Virtual Sensor Modeling via Raycasting for Automated Driving
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Solution Overview
Problem
Current methods for modeling virtual sensors in virtual test environments for automated driving are inefficient due to the complexity of physical sensor models, which require significant computing power and memory, and lack a systematic approach for creating sensor models, especially for wave-based sensors like ultrasound, radar, and lidar.
Innovation Solution
A method using raycasting technology to model virtual sensors, including definitions of a sensor support, raycast distribution shape, raycast properties, raycast reflection factor, and raycast echo, allowing for the creation of a formalized and generally usable virtual sensor model that can be adapted to individual requirements, using either manual modeling or automatic generation based on real sensor properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physical sensor models are used in virtual test environments, then measurement precision and realism are improved, but computing power requirements and device complexity increase significantly
Solution Approach 1:
The patent segments the sensor model into discrete raycast elements that can be independently calculated and processed. Each raycast represents a specific measurement direction and can be computed separately, allowing the complex physical model to be broken down into manageable computational units that reduce overall system complexity while maintaining measurement precision.
Solution Approach 2:
The patent replaces complex physical sensor models with a raycasting-based computational approach. Instead of simulating full wave propagation physics, the system uses geometric ray tracing to model sensor behavior, substituting mechanical/physical simulation with a more efficient computational method that maintains accuracy while reducing computing requirements.
2Measurement precision
If physical sensor models are used in virtual test environments, then measurement precision is improved, but computing power requirements increase
Solution Approach 1:
The patent extracts only the essential measurement characteristics from complex physical sensor models, isolating the key parameters needed for accurate sensing while discarding unnecessary computational complexity. This extraction allows the system to maintain measurement precision by preserving critical sensor behaviors while removing computationally expensive elements.
Solution Approach 2:
The patent uses simplified raycast representations that are computationally inexpensive to generate and can be rapidly created and discarded for each simulation step. These lightweight raycast objects replace heavy physical models, enabling frequent updates and comprehensive testing scenarios without excessive computing power requirements.
3Reliability
If comprehensive sensor information is collected for automated driving, then reliability is improved, but the number of sensor models required increases device complexity
Solution Approach 1:
The patent creates a universal raycasting framework that can model multiple types of sensors (ultrasound, radar, lidar, camera) using a single unified approach. Instead of requiring separate complex models for each sensor type, the raycasting system provides multi-functional capability to represent diverse sensors through common computational principles, reducing the number of distinct models needed while maintaining reliability across all sensor types.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables efficient and realistic modeling of sensors in virtual environments, reducing the need for extensive computing resources and providing a flexible framework for testing various sensor scenarios, such as ultrasonic and LIDAR sensors, with the ability to automatically generate sensor models and adapt to different test cases.
Implementation Method 1
A method using raycasting technology to model virtual sensors, including definitions of a sensor support, raycast distribution shape, raycast properties, raycast reflection factor, and raycast echo
Data Source
AI summary
The disclosure relates to a method that models a motor vehicle sensor in a virtual test environment by way of definition. Using a sensor support, a raycast distribution shape, a group of raycast properties, a raycast reflection factor, and a raycast echo, a sensor in reality may be tested in a virtual environment to calibrate the sensor in reality. The sensor support is a virtual sensor support for a virtual sensor model, which forms a three-dimensional or two-dimensional avatar of the sensor in reality, in the virtual test environment. The sensor support has a sensor starting point that is used as an origin for a raycast distribution shape. The method extracts a special application of the sensor in reality in an application case, which is particularly useful for testing scenarios.