Driving Simulation Planner Switching for Realistic Agent Control
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Solution Overview
Problem
Creating realistic and cost-effective driving simulations that accurately reflect real-world scenarios is challenging due to noisy, inconsistent, or incomplete data, and the computational expense of executing complex interactions between vehicle control systems and other agents in the simulated environment.
Innovation Solution
The technique involves switching control of simulated agents between different programmatic planners based on detected driving scenarios, using a combination of heuristic and machine-learned planners to achieve more realistic and efficient simulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If driving simulations use complex interacting systems and components to accurately reflect real-world scenarios, then simulation realism and accuracy are improved, but computational resources and execution cost increase
Solution Approach 1:
The patent segments the simulation system into multiple independent components: vehicle control systems, sensor models, environment models, and agent systems. Each component can be executed, paused, and reset independently, allowing selective execution of computationally intensive parts while maintaining overall simulation accuracy.
Solution Approach 2:
The patent implements pre-computation of simulation scenarios and agent behaviors. Agent policies are trained offline using machine learning, and simulation scenarios are pre-validated. This allows the runtime execution to focus only on evaluating vehicle control systems, significantly reducing computational resources during actual simulation runs.
2Reliability
If driving simulations use multiple different interacting systems and components to accurately reflect real-world scenarios, then simulation realism is improved, but device complexity increases
Solution Approach 1:
The patent implements a universal simulation platform that can execute multiple different vehicle control systems, sensor models, and environment configurations through a common interface. The agent system serves multiple functions by acting as both test subjects and environmental elements, reducing the need for separate specialized components.
Solution Approach 2:
The patent structures the simulation system with nested components where agent systems are contained within the broader simulation environment, which itself is contained within the overall simulation platform. This hierarchical nesting allows complex systems to be managed through layered abstraction, reducing apparent complexity while maintaining realism.
3Reliability
If driving simulations are executed for longer periods to improve evaluation robustness, then evaluation accuracy is improved, but execution time and resource consumption increase
Solution Approach 1:
The patent implements continuous simulation execution with automatic scenario generation and agent behavior maintenance. Instead of running discrete separate simulations, the system maintains continuous operation where agents continuously interact with the vehicle control system being evaluated, providing robust evaluation data without requiring multiple separate execution cycles.
Solution Approach 2:
The patent uses playback agents that replay pre-recorded real-world driving scenarios and agent behaviors. This allows the simulation to evaluate vehicle control systems against realistic driving patterns without requiring equally long real-time execution, as the challenging scenarios are captured and reused multiple times.
Data Source
AI summary
Techniques are described herein for executing driving simulations where control of simulated agents is switched between different programmatic planners based on different driving scenarios encountered in the simulation. As an example, a simulation system may execute a driving simulation including a programmatic agent initially controlled using a heuristic planner. During the execution of the simulation, the simulation system may detect one or more driving scenarios for which the heuristic planner is unable to navigate the environment or may determine an unrealistic trajectory for the simulated agent. Based on detecting a driving scenario, the simulation system may determine a machine-learned planner to control the simulated agent to navigate the driving scenario. After the driving scenario has been successfully traversed by the simulated agent using the machine-learned planner, the simulation system may switch the control of the simulated agent back to the heuristic planner to continue the simulation.