Autonomous Vehicle Motion Plan Analysis for Constraint Influence
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Solution Overview
Problem
Current systems for autonomous vehicles lack the capability to effectively analyze and understand the influence of various environmental constraints on their motion plans, which hinders the optimization and refinement of navigation strategies.
Innovation Solution
A computer-implemented method and system that receive data on motion plans of autonomous vehicles, determining the influence of each environmental constraint on the vehicle's trajectory, allowing for the optimization of navigation paths based on multiple constraints such as objects and actions, using techniques like linear quadratic regulators and object-oriented implementations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If motion plans are generated based on multiple environmental constraints, then navigation safety and compliance are improved, but the complexity of motion planning increases
Solution Approach 1:
The patent segments the motion planning process into multiple independent constraint modules, where each constraint (e.g., traffic rules, physical limits, environmental factors) is evaluated separately. This modular approach allows the system to handle multiple constraints without exponentially increasing overall complexity, as each constraint can be processed independently and combined in the final motion plan.
Solution Approach 2:
The patent transforms qualitative constraint requirements into quantitative parameters that can be processed algorithmically. By defining constraints in terms of measurable parameters (e.g., speed limits, distance thresholds, time windows), the system can evaluate and balance multiple constraints through mathematical optimization rather than complex logical reasoning.
2Measurement precision
If the system determines influence measures for each constraint at every trajectory point, then the precision of navigation analysis is improved, but the computational time increases
Solution Approach 1:
The patent performs preliminary calculations of constraint influences at key trajectory points before final path determination. By pre-computing constraint impact metrics and caching these results, the system avoids redundant calculations during real-time navigation, maintaining high precision while reducing computational overhead.
Solution Approach 2:
The patent