A Library of Lower Fidelity Dynamics Models (LFDMs) For On-Road Vehicle Dynamics Targeting Faster Than Real-Time Applications - First release

First release of the library of Low Fidelity Dynamic Models.

Key Features

1. High-Speed Performance: Models surpass real-time processing speeds. For instance, the 18 Degrees of Freedom (DOF) model achieves 2000x faster performance than real-time on standard CPUs, with an integration timestep of 1e-3 s.

2. GPU Optimization for Scalability: The GPU models are adept at parallel simulations of multiple vehicles. The 18 DOF GPU model, for example, can simulate 300,000 vehicles in real-time on an NVIDIA A100 GPU. Note: The GPU models are only available for Nvidia GPUs.

3. Python API: The SWIG-wrapped Python version maintains significant speed, being only 8 times slower than the C++ models, thereby offering Python's ease of use with C++ efficiency.

4. Advanced Analysis with Sundials: The CPU models support Forward Sensitivity Analysis (FSA) for select parameters. The use of a half-implicit integrator allows easy access to Jacobians of the system's RHS in relation to states and controls, beneficial for gradient-based Model Predictive Control (MPC) methods.

5. Comprehensive Vehicle Dynamics Simulation: Including models for the engine, powertrain, and torque converter, these simulations closely replicate actual vehicles. Users also have a choice between two semi-empirical TMeasy tire models, noted for their accuracy and performance at high vehicle speeds.

6. User-Friendly Configuration: Parameters for the models can be set dynamically at runtime through JSON files.

Scientific Software - Peer-reviewed - C++
Published by Huzaifg over 1 year ago