Autonomous Vehicle Simulation for Realistic Motion Planner Testing
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Solution Overview
Problem
Conventional simulators struggle to realistically model the behaviors of simulated dynamic vehicles near autonomous vehicles, making it difficult to effectively test autonomous vehicle motion planners in a variety of scenarios.
Innovation Solution
An autonomous vehicle simulation system that generates simulated map data and perception data with simulated dynamic vehicles exhibiting various driving behaviors, allowing for the testing and evaluation of autonomous vehicle motion planning systems in a realistic and efficient manner.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional simulators are used to test autonomous vehicle motion planners, then testing can be performed without real-world risks, but the simulators fail to realistically model the behaviors of simulated dynamic vehicles
Solution Approach 1:
The patent creates virtual copies of real dynamic vehicles (NPC vehicles) that replicate human driving behaviors. These simulated vehicles copy the complex, unpredictable behaviors of real drivers including lane changes, overtaking, and acceleration patterns, providing realistic testing scenarios without requiring actual physical vehicles
Solution Approach 2:
The system dynamically adjusts simulation parameters such as vehicle density, weather conditions, road types, and traffic patterns to create diverse driving scenarios. This allows the simulator to model various real-world conditions while maintaining behavioral realism through configurable parameters
2Productivity
If more comprehensive testing scenarios are implemented to improve motion planner evaluation, then testing coverage increases, but computational resources and testing time requirements increase
Solution Approach 1:
The system pre-generates and stores numerous driving scenarios, traffic patterns, and vehicle behavior templates before actual testing begins. This preliminary preparation allows the simulator to quickly load and execute comprehensive test cases without requiring extensive real-time computation during the actual testing phase
Solution Approach 2:
The simulation system executes tests in periodic batches with different scenario configurations, allowing parallel processing of multiple test cases. This structured periodic execution improves overall testing throughput while managing computational resource utilization efficiently
3Adaptability or versatility
If the motion planner logic is made more complex to handle various driving scenarios, then the planner can detect and react to more situations, but the difficulty of building and configuring the planner increases
Solution Approach 1:
The motion planner is designed with universal, modular components that can handle multiple driving scenarios through configuration rather than complex custom logic. The same core planner infrastructure adapts to different scenarios by loading appropriate scenario definitions and parameters, reducing overall system complexity while maintaining versatility
Solution Approach 2:
The patent introduces an intermediary scenario definition layer between the motion planner and the simulation environment. This intermediary layer translates diverse driving scenarios into standardized planner inputs, allowing the planner to handle various situations without requiring complex internal logic for each specific scenario
Data Source
AI summary
An autonomous vehicle simulation system for analyzing motion planners is disclosed. A particular embodiment includes: receiving map data corresponding to a real world driving environment; obtaining perception data and configuration data including pre-defined parameters and executables defining a specific driving behavior for each of a plurality of simulated dynamic vehicles; generating simulated perception data for each of the plurality of simulated dynamic vehicles based on the map data, the perception data, and the configuration data; receiving vehicle control messages from an autonomous vehicle control system; and simulating the operation and behavior of a real world autonomous vehicle based on the vehicle control messages received from the autonomous vehicle control system.