Virtual Lidar Training Data Generation for Dangerous Driving Scenarios
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Solution Overview
Problem
Collecting large amounts of real-world driving datasets for autonomous vehicles is costly, burdensome, and dangerous, making it impractical for training effective machine learning models.
Innovation Solution
Generating feature-rich training datasets using virtual environments, including photo-realistic and depth-map-realistic scenes, and environment-object data to simulate real-world driving scenarios, allowing for safe and efficient training of autonomous vehicle models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-world driving datasets are collected for autonomous vehicle training, then the training data reflects actual driving conditions, but the data collection becomes costly, time-intensive, and dangerous
Solution Approach 1:
The patent creates virtual copies of real-world driving environments, vehicles, and scenarios through simulation. Instead of collecting data from physical test drives, the system generates synthetic training datasets that replicate actual driving conditions, traffic patterns, and edge cases in a virtual space, eliminating the need for time-consuming real-world data collection while maintaining training data quality
Solution Approach 2:
The system pre-generates comprehensive training datasets covering rare and dangerous scenarios before they are needed for model training. By creating a library of virtual driving scenarios in advance—including dangerous situations like crashes and risky driving behaviors—the system eliminates the need to collect this data in real-time, saving significant time and avoiding safety risks
2Reliability
If real-world driving datasets are collected to cover dangerous scenarios, then the training model learns from actual risk situations, but the data collection process itself becomes dangerous
Solution Approach 1:
The patent uses virtual simulations to copy dangerous driving scenarios and environmental conditions without exposing physical vehicles or personnel to actual risks. The simulation engine recreates hazardous situations such as crashes, risky driving behaviors, and extreme weather conditions in a safe virtual environment, allowing the training model to learn from these scenarios without causing harm
Solution Approach 2:
The system converts the harmful aspect of real-world dangerous scenario collection into a benefit by using virtual simulation. What would be harmful in the real world (exposing vehicles to crashes and risky situations) becomes beneficial when simulated, as it allows unlimited repetition of dangerous scenarios for training purposes without any actual safety risks
3Reliability
If sufficient training datasets are collected for autonomous driving, then the machine learning model achieves adequate performance, but the collection process becomes extremely costly and burdensome
Solution Approach 1:
The patent replaces expensive and burdensome real-world data collection with cost-effective virtual simulation. The system generates training datasets by rendering virtual environments, vehicles, and scenarios using software, eliminating the need for costly physical test drives, specialized equipment, and large teams of data collectors while maintaining model training effectiveness
Solution Approach 2:
The simulation system is self-sufficient in generating training data without requiring external real-world data collection operations. The virtual environment automatically generates diverse driving scenarios, captures sensor data from virtual sensors, and creates labeled training datasets through the simulation process itself, making the entire data generation process internally managed and cost-efficient
Data Source
AI summary
Automated training dataset generators that generate feature training datasets for use in real-world autonomous driving applications based on virtual environments are disclosed herein. The feature training datasets may be associated with training a machine learning model to control real-world autonomous vehicles. In some embodiments, an occupancy grid generator is used to generate an occupancy grid indicative of an environment of an autonomous vehicle from an imaging scene that depicts the environment. The occupancy grid is used to control the vehicle as the vehicle moves through the environment. In further embodiments, a sensor parameter optimizer may determine parameter settings for use by real-world sensors in autonomous driving applications. The sensor parameter optimizer may determine, based on operation of the autonomous vehicle, an optimal parameter setting of the parameter setting where the optimal parameter setting may be applied to a real-world sensor associated with real-world autonomous driving applications.