Autonomous Vehicle Perception Muxing for In-Vehicle Scenario Simulation
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Solution Overview
Problem
Testing autonomous vehicles in real-world scenarios is challenging due to the difficulty in simulating all possible data combinations, especially when interacting with other actors, which can be risky or unreliable with current augmented reality methods.
Innovation Solution
A system and method that combines real-world perception data with simulated data using a muxing tool to generate augmented perception data, ensuring seamless integration and conflict resolution between actual and simulated objects, allowing for more accurate simulation of real-world environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-world actors and objects are used for testing, then the realism and reliability of test results improve, but the risk and difficulty of coordination increase
Solution Approach 1:
The patent creates virtual copies of real-world actors and objects through simulation scenarios. These digital twins replicate the behavior, appearance, and interaction patterns of actual entities, allowing comprehensive testing without physical presence. The virtual actors are generated based on real-world data and can be deployed in augmented reality environments to maintain ecological validity while eliminating safety risks.
Solution Approach 2:
The system introduces a simulation layer as an intermediary between the test subject and the real world. This virtual environment acts as a mediator that translates real-world complexity into controllable, risk-free test scenarios. The intermediary layer allows indirect observation and interaction with real-world phenomena without direct exposure to their hazards.
2Adaptability or versatility
If all combinations of perceived data are tested, then the comprehensiveness of testing improves, but the complexity and resource requirements increase
Solution Approach 1:
The simulation scenario system dynamically generates and adapts test scenarios based on the autonomous vehicle's current state, location, and detected environmental conditions. Rather than pre-defining all possible scenarios, the system creates relevant test cases in real-time, adjusting the complexity and scope of simulations to match actual operating conditions. This dynamic approach ensures comprehensive coverage without requiring all possible combinations to be pre-programmed.
Solution Approach 2:
The virtual actor and simulation framework serves multiple testing functions simultaneously. A single simulation infrastructure can generate diverse scenarios ranging from edge cases to normal operations, accommodate different vehicle types and environments, and support various testing methodologies. This multi-functional platform reduces overall system complexity by consolidating multiple specialized testing systems into one universal solution.
3Reliability
If augmented reality is used to add simulated virtual environments, then the seamlessness and reliability of testing improve, but the complexity of data integration increases
Solution Approach 1:
The system merges real sensor data from the autonomous vehicle with simulated data from virtual scenarios through a unified data processing pipeline. The perception data, simulation data, and augmented reality elements are integrated into a single coherent environmental model that the vehicle's processing systems can interpret naturally. This merging occurs at the data level rather than requiring separate processing paths, reducing integration complexity while maintaining reliability.
Data Source
AI summary
This document discloses system, method, and computer program product embodiments for operating an autonomous vehicle (AV). For example, the method includes performing the following operations by a muxing tool when AV is deployed within a particular geographic area in a real-world environment: receiving perception data that is representative of at least one actual object which is perceived while AV is deployed within the particular geographic area in a real-world environment; receiving simulation data that represents a simulated object that could be perceived by AV in the real-world environment and that was generated using a simulation scenario which is selected from a plurality of simulation scenarios based on at least one of the particular geographic area in which AV is currently located and a current operational state of AV; and generating augmented perception data by combining the simulation data with the perception data.