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We present a Vehicle Model (VM) that has 17 degrees of freedom and includes nonlinear tire and powertrain subsystems. Implemented as a relatively small piece of C++ code, the model runs vehicle dynamics 2000 times faster than real time at a simulation time step of $1 \times 10^{-3}, \text {s}$ on a single core of a commodity CPU. When executed on the GPU, one can perform 300000 vehicle simulations in real-time. These properties make the model a good candidate for reinforcement learning, model predictive control, model predictive path integral control, path planning, state estimation, and traffic simulation tasks. The model is expressive in that it can capture the dynamics of vastly different vehicles. This is demonstrated by first calibrating the model to mimic the dynamics of a 1/ $6^{th}$ scale vehicle called the Autonomy Research Testbed (ART) vehicle, which has a mass of approximately 5.8 kg. Subsequently, the model is calibrated to mimic the dynamics of a heavy-duty High Mobility Multipurpose Wheeled Vehicle (HMMWV), which has a mass of 2097 kg. The Bayesian calibration process discussed can $(i)$ handle real-life measurement noise, and $(ii)$ provide model parameter probability distributions. The vehicle model, which is open source and freely available in a public repository, can also be imported into Python owing to SWIG wrapping.

X-in-the-loop (XIL) technologies are receiving increased attention in modern automotive development processes. In particular, collaborative experiments, such as XIL tools, have efficient applications in the design of multi-actuated, electric, and automated vehicles. The presented paper introduces results of such a collaborative study for XIL, which focused on the feasibility of coordinated real-time simulations for the control of vehicle dynamics systems. The outcomes are based on extensive co-simulation tests performed using remote connections among different geographical locations; the connections were between Germany, on one side, and USA, South Africa, and The Netherlands, from the other side. The performed study allowed formulating requirements for further shared and distributed XIL-experiments for functional validation of automotive control systems.

Localization and navigation not only serve to provide positioning and route guidance information for users, but also are important inputs for vehicle control. This paper investigates the possibility of using odometry to estimate the position and orientation of a vehicle with a wheel individual steering system in omnidirectional parking maneuvers. Vehicle models and sensors have been identified for this application. Several odometry versions are designed using a modular approach, which was developed in this paper to help users to design state estimators. Different odometry versions have been implemented and validated both in the simulation environment and in real driving tests. The evaluated results show that the versions using more models and using state variables in models provide both more accurate and more robust estimation.