On the planner of OpenPilot

This post will walk through how OpenPilot plans in the longitudinal direction.

We can see the longitudinal plan is conducted by a Planner object. The key step for the longitudinal planner is: PL.update.

Let’s dive into the planner object:

In the planner object, the key function is update(), which outputs the plan message directly. Notice that the planner with and without model are different. Now let’s break down the update function:

If the planner is using the neural network model as one of the solution sources, the update (every planning step) should look like this:

First, we check out the official implementation from Openpilot:

Impact of the curve:

a speed smoother on the curve

Then it gets the lead vehicle information from radarState:

So, does it mean the vehicle is not following if the relevant distance is larger than 45 meters?

Now we calculate the speed for normal cruise control:

Below is how the two mpc controller works?

Now let’s look into how mpc1 and mpc2 set their current state and update the planning results?

Most importantly, it instantiates a mpc solver written in C++.

3. Then the mpc controller executes the update(pm,CS,lead) step. Here it takes the input of the Pub message, current car state, as well as the lead information.

The pm is to receive the results. The lead information is the most important input.

The mpc controller first set up its current state:

We see mpc controller initialize the simulation with:

Then it calculates mpc:

The regular mpc only takes the lead vehicle information.

The planner that incorporates model:

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