Hybrid AV Simulation Using Real Vehicle Hardware Feedback
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Solution Overview
Problem
Conventional simulation systems for autonomous vehicles struggle to accurately test the hardware performance of complex systems like semi-trailer trucks, as they rely solely on software simulations, which can lead to failures in real-world scenarios due to unpredictable hardware responses to control signals.
Innovation Solution
A hybrid simulation system that uses a vehicle simulation computer operating within an autonomous vehicle to test its operations and performance under pre-configured scenarios, allowing actual vehicles to respond to simulated environments in real-world settings, including sending control signals for steering, throttle, braking, and gear shifting, and comparing responses to expected outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional simulation systems rely solely on software simulations, then simulation cost and time are reduced, but hardware performance testing accuracy deteriorates
Solution Approach 1:
The patent introduces a simulation computer as an intermediary device that generates simulated sensor data and control signals, mediating between the virtual simulation environment and the physical autonomous vehicle hardware. This allows the vehicle to respond to simulated scenarios in the real world, combining software simulation efficiency with hardware performance validation.
Solution Approach 2:
The patent creates a virtual copy of the real-world environment through simulated sensor data, including simulated objects, road conditions, and traffic scenarios. This virtual copy is transmitted to the autonomous vehicle, which processes it as if it were real sensor input, enabling realistic hardware testing without physical replication of all test scenarios.
2Measurement precision
If actual autonomous vehicles are used to respond to simulated environments, then hardware performance testing accuracy is improved, but system complexity increases
Solution Approach 1:
The simulation computer serves multiple functions: generating simulated sensor data, creating virtual environments, transmitting control signals, and receiving vehicle status information. This multi-functional approach consolidates complex simulation capabilities into a single device that interfaces with the autonomous vehicle's existing hardware, reducing overall system complexity.
Solution Approach 2:
The system implements a feedback loop where the autonomous vehicle sends status information back to the simulation computer, which compares actual responses with expected responses. This automated feedback mechanism simplifies the testing process by automatically evaluating hardware performance without requiring manual analysis of complex vehicle responses.
3Productivity
If pre-configured scenarios are used for testing, then testing efficiency is improved, but test coverage of unexpected real-world conditions deteriorates
Solution Approach 1:
The patent enables dynamic scenario generation where the simulation computer can create and modify test scenarios in real-time based on vehicle performance data. Pre-configured scenarios provide structured testing efficiency, while the ability to dynamically adjust and create new scenarios ensures comprehensive coverage of unexpected real-world conditions.
Solution Approach 2:
The system performs preliminary configuration of test scenarios before actual testing, allowing efficient reuse of proven test cases.同时, the framework allows for preliminary analysis of test results to identify gaps in coverage, enabling proactive addition of new scenarios to address unexpected real-world conditions.
Data Source
AI summary
Techniques are disclosed for performing hybrid simulation operations with an autonomous vehicle. A method of testing autonomous vehicle operations includes receiving, by a computer, a pre-configured scenario that includes one or more simulation parameters and one or more initial condition parameters, sending, to the autonomous vehicle and based on the one or more initial condition parameters, control signals that instruct the autonomous vehicle to operate at an operative condition, and in response to determining that the autonomous vehicle is operating at the operative condition, performing a simulation with the one or more simulated objects and the autonomous vehicle to test a response of the autonomous vehicle.