3D Traffic Scenario Simulation for Impact-Prediction Training
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Solution Overview
Problem
Training machine learning systems for vehicle operations, such as collision prediction, is challenging due to the rarity and difficulty of acquiring real-world video sequences that include impacts and near-impacts, necessitating improved simulation techniques.
Innovation Solution
Generate photorealistic simulated traffic scenarios using a three-dimensional simulation engine and a machine learning system, perturbing scenarios to create variations with impact or near-impact conditions, and train the system with these scenarios to enhance its predictive capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-world video sequences are acquired for training machine learning systems, then training data authenticity is improved, but data availability deteriorates due to rarity of impact scenarios
Solution Approach 1:
The patent creates photorealistic simulated copies of real-world traffic scenarios using 3D simulation engines. These synthetic video sequences replicate authentic driving conditions, objects, and environmental factors while being freely generatable in large quantities, thus resolving the contradiction between data authenticity and availability
Solution Approach 2:
The system pre-generates extensive training datasets containing impact and near-impact scenarios through simulation before actual machine learning training begins. This preliminary creation of diverse training data eliminates the need to wait for rare real-world incidents, ensuring sufficient authentic-like data is available for robust model training
2Quantity of substance
If simulation techniques are used to generate training scenarios, then data availability is improved, but scenario realism deteriorates
Solution Approach 1:
The patent employs photorealistic rendering techniques to create highly realistic visual copies of real-world scenarios in the simulation. By accurately replicating lighting, textures, object appearances, and environmental conditions, the simulated scenarios achieve sufficient realism for effective machine learning training while maintaining unlimited data generability
Solution Approach 2:
The system adjusts simulation parameters such as graphics fidelity, environmental conditions, and physical accuracy to optimize the balance between realism and computational efficiency. By carefully tuning these parameters, the patent achieves photorealistic quality that maintains scenario authenticity while enabling large-scale scenario generation
3Reliability
If photorealistic rendering is used to create simulated scenarios, then scenario realism is improved, but computational complexity increases
Solution Approach 1:
The patent applies photorealistic rendering at appropriate levels of detail based on scenario requirements. Not all simulation elements require maximum fidelity - the system selectively applies high-quality rendering where it matters most for training accuracy while using optimized or simplified rendering for less critical elements, thus managing computational complexity while maintaining necessary realism
4Productivity
If large numbers of scenarios are generated for training, then training effectiveness is improved, but processing time increases
Solution Approach 1:
The system generates training scenarios in organized batches or sequences rather than all at once. By processing scenarios in periodic batches and utilizing parallel computation where possible, the patent manages the large volume of scenario generation and processing efficiently, reducing overall processing time while maintaining comprehensive training coverage
Data Source
AI summary
A computer that includes a processor and a memory, the memory including instructions executable by the processor to generate first traffic scenarios including one or more objects based on initial object locations and initial object trajectories of the one or more objects, wherein the first traffic scenarios are generated with a three-dimensional simulation engine. Probabilities of impact can be determined between the one or more objects included in the first traffic scenarios; selecting a subset of the first traffic scenarios that include the probabilities of impact greater than a user-selected threshold. Second traffic scenarios can be generated based on perturbing the selected subset of the first traffic scenarios wherein the second traffic scenarios are generated with the three-dimensional simulation engine. A machine learning system can be trained based on the first and the second traffic scenarios.