# OpenPilot Autonomous Driving Stack

A long while back, I played around with a comma.ai Panda OBD-II Dongle which is an inexpensive vehicle CAN bus interface designed to perform basic self-driving functionality using only an RGB camera and lightweight processor, both mounted behind the rear view mirror. My goal was to understand comma.ai’s open-source “OpenPilot” codebase both as an intro to a basic self-driving control stack and to eventually (hopefully) use the Panda dongle either with OpenPilot or custom software to do some simple autonomous control, ideally with the inclusion of imaging radar. I didn’t quite get that far due to shifting priorities, but I took some notes that I might as well post now that I’m looking back over them. I hope it helps someone somehow!

# OpenPilot

The OpenPilot project is an open-source autonomous driving platform designed for comma.ai’s products (Panda OBD-II Dongle and EON processor). It contains python code for doing everything from interpreting CAN messages for different car types to performing model predictive control using a combination of sensors. For a very simple but useful overview of the project, check out this article from an OpenPilot contributor.

Unfortunately, it is written specifically for the EON hardware/architecture and thus has dependencies which were not easily resolvable on an Ubuntu system (I also tried a Docker container but still couldn’t use it). While it may be possible to isolate certain functionality, we can also just use the project as a guide for implementing our own versions of specific modules. For example, in our Panda wiki there is information on where to find files related to CAN parsing for our platform.

This page will serve to document some of the higher-level functionality (car modelling, control, sensor fusion etc) as needed.

## Package Structure

The OpenPilot package has the following structure. At the top level are a number of different libraries on which the self-driving functionality, found in openpilot/selfdrive, depend on. These include eg the panda module for CAN communication, opendbc for DBC parsing and so on.

### Controls

Within the selfdrive subpackage are different subsubpackages handling different aspects of self driving functionality. Vehicle models and control algorithms which use these models are found in the controls package, with controlsd.py implementing high-level control using the rest of the controls library.

The main control loop is set to run in a thread at 100Hz. Let’s take a closer look at what it’s doing:

def controlsd_thread(gctx=None, rate=100, default_bias=0.):
gc.disable()

# start the loop
set_realtime_priority(3)

context = zmq.Context()
params = Params()

# Pub Sockets
live100 = messaging.pub_sock(context, service_list['live100'].port)
carstate = messaging.pub_sock(context, service_list['carState'].port)
carcontrol = messaging.pub_sock(context, service_list['carControl'].port)
livempc = messaging.pub_sock(context, service_list['liveMpc'].port)

is_metric = params.get("IsMetric") == "1"
passive = params.get("Passive") != "0"

# No sendcan if passive
if not passive:
sendcan = messaging.pub_sock(context, service_list['sendcan'].port)
else:
sendcan = None

# Sub sockets
poller = zmq.Poller()
thermal = messaging.sub_sock(context, service_list['thermal'].port, conflate=True, poller=poller)
health = messaging.sub_sock(context, service_list['health'].port, conflate=True, poller=poller)
cal = messaging.sub_sock(context, service_list['liveCalibration'].port, conflate=True, poller=poller)
driver_monitor = messaging.sub_sock(context, service_list['driverMonitoring'].port, conflate=True, poller=poller)
gps_location = messaging.sub_sock(context, service_list['gpsLocationExternal'].port, conflate=True, poller=poller)
logcan = messaging.sub_sock(context, service_list['can'].port)


First, it creates communication sockets using messaging.py which is a simple wrapper for ZeroMQ distributed messaging using Cap’n Proto for serialization of python objects (see eg car.capnp to understand how serialization is implemented for car-related data structures).

  CC = car.CarControl.new_message()
CI, CP = get_car(logcan, sendcan, 1.0 if passive else None)

if CI is None:
raise Exception("unsupported car")

# if stock camera is connected, then force passive behavior
if not CP.enableCamera:
passive = True
sendcan = None

if passive:
CP.safetyModel = car.CarParams.SafetyModels.noOutput

# Get FCW toggle from settings
fcw_enabled = params.get("IsFcwEnabled") == "1"
geofence = None


The serialization of a CarControl capnp structure is used to send car commands which are computed in the remainder of the thread loop. The car package is also used here to get a car information object through a generic interface defined by carhelpers.py. This return a car interface and car parameters objects which store physical parameters, settings etc.

