Neural Sensor Data Evaluation With Latent Memory for Driver Assistance
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Solution Overview
Problem
Existing driver assistance systems face challenges in efficiently processing and representing the surroundings of a motor vehicle under various weather conditions, particularly with radar sensors, which require significant memory and computing resources for temporal data processing.
Innovation Solution
A device and method utilizing an artificial neural network with a memory state that stores latent features, allowing for temporal information retention and reduced memory and computing requirements by processing only current sensor data, while compensating for ego-vehicle movement and fusing multiple time steps without storing raw data histories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If temporal information from multiple time steps is stored and processed using raw data histories, then the accuracy of object detection and tracking is improved, but the memory requirement and computing requirement increase significantly
Solution Approach 1:
The patent extracts only the essential temporal information in the form of latent features from the raw sensor data histories. Instead of storing and processing complete raw data from multiple time steps, the system extracts compressed latent features that capture the essential temporal patterns. This extraction principle resolves the contradiction by retaining only the necessary information for accurate detection and tracking while discarding redundant data, thereby reducing memory and computing requirements.
Solution Approach 2:
The patent transforms the representation of temporal information by changing from raw data parameters to latent feature parameters. The neural network learns to encode temporal patterns into compact latent feature vectors that preserve the essential information needed for accurate object detection and tracking. This parameter transformation allows the system to maintain high accuracy while working with a much more compact data representation, resolving the memory and computing resource contradiction.
2Loss of information
If measurements of multiple time steps are temporarily stored and processed, then temporal information is retained for better evaluation, but the processing time and memory requirement increase
Solution Approach 1:
The patent performs preliminary action by pre-training the neural network to learn effective latent feature representations that capture temporal patterns. During this offline training phase, the network learns to compress temporal information into compact latent features. During actual operation, this pre-learned capability allows rapid processing without needing to store and process extensive raw data histories, thus retaining temporal information while minimizing processing time.
Solution Approach 2:
The patent creates a compressed copy of temporal information in the form of latent features rather than storing the original raw data. The neural network learns to create these latent feature copies that encapsulate the essential temporal patterns needed for accurate evaluation. This copying approach allows the system to retain temporal information efficiently while enabling fast processing during operation.
3Measurement precision
If a long history of raw data is used for each prediction, then the accuracy of sensor data evaluation is improved, but the inference time and memory requirement increase
Solution Approach 1:
The patent extracts only the essential evaluation results from the temporal patterns in the data history. Instead of using the entire raw data history during inference, the system extracts compact latent features that capture the necessary temporal context for accurate evaluation. This extraction enables high accuracy while maintaining fast inference times by working with compressed representations rather than raw data.
4Reliability
If radar sensors are used for accurate representation of surroundings, then reliability under various weather conditions is improved, but the complexity of processing radar data increases
Solution Approach 1:
The patent replaces complex mechanical processing of radar data with a learned neural network model. Instead of using traditional signal processing algorithms that are computationally intensive and complex, the system uses a neural network that has been trained to process radar point cloud data efficiently. The network automatically learns the appropriate features and transformations needed, substituting complex mechanical processing with a more efficient learned model that maintains reliability while reducing processing complexity.
Data Source
AI summary
A device for evaluating sensor data of a sensor device. The device includes an interface that receives the sensor data from the sensor device. A computing device provides input data to an artificial neural network based on the received sensor data and the artificial neural network uses the input data to output output data for evaluating the sensor data. The computing device adjusts parameters of the artificial neural network using a memory state, wherein the memory state depends on latent features of the artificial neural network relating to a previous calculation step.