0;
// v - speed, m/s
state[3] = v;
// cte - is simply f(0.0)
state[4] = polyeval(coeff, 0.0);
// epsi - angle btw. f(x) & x-axis at x=0 --> arctan(f'(0))
//        f=ax^2+bx+c --> f'(x)=2ax+b --> f'(0)=b
state[5] = -atan(coeff[1]);

Various other steps have been applied as preprocessing step:

  • Accounting for latency: see next section
  • Calculating reference velocity: depending on whether the current section of the road (i.e. the current fitted polynomial) was curved or straight the reference velocity was set to "high" or "low". Also, if the car "just started" (i.e., the current velocity was very low), the reference velocity is set higher in order to speed up faster.
// calculate radius of curvature of polynomial f(x) at x=0
double radius;
// catch division by zero
if (fabs(coeff[2]) > 0.0001) {
    // R(x) = ((1 + (dy/dx)^2)^1.5) / abs(d^2y/dx^2)
    //      = ((1 + f'(x)^2)^1.5) / abs(f''(x))
    //      = ((1 + (3ax^2+2bx+c)^2)^1.5 / abs(6ax+2b)
    // R(0) = ((1 + c^2)^1.5 / abs(2b)
    radius = pow(1.0+pow(coeff[1], 2), 1.5) / fabs(2.*coeff[2]);
} else {
    radius = 1000.;

// Set reference speed depending on radius
double v_ref; // in mph. MPC will convert to m/s2
if (v_mph < 60.) {
    // at beginning, speed up
    v_ref = 120;
} else {
    if (radius < 80) {
        // in a turn, slow down
        v_ref = 60;
    } else {
        // in a straight road, accelerate!
        v_ref = 90;
v_ref *= mph_to_mps;

Accounting for Latency

The latency was assumed to be slightly higher than the artificial latency of 0.1ms. Therefore, we assume the initial state of the car to be ~0.15ms ahead of its current state. We simply update the state according to our motion model. As a simplification, we assume the throttle to be equal to the acceleration (see main.cpp):

// Take into account latency
const double latency = 0.15 ; // ~150 ms
if (latency > 0) {
    px = px + v * cos(psi) * latency;
    py = py + v * sin(psi) * latency;
    psi = psi - v * steering_value / mpc.Lf * latency;
    v = v + throttle * latency;



Development Environment

I used a combination of Windows 10 Home + Docker (with Boot2Docker in Virtual Box) running GNU Build tools + Netbeans with Remote Debugging.

If this is the setup you want to use, there are some ressources to get started in the ide_profiles/netbeans-win10-docker folder. The docker container set up there even allows for X11 forwarding of graphics produced by matplotlib!


  1. Clone this repo.
  2. Make a build directory: mkdir build && cd build
  3. Compile: cmake .. && make
  4. Run it: ./mpc.

Submitted Files

  •, readme.html: you are reading it! :)
  • src/MPC.cpp: Controller logic
  • src/main.cpp: Boilerplate code and code for websocket communication as well as initializing of controller + preprocessing of data
  • ide_profiles/netbeans-win10-docker: some ressources to get started with a development environment based on Windows 10 Home + Docker (with Boot2Docker in Virtual Box) running GNU Build tools + Netbeans with Remote Debugging. Even includes X11 forwarding of graphics produced by matplotlib to Xming on Windows or similar.


Project 10 "Model Predictive Control" of Udacity's "Self Driving Car Engineer" Nanodegree






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