  PL = Planner(CP, fcw_enabled)
LoC = LongControl(CP, CI.compute_gb)
VM = VehicleModel(CP)
LaC = LatControl(CP)
driver_status = DriverStatus()

if not passive:

# Write CarParams for radard and boardd safety mode
params.put("CarParams", CP.to_bytes())
params.put("LongitudinalControl", "1" if CP.openpilotLongitudinalControl else "0")


The car interface and parameter objects are then used to create and initialize car planners and controllers.

  state = State.disabled
soft_disable_timer = 0
v_cruise_kph = 255
v_cruise_kph_last = 0
overtemp = False
free_space = False
cal_status = Calibration.INVALID
cal_perc = 0
mismatch_counter = 0
low_battery = False

rk = Ratekeeper(rate, print_delay_threshold=2. / 1000)

# Read angle offset from previous drive, fallback to default
angle_offset = default_bias
calibration_params = params.get("CalibrationParams")
if calibration_params:
try:
angle_offset = calibration_params["angle_offset2"]
except (ValueError, KeyError):
pass

prof = Profiler(False)  # off by default


Here the initial state machine state is initialized along with other parameters before entering the main loop. The ratekeeper is defined in realtime.py along with other utilities for maintaining realtime loops and setting thread priority. The profile is a utility for printing times of checkpoints, defined in profiler.py.

  while True:
prof.checkpoint("Ratekeeper", ignore=True)

# Sample data and compute car events
CS, events, cal_status, cal_perc, overtemp, free_space, low_battery, mismatch_counter = data_sample(CI, CC, thermal, cal, health,
driver_monitor, gps_location, poller, cal_status, cal_perc, overtemp, free_space, low_battery, driver_status, geofence, state, mismatch_counter, params)
prof.checkpoint("Sample")

# Define longitudinal plan (MPC)
plan, plan_ts = calc_plan(CS, CP, VM, events, PL, LaC, LoC, v_cruise_kph, driver_status, geofence)
prof.checkpoint("Plan")


The main loop for the control thread first gets updated car data using data_sample which polls for sensor data and sets events (eg low battery, high temperature, etc) and then computes a plan using model predictive control (MPC) in calc_plan. This function uses the Planner class of planner.py, which gets updated by passing in longitudinal and lateral planners to update. As noted, only the longitudinal MPC is defined in the planner; the lateral MPC is defined in the lateral controller.

For both lat/long MPCs, the actual optimization uses cffi to interface python with the C++ Acado optimization toolkit which uses qpOASES as a QP solver. The MPC problems are defined in C in longitudinal_mpc.c and lateral_mpc.c; these are compiled against the optimization libraries and given python interfaces using cffi in longitudinal_mpc/libmpc_py.py and lateral_mpc/libmpc_py.py respectively.

The libmpc_py.py interface files for each planner provide the get_mpc function which returns an interface to the MPC variables and the init and run_mpc functions which are ultimately used by the python planners to solve the MPC problems.

    if not passive:
# update control state
state, soft_disable_timer, v_cruise_kph, v_cruise_kph_last = \
state_transition(CS, CP, state, events, soft_disable_timer, v_cruise_kph, AM)
prof.checkpoint("State transition")

# Compute actuators (runs PID loops and lateral MPC)
actuators, v_cruise_kph, driver_status, angle_offset = state_control(plan, CS, CP, state, events, v_cruise_kph,
v_cruise_kph_last, AM, rk, driver_status, PL, LaC, LoC, VM, angle_offset, passive, is_metric, cal_perc)
prof.checkpoint("State Control")

# Publish data
CC = data_send(PL.perception_state, plan, plan_ts, CS, CI, CP, VM, state, events, actuators, v_cruise_kph, rk, carstate, carcontrol,
live100, livempc, AM, driver_status, LaC, LoC, angle_offset, passive)
prof.checkpoint("Sent")

rk.keep_time()  # Run at 100Hz
prof.display()


In function state_control the lateral control and longitudinal control get updated. Since the longitudinal MPC was computed above, only its feedback look is updated here; the lateral control has both MPC and feedback loop updated at this point. The output of the lat/lon controllers are actuator commands.

Before sending actuator commands, the state machine state is updated in state_transition by checking for events triggered previously (in the data_sample or calc_plan functions).

## Sending Control Commands

Parsing CAN data is straightforward using the correct DBC file, but sending commands to eg set the steering angle is more involved. The point in the controller at which actuator commands are sent is here, using a car interface object and a capnp CarControl message loaded with computed commands.

### Car Interfaces

The car interface is returned from get_car which uses some nifty functionality defined in common/fingerprints.py to automatically return the correct car interface by matching current CAN data with candidates in the library of known vehicles.

Interfaces are different for different manufacturers; the library of interfaces is loaded here by iterating through the car package and importing interface.py for each manufacturer (eg for honda).

