Generative Sensor Models for Autonomous Vehicle Simulation
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Solution Overview
Problem
Creating realistic sensor data for autonomous vehicles is challenging due to high computational costs and the need for detailed radio frequency simulations, signal processing, and scene modeling, especially for sensors like RADAR, which requires understanding internal sensor designs that may not be disclosed by manufacturers.
Innovation Solution
A system using generative machine learning to learn sensor models, such as deep neural networks, that can predict virtual sensor data for given scene configurations, reducing the need for intimate knowledge of sensor designs and enabling efficient simulation of various sensors like RADAR, LIDAR, and cameras.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional physics-based simulation techniques are used for RADAR sensor data, then measurement precision is improved, but computational cost increases significantly
Solution Approach 1:
The patent creates virtual sensor data by copying and transforming real sensor data through generative models. Instead of performing complex physics-based simulations, the system learns the mapping from scene configurations to sensor readings from real data and generates synthetic sensor data that replicates real-world characteristics, thereby achieving measurement precision without the high computational cost of traditional simulation methods
Solution Approach 2:
The patent replaces the mechanical physics-based simulation system with a machine learning-based generative model. The complex ray-casting, signal processing, and physics calculations are substituted with a trained neural network that directly generates sensor data from scene descriptions, dramatically reducing computational requirements while maintaining data realism
2Measurement precision
If detailed sensor models with reflective characteristics are created, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent copies the essential characteristics of complex sensor models from real sensors through data-driven learning. Instead of manually creating detailed models with reflective properties and penetration effects, the system learns these complex behaviors from real sensor data and reproduces them through generative models, achieving high modeling accuracy without the complexity of manual model creation
Solution Approach 2:
The patent transforms the complex sensor modeling problem from a deterministic physics-based approach to a probabilistic data-driven approach. By changing from fixed physical parameters to learned statistical distributions, the system captures complex sensor behaviors including reflections and penetrations through data patterns rather than explicit physical models, reducing device complexity
3Measurement precision
If conventional sensor modeling approaches are used, then measurement precision is improved, but ease of manufacture worsens
Solution Approach 1:
The patent copies sensor behavior patterns from real-world data without requiring access to internal sensor designs. By learning from external observations rather than internal mechanisms, the system achieves accurate sensor simulation without needing proprietary information from manufacturers, making the approach universally applicable and easy to implement
Solution Approach 2:
The patent introduces a generative model as an intermediary between scene configurations and sensor data. This intermediary learns the complex mapping relationships from real data and serves as a universal translator that works with any sensor type without requiring intimate knowledge of internal sensor designs, greatly simplifying implementation
4Measurement precision
If comprehensive scene modeling is performed for all reflections, then measurement precision is improved, but productivity decreases
Solution Approach 1:
The patent copies the net effect of all reflection and penetration phenomena from real sensor data without explicitly modeling each individual interaction. The generative model learns the aggregate patterns of multiple reflections and penetrations and reproduces them directly, achieving comprehensive modeling accuracy without the computational burden of tracking each reflection separately
Solution Approach 2:
The patent performs preliminary learning of complex reflection and penetration patterns during the training phase. By pre-learning these complex interactions from real data, the system can quickly generate accurate sensor data during simulation without performing real-time physics calculations for each reflection, greatly improving simulation speed while maintaining accuracy
Data Source
AI summary
In various examples, a sensor model may be learned to predict virtual sensor data for a given scene configuration. For example, a sensor model may include a deep neural network that supports generative learning—such as a generative adversarial network (GAN). The sensor model may accept an encoded representation of a scene configuration as an input using any number of data structures and/or channels (e.g., concatenated vectors, matrices, tensors, images, etc.), and may output virtual sensor data. Real-world data and/or virtual data may be collected and used to derive training data, which may be used to train the sensor model to predict virtual sensor data for a given scene configuration. As such, one or more sensor models may be used as virtual sensors in any of a variety of applications, such as in a simulated environment to test features and/or functionality of one or more autonomous or semi-autonomous driving software stacks.