Environment Sensor Behavior Modeling for Precise Virtual Test Drives
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Solution Overview
Problem
Current methods for simulating environment sensors in driver assistance systems lack precision, especially when exact specifications of sensors are not available, making it difficult to create accurate physical models for virtual test drives.
Innovation Solution
A method using artificial neural networks to simulate environment sensors by training them with data streams from traffic scenarios, allowing for realistic simulation of sensor data and behavior, including effects of weather conditions and hardware defects, enabling precise virtual test drives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a physical model is used to simulate environment sensors, then the simulation can be performed with available data, but the precision and realism of the sensor simulation deteriorates when exact sensor specifications are not available
Solution Approach 1:
The patent creates a virtual copy of the environment sensor by training an artificial neural network to replicate the sensor's behavior. The neural network is trained using actual sensor data from test drives, creating a digital twin that mimics the sensor's response characteristics without requiring detailed physical specifications. This copying approach allows high-fidelity simulation even when exact sensor parameters are unavailable.
Solution Approach 2:
The patent transforms the sensor simulation approach by changing from fixed physical parameters to adaptive neural network parameters. Instead of relying on predetermined sensor specifications, the system uses learnable parameters that are adjusted during training to match actual sensor behavior. This allows the simulation to capture complex sensor characteristics that cannot be described by simple physical models.
2Measurement precision
If complex physical models are created to accurately represent sensor behavior, then simulation precision improves, but the complexity and resource requirements of the simulation system increases
Solution Approach 1:
The patent replaces complex mechanical/physical modeling approaches with a data-driven neural network system. Instead of creating detailed physical models of sensor hardware and processing algorithms, the system uses a neural network that learns sensor behavior directly from data. This substitution simplifies the simulation architecture while maintaining or improving accuracy, as the neural network automatically captures complex relationships without requiring explicit physical models.
Solution Approach 2:
The patent performs preliminary training of the neural network using extensive sensor data from real test drives before deployment. This preliminary action creates a pre-trained model that encapsulates complex sensor behavior patterns. During actual simulation, the pre-trained network can be efficiently executed without requiring complex real-time calculations, thus reducing runtime complexity while maintaining high precision.
3Reliability
If real sensor data from test drives is used to train simulation models, then the realism of virtual test drives improves, but the amount of data processing and training time increases
Solution Approach 1:
The patent performs the computationally intensive data processing and neural network training as a preliminary action before the virtual test drives are executed. By completing the training phase in advance using historical sensor data, the system creates a ready-to-use simulation model. During subsequent virtual test drives, the pre-trained model can be efficiently applied without requiring extensive real-time processing, thus achieving high realism without time loss during actual testing.
Data Source
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AI summary
The invention relates to a computer-aided method for training an artificial neuronal network for the simulation of an environment sensor of a driver assistance system, preferably comprising the following work steps: reading in traffic scenarios of a test journey; deriving test journey data and the sensor data to be output by the environment sensor from the traffic scenarios; generating a data stream, in particular an image stream, which depicts the traffic scenarios from the perspective of the environment sensor; outputting the data stream such that the environment sensor can generate sensor data on the basis of the data stream and can provide same to a data interface at which the test journey data and the sensor data to be output are also provided; and reading the provided data into the artificial neuronal network.