The latest versions of the Matlab and Simulink modelling tools from MathWorkshave added support of artificial intelligence, deep learning and power system modelling for automotive designs.
Release 2019b also includes support of robotics, new training resources for event-based modeling and updates and bug fixes across the MATLAB and Simulink product families.
The Powertrain Blockset provides the ability to generate a deep learning SI engine model for algorithm design and performance, fuel economy, and emissions analysis. It also includes HEV P0, P1, P3, and P4 Reference Applications, fully assembled models for HIL testing, tradeoff analysis, and control parameter optimization of hybrid electric vehicles.
An Automated Driving Toolbox supports 3D simulation, including the ability to develop, test, and verify driving algorithms in a 3D environment, and a block that enables users to generate the velocity profile of a driving patch given kinematic constraints.Sensor Fusion and Tracking Toolbox: Ability to perform track-to-track fusion and architect decentralized tracking systems.
It also adds Live Editor Tasks, which enable users to interactively explore parameters, preprocess data, and generate MATLAB code that becomes part of the live script.
A Deep Learning Toolbox builds on the flexible training loops and networks introduced earlier this year. New capabilities enable users to train advanced network architectures using custom training loops, automatic differentiation, shared weights, and custom loss functions. In addition, users can now build generative adversarial networks (GANs), Siamese networks, variational autoencoders, and attention networks. Deep Learning Toolbox also can now export to ONNX format networks that combine CNN and LSTM layers and networks that include 3D CNN layers.