Generative AI for Text and Sensor Data Synthesis

Cross-modal data generation and transformation for automotive applications with Bosch Automated Driving

Duration: April 2017 - April 2018
Organization: Bosch Automated Driving

Project Overview

Research and development of Generative AI techniques for text synthesis and sensor measurement generation, with a focus on cross-modal transformation capabilities. Implemented solutions ranging from autoencoder-based architectures to advanced CycleGAN variants to enable flexible data generation and modality conversion in automotive contexts.

Generative AI Results on Public Data (KITTI)

Methodology

Text Synthesis

Developed Generative AI models for synthetic text generation, enabling the creation of diverse text datasets for training and testing purposes.

Sensor Measurement Synthesis

Implemented AI-based methods for generating synthetic sensor measurements, providing realistic data for system development and validation.

Cross-Modal Transformation

Created advanced models capable of transforming data between different modalities (e.g., text to sensor data, sensor to sensor), enabling flexible data manipulation.

Key Contributions

  • Autoencoder-Based Methods: Developed and implemented autoencoder architectures for efficient data representation and generation.
  • CycleGAN Variants: Explored and adapted various CycleGAN variants for cross-modal transformation tasks, enabling bidirectional conversion between data modalities.
  • Text Generation: Created Generative AI models for synthetic text synthesis with applications in automotive data augmentation.
  • Sensor Data Generation: Developed methods for generating realistic synthetic sensor measurements for system testing and validation.
  • Cross-Modal Capabilities: Established frameworks for transforming data between different modalities, expanding the flexibility of data processing pipelines.

Technical Stack

Python Primary Language
Generative Adversarial Networks Deep Learning
Autoencoders Neural Networks
CycleGAN Image-to-Image Translation
PyTorch/TensorFlow Frameworks

Applications

Automotive Data Augmentation

Generated synthetic text and sensor data to augment training datasets for automotive AI systems.

System Testing & Validation

Provided realistic synthetic data for comprehensive testing and validation of automotive perception systems.

Multi-Modal Integration

Enabled seamless integration of different data modalities through cross-modal transformation capabilities.

Related Skills

Generative AI Deep Learning Neural Networks GANs Autoencoders CycleGAN Text Generation Sensor Data Synthesis Cross-Modal Transformation Data Augmentation Python PyTorch TensorFlow
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