Features

Sensor Performance Simulation

Radar simulation addresses (a) antenna design (b) radome design (c) vehicle integration (d) radar response in environment.

Sensors are key new components that need to be developed for autonomous systems. Simulation uses high-fidelity physics to predict the performance of sensors such as radars, communication antennas, and ultrasonic sensors. For instance, simulation predicts radar patterns and gain in specific mission scenarios, eliminating expensive and time-consuming physical testing. Further, simulation computes the changes in performance of a radar when it is mounted on a vehicle, and when it operates in rain or snow, providing precise insights into real-life radar operation, at a fraction of the cost and time needed for field tests.

3D electromagnetic field solvers based on the Finite Element Method (FEM) and Shooting-Bouncing Rays solvers (SBR) are used for performing high-fidelity simulations of automotive radars. These simulations accelerate four radar development aspects, as follows:

(a) Isolated Radar Simulation: Here the radar antenna(s) and radome are simulated as placed in free space. Rapid parametric studies are conducted in such simulations to optimize geometric and material design of antennas and radomes.

(b) As-Installed Radar Simulation: Here the radar is simulated as installed on a vehicle to determine the degradation of radar performance due to obstructions caused by the vehicle's neighboring components.

(c) In-Environment Radar Simulation: Here the performance of a radar is simulated in a large, realistic environment comprised of other vehicles, buildings, humans, trees, etc. Given an input signal at the radar's transmit antenna(s), the high-fidelity physics simulation computes the output signal at the radar's receive antenna(s) based on what the radar “observes” in the virtual environment where it is placed.

(d) In-Mission-Scenario Radar Simulation: Here, reduced order models (ROMs) of high-fidelity radar simulations are used to create fast-executing, yet high accuracy, models of radars that can be used in mission scenario simulations described above.

Electronics Hardware and Semiconductor Simulation

Autonomous vehicles contain a host of new electronics hardware in the form of radars, lidars, cameras, other sensors, communication systems, signal processing systems sensor fusion boards, artificial intelligence computers, controllers, actuators, and HMIs (human-machine interfaces). The components need to be designed to withstand electrical, thermal, vibrational, and mechanical loads without failure over the lifetime of the vehicle. Simulation greatly expedites the speed of testing designs and provides physical insights that enable engineers to optimize electronics components and make them robust.

High-fidelity 3D physics-based simulation helps to analyze various physical phenomena across electronic packages, boards, enclosures, and systems, such as power optimization, power integrity, electrostatic discharge (ESD), electromagnetic interference/ electromagnetic compatibility (EMI/EMC), thermal, and structural reliability.

Simulation and modeling tools also help chip designers with accuracy and performance needed to reduce power noise and improve reliability of ICs that are being specially developed for autonomy applications. The challenges designers face are higher temperatures from higher current densities, self-heat, electromigration (EM) and electro-static discharge (ESD). An increase in temperature of 25°C typically leads to 3× to 5× degradation of the expected lifetime of devices. High-fidelity physics-based simulation provides accurate thermal analysis with robustness and connectivity checks, power and signal EM checks, full-chip ESD analysis, and effects of self-heating.

The more intelligent we make our machines and the more autonomously we let them operate in the open world, the more the operating scenarios that we must consider in designing and testing these machines. Such scenarios can easily number in millions, if not billions. Physical testing is prohibitively resource intensive and, therefore, impossible. Yet we can develop viable unmanned systems, autonomous vehicles, and robots with the help of engineering simulation that can virtually test a vehicle in thousands of operating scenarios in a fraction of the time and cost needed for physical testing.

This article was written by Sandeep Sovani, Ph.D., Director, Global Automotive Industry, ANSYS Inc. (Canonsburg, PA). For more information, Click Here.