CarSim

The CarSim example provides basic Vehicle Signals based on a physical model simulation and feeds them to the databroker.

Architecture Overview

Description

CarSim is a simple physical model implementation, based on the kinematic bicycle model. It can be used as a mock implementation in development, testing and simulation scenarios.

It takes three external inputs:

• accelerator position
• brake position
• steering wheel angle

It propagates the physical model in time with a pre-defined step interval (10ms), recalculates the acceleration components, speed and position, and finally publishes the numeric values as standard VSS datapoints to the databroker.

To simulate the input from a human driver, the optional DriverSim component can be run in parallel. DriverSim toggles between two different types of driver style in a pre-defined time interval:

• Good driver - slowly accelerates and moves the steering angle accordingly and then slowly brakes.
• Bad driver - accelerates hard (full throttle), moves the steering wheel randomly in both directions, brakes hard.

Background

Each component can be replaced individually at runtime, e.g. by more advanced or real implementations. There is no need to adapt, recompile or redeploy any of the other applications.

This is important for decoupling and standardizing vehicle signals, to enable future vehicle application platforms to evolve. Loose coupling and standardizing of vehicle signal interfaces enable thirdparty applications to use signals in higher-level applications or value-add services.

Both implementors (imagine separate organizations), can agree to use the Vehicle Signal Specification to decouple the development lifecycle for vehicle applications and vehicle services. This decoupling approach reduces the time and effort for proprietary design specifications processes tremendously.

Vehicle Signals

The CarSim application reads the following input signals from the databroker:

Input

“Human” driver input	Python variable	VSS subscription path	VSS datatype
Brake pedal position	DP_BRAKE_POS	Vehicle.Chassis.Brake.PedalPosition	uint8
Accelerator pedal position	DP_ACCELR_POS	Vehicle.Chassis.Accelerator.PedalPosition	uint8
Steering wheel angle	DP_STEER_ANGLE	Vehicle.Chassis.SteeringWheel.Angle	int16

Note: You can either provide these signals externally on your own, or use DriverSim to simulate them.

Output

The CarSim application feeds the following output signals to the databroker:

Python variable	VSS feeding path	VSS datatype
DP_SPEED	Vehicle.Speed	float
DP_ACCEL_LAT	Vehicle.Acceleration.Lateral	float
DP_ACCEL_LONG	Vehicle.Acceleration.Longitudinal	float
DP_ACCEL_VERT	Vehicle.Acceleration.Vertical	float

Getting started

1. Start up Leda and wait for the runtime to be available. For other deployment options, see Getting Started

   docker run -it ghcr.io/eclipse-leda/leda-distro/leda-quickstart-x86
   

2. Deploy the CarSim container:

   kanto-cm create --name carsim --e=DATABROKER_ADDRESS=databroker:55555 --hosts="databroker:container_databroker-host" ghcr.io/eclipse-leda/leda-example-applications/leda-example-carsim:v0.0.1
   kanto-cm start --name carsim
   

3. Deploy DriverSim container:

   kanto-cm create --name driversim --e=DATABROKER_ADDRESS=databroker:55555 --hosts="databroker:container_databroker-host" ghcr.io/eclipse-leda/leda-example-applications/leda-example-driversim:v0.0.1
   kanto-cm start --name driversim
   

4. Use kantui or kanto-cm logs --name <carsim|driversim> to view log files:

   

   

5. Use the databroker-cli to subscribe to the following VSS signals.

   Note: Separate multiple signals by commas. Use <TAB> to auto-complete signal names. See QUERY.md for the query syntax.

   $ databroker-cli
   connect localhost:30555
   subscribe
       SELECT
        Vehicle.Speed,
        Vehicle.Acceleration.Vertical,
        Vehicle.Acceleration.Longitudinal,
        Vehicle.Acceleration.Lateral
       WHERE
        Vehicle.Speed > 50
   

   

6. Happy hacking!

   Need ideas? How about implementing your own driver simulation and integrate with existing open source simulation technologies from Autonomous Driving?

References

• Sources: https://github.com/eclipse-leda/leda-example-applications/tree/main/kuksa-carsim
• Kinetic Bicycle Model: https://thef1clan.com/2020/09/21/vehicle-dynamics-the-kinematic-bicycle-model/
• Kuksa.VAL: https://github.com/eclipse/kuksa.val/tree/master/kuksa_databroker
• COVESA Vehicle Signal Specification: https://github.com/COVESA/vehicle_signal_specification