Autonomous Driving Planner Context-Aware Parameter Optimization
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Solution Overview
Problem
Existing autonomous driving systems lack adaptability to various driving conditions and traffic contexts, as planner model parameters are set globally and do not adjust to specific scenarios, affecting planning performance.
Innovation Solution
A context-aware optimization framework adjusts planner model parameters based on the driving context, using machine learning and neural networks to optimize performance for specific environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If planner model parameters are set globally for all scenarios, then the system is simple to operate and maintain, but the planning performance deteriorates in specific driving conditions
Solution Approach 1:
The patent segments the globally applicable planner parameters into context-specific parameter sets. Different parameter configurations are created for different driving contexts (e.g., urban, highway, residential areas), allowing the system to select appropriate parameters based on the current driving scenario rather than using a single global configuration.
Solution Approach 2:
The patent implements dynamic parameter adjustment by introducing a context recognition module that automatically identifies the current driving scenario and selects or adjusts parameters in real-time. This transforms the static global parameter system into a dynamic one that adapts to changing driving conditions, improving planning performance without requiring manual reconfiguration.
2Reliability
If planner model parameters are optimized for specific driving contexts, then the planning performance improves, but the system complexity increases
Solution Approach 1:
The patent creates a universal parameter optimization framework that can handle multiple driving contexts through a single integrated system. The context recognition module and parameter selection mechanism serve multiple functions: identifying various driving scenarios, selecting appropriate parameter sets, and adapting to new contexts, thereby managing complexity through multi-functionality rather than separate systems for each context.
Solution Approach 2:
The system implements self-service through automated context recognition and parameter selection. The planner automatically identifies the current driving context and adjusts parameters without requiring manual intervention or complex configuration management, allowing the system to serve itself in optimizing performance across different scenarios.
3Adaptability or versatility
If context-aware parameter optimization is implemented, then the adaptability to different driving conditions improves, but the computational resources required increase
Solution Approach 1:
The patent applies preliminary action by pre-configuring parameter sets for different driving contexts before runtime. The context recognition module and parameter selection logic are prepared in advance, allowing the system to quickly match current conditions with pre-defined parameter configurations rather than performing complex optimization calculations in real-time, thereby reducing computational energy consumption while maintaining high adaptability.
Data Source
AI summary
Systems and methods for optimizing a planning model for autonomous driving. Image data is generated from a vehicle camera. A perception model detects agents in the environment based on the image data. A planner model is executed to generate a predicted trajectory of the vehicle based upon configuration parameters associated with characteristics of the vehicle. Information regarding a context associated with the detected agents is received from the perception model. An optimizer is configured to select a subset of the configuration parameters to be optimized based on the context, select an objective function based on the context, adjust the selected subset of configuration parameters based on a current value of the objective function, generate a new planned trajectory, derive a value of the objective function, and determine optimal configuration parameters that minimize the objective function.