commonroad-vehicle-models 2.0.0

Implementation of vehicle models with varying abstraction levels ranging from kinematic single track model to a multi-body model.

Python Vehicle Models of CommonRoad

This package contains all vehicle models of the CommonRoad benchmarks.

We provide implementations of the vehicle dynamics, routines to convert initial states, and vehicle parameters.

Documentation

For a detailed explanation of the vehicle models, please have a look at the documentation.

Installation

To use vehicle models and parameters, run

pip install commonroad-vehicle-models

Code examples

For an extended simulation example demonstrating the advantages of more complicated models, we refer to our gitlab repository. A simple simulation example for using the single-track model in combination with an odeint solver would be

from scipy.integrate import odeint
import numpy

from vehiclemodels.init_ks import init_ks
from vehiclemodels.parameters_vehicle1 import parameters_vehicle1
from vehiclemodels.vehicle_dynamics_ks import vehicle_dynamics_ks

def func_KS(x, t, u, p):
    f = vehicle_dynamics_ks(x, u, p)
    return f

tStart = 0  # start time
tFinal = 1  # start time

# load vehicle parameters
p = parameters_vehicle1()

# initial state for simulation
delta0 = 0
vel0 = 15
Psi0 = 0
sy0 = 0
initialState = [0, sy0, delta0, vel0, Psi0]
x0_KS = init_ks(initialState)

t = numpy.arange(0, tFinal, 0.01)
u = [0, 5]
x = odeint(func_KS, x0_KS, t, args=(u, p))

Contribute

If you want to contribute new vehicle models, you can create a merge request in our repository, or contact via our forum.

Changelog

Compared to version 2019b the following features were added:

• kinematic single-track model with on-axle trailer
• vehicle parameter set for a semi-trailer truck (vehicle ID: 4)
• single-track drift model: nonlinear single-track model with Pacejka tire forces

Referencing

If you use CommonRoad, please cite

M. Althoff, M. Koschi, and S. Manzinger, ''CommonRoad: Composable Benchmarks for Motion Planning on Roads,'' in Proc. of the IEEE Intelligent Vehicles Symposium, 2017, pp. 719-726.