Autonomous Vehicle Algorithm Testing With Trajectory-Based Traffic Simulation
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Solution Overview
Problem
Robust testing of autonomous vehicle algorithms in real-world environments is dangerous or infeasible, and existing simulators lack the capability to realistically model interactions with other vehicles in a time-efficient manner.
Innovation Solution
The development of a simulation framework that allows for 'super real-time' simulation of autonomous vehicle systems, enabling the collection of vast amounts of data efficiently by configuring primary and secondary vehicular models with algorithms, calculating trajectories, and integrating updated algorithms into autonomous vehicle systems, thereby enhancing operational robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If simulators are used to test autonomous vehicle algorithms, then safety of real-world testing is improved, but realism and effectiveness of testing deteriorates
Solution Approach 1:
The patent creates virtual copies of real vehicles, environments, and traffic scenarios in a simulation framework. These virtual replicas maintain the physical and behavioral characteristics of real-world entities, allowing realistic testing without physical risks. The simulation engine replicates sensor data, vehicle dynamics, and environmental conditions to mirror actual driving scenarios.
Solution Approach 2:
The system performs preliminary testing and validation of autonomous vehicle algorithms in the virtual environment before deploying to real-world conditions. By pre-simulating various driving scenarios, edge cases, and failure modes, the system prepares and validates algorithms in advance, ensuring safety and realism are both achieved through progressive testing.
2Productivity
If simulators are used to test autonomous vehicle algorithms, then operational costs and time are reduced, but testing effectiveness deteriorates
Solution Approach 1:
The simulation framework dynamically adjusts scenario complexity, simulation speed, and environmental conditions based on testing requirements. The system can transition between different fidelity levels and scenario types, enabling efficient coverage of both common and edge-case scenarios while maintaining testing effectiveness through adaptive scenario generation.
Solution Approach 2:
The system varies multiple parameters including weather conditions, traffic density, vehicle speeds, and sensor noise levels to create diverse testing scenarios. By systematically changing these parameters, the simulation achieves comprehensive testing coverage efficiently, validating algorithm robustness across different operating conditions without requiring proportional increases in real-world testing resources.
3Reliability
If algorithms are robustly tested in real-world environments, then testing realism is improved, but safety and feasibility deteriorates
Solution Approach 1:
The virtual simulation environment serves as an intermediary between algorithm development and real-world deployment. This intermediate layer allows realistic testing of autonomous vehicle algorithms without direct exposure to physical risks, acting as a buffer that preserves both realism and safety by filtering and controlling the transfer of testing scenarios between virtual and physical domains.
4Reliability
If more comprehensive scenarios are simulated, then operational robustness is improved, but computational resources and time increase
Solution Approach 1:
The simulation system employs periodic execution of test scenarios with varying complexity levels. Rather than running all comprehensive scenarios continuously, the system cycles through different scenario sets, adjusting the mix of common and edge-case scenarios based on testing progress and algorithm performance, thereby achieving robustness validation efficiently over time.
Solution Approach 2:
The system applies partial simulation comprehensiveness by focusing computational resources on the most critical and informative scenarios. Rather than equally simulating all possible scenarios, the system prioritizes testing of high-impact edge cases and failure modes that provide maximum insight into algorithm robustness, achieving effective validation with reduced computational overhead.
Data Source
AI summary
Disclosed are devices, systems and methods for the operational testing on autonomous vehicles. One exemplary method includes configuring a primary vehicular model with an algorithm, calculating one or more trajectories for each of one or more secondary vehicular models that exclude the algorithm, configuring the one or more secondary vehicular models with a corresponding trajectory of the one or more trajectories, generating an updated algorithm based on running a simulation of the primary vehicular model interacting with the one or more secondary vehicular models that conform to the corresponding trajectory in the simulation, and integrating the updated algorithm into an algorithmic unit of the autonomous vehicle.