18464381. MULTI-MODALITY DATA AUGMENTATION ENGINE TO IMPROVE RARE DRIVING SCENARIO DETECTION FOR VEHICLE SENSORS simplified abstract (NEC Corporation)
Contents
- 1 MULTI-MODALITY DATA AUGMENTATION ENGINE TO IMPROVE RARE DRIVING SCENARIO DETECTION FOR VEHICLE SENSORS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 MULTI-MODALITY DATA AUGMENTATION ENGINE TO IMPROVE RARE DRIVING SCENARIO DETECTION FOR VEHICLE SENSORS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Original Abstract Submitted
MULTI-MODALITY DATA AUGMENTATION ENGINE TO IMPROVE RARE DRIVING SCENARIO DETECTION FOR VEHICLE SENSORS
Organization Name
Inventor(s)
Shepard Jiang of Princeton NJ (US)
Peng Yuan of Princeton NJ (US)
Yuncong Chen of Plainsboro NJ (US)
Haifeng Chen of West Windsor NJ (US)
MULTI-MODALITY DATA AUGMENTATION ENGINE TO IMPROVE RARE DRIVING SCENARIO DETECTION FOR VEHICLE SENSORS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18464381 titled 'MULTI-MODALITY DATA AUGMENTATION ENGINE TO IMPROVE RARE DRIVING SCENARIO DETECTION FOR VEHICLE SENSORS
Simplified Explanation
The abstract describes a computer-implemented method for simulating vehicle data and improving driving scenario detection. It involves retrieving key parameters from real data, transferring scenario descriptions to scripts, training a neural network model, refining simulation data for rare driving scenarios, and outputting the training data for scenario detection.
- Key parameters from real data are used to generate scenario configurations and descriptions.
- Neural network model is trained to minimize differences between raw simulation data.
- Rare driving scenarios are refined to generate training data for scenario detection.
Potential Applications
This technology can be applied in the development of autonomous driving systems, advanced driver assistance systems, and simulation software for vehicle testing and training.
Problems Solved
1. Improved accuracy in detecting driving scenarios. 2. Enhanced training data for rare driving scenarios.
Benefits
1. Increased safety on the roads. 2. Better performance of autonomous driving systems. 3. Efficient training of scenario detectors.
Potential Commercial Applications
Enhancing Autonomous Driving Systems with Advanced Scenario Detection Technology
Unanswered Questions
=== How does this technology impact data privacy and security in autonomous vehicles? === What are the potential limitations or challenges in implementing this method in real-world driving scenarios?
Original Abstract Submitted
A computer-implemented method for simulating vehicle data and improving driving scenario detection is provided. The method includes retrieving, from vehicle sensors, key parameters from real data of validation scenarios to generate corresponding scenario configurations and descriptions, transferring target scenario descriptions and validation scenario descriptions to target scenario scripts and validation scenario scripts, respectively, to create first raw simulation data pertaining to target scenario descriptions and second raw simulation data pertaining to validation scenario descriptions, training, by an adjuster network, a deep neural network model to minimize differences between the first raw simulation data and the second raw simulation data, refining the first and second raw simulation data of rare driving scenarios to generate rare driving scenario training data, and outputting the rare driving scenario training data to a display screen of a computing device to enable a user to train a scenario detector for an autonomic driving assistant system.