Every car interface has an apply function which takes as input a CarControl message structure containing commands. Every interface imports a CarController class specific to that manufacturer which implements parsing the CarControl message into interface-specific commands inside its update function.

Within the update, functions like create_steering_control, create_gas_command etc are called from the manufacturer-specific CAN interface, eg hondacan.py using a CANPacker . These CAN messages get appended to a list of messages and are finally sent here.

Note that create_steering_control passes along lkas_active, which is related to the Lane Keep Assist System which comes stock with the car; without LKAS enabled for the car, OpenPilot cannot control the steering. The variable lkas_active is set to enabled here unless steering has been disabled from the CarState; this is determined here.

### Sending our own controls

Let’s look at what it would take to send a steering command. We actually send steer torques, so to command a steering angle we’ll need to implement a feedback loop for torque. The steering command to apply is computed here in the car controller, clipped to min/max hex values:

    # *** compute control surfaces ***
BRAKE_MAX = 1024/4
if CS.CP.carFingerprint in (CAR.ACURA_ILX):
STEER_MAX = 0xF00
elif CS.CP.carFingerprint in (CAR.CRV, CAR.ACURA_RDX):
STEER_MAX = 0x3e8  # CR-V only uses 12-bits and requires a lower value (max value from energee)
else:
STEER_MAX = 0x1000

# steer torque is converted back to CAN reference (positive when steering right)
apply_gas = clip(actuators.gas, 0., 1.)
apply_brake = int(clip(self.brake_last * BRAKE_MAX, 0, BRAKE_MAX - 1))
apply_steer = int(clip(-actuators.steer * STEER_MAX, -STEER_MAX, STEER_MAX))


where the limits of the actuator apply_steer value are found in the capnp message definition:

  struct Actuators {
# range from 0.0 - 1.0
gas @0: Float32;
brake @1: Float32;
# range from -1.0 - 1.0
steer @2: Float32;
steerAngle @3: Float32;
}


In the case of our Acura ILX, the max is 0xF00 which is 3840. This corresponds to the STEERING_CONTROL DBC message format which includes min/max:

BO_ 228 STEERING_CONTROL: 5 ADAS
SG_ STEER_TORQUE : 7|16@0- (1,0) [-3840|3840] "" EPS
SG_ STEER_TORQUE_REQUEST : 23|1@0+ (1,0) [0|1] "" EPS
SG_ SET_ME_X00 : 31|8@0+ (1,0) [0|0] "" EPS
SG_ COUNTER : 37|2@0+ (1,0) [0|3] "" EPS
SG_ CHECKSUM : 35|4@0+ (1,0) [0|3] "" EPS


## Compiling OpenDBC outside OpenPilot

While OpenPilot has a lot of great functionality, we really just want to pull out the modules which read DBC files, interpret incoming CAN data and allow sending commands - all while being agnostic to the vehicle make/model.

The bulk of this functionality is in the can module, which relies on the cereal module for serialization. The can module also uses the opendbc module which contains all generated DBC files to be loaded. There is also dependency on the common module. These should be able to be isolated and built separately. I pulled out the can module and put the opendbc and cereal modules inside it, and pulled out the common module and placed it beside can.

Building these requires modifying Makefiles to replace phone libs with system libs (including capnproto and zmq). I modified this line in the can Makefile to use c++14:

CFLAGS = -std=gnu14 -g -fPIC -O2 $(WARN_FLAGS) CXXFLAGS = -std=c++14 -g -fPIC -O2$(WARN_FLAGS)


And also modified these lines to use the system install of zmq:

else ifeq ($(UNAME_M),x86_64) ZMQ_FLAGS = -I/usr/local/include/zmq ZMQ_LIBS = -L/usr/local/lib -l:libzmq.a  Compilation requires Clang and Clang++ so I installed sudo apt install clang-3.8 and created symlinks with sudo ln -s /usr/bin.clang-3.8 /usr/bin/clang and sudo ln -s /usr/bin.clang++-3.8 /usr/bin/clang++. The cereal Makefile got modifed here to specify the system install of capnp: CEREAL_CFLAGS = -I$/usr/local/include/capnp/


And same here:

else ifeq ($(UNAME_M),x86_64) CEREAL_CXXFLAGS = -I/usr/local/include/capnp ifeq ($(CEREAL_LIBS),)
CEREAL_LIBS = -L/usr/local/lib/ \
-L/usr/local/lib/ \
-l:libcapnp.a -l:libkj.a -l:libcapnp_c.a
endif


The end result when trying to compile can (which tries to compile cereal as a prereq) was tons of c++14 related errors, for some reason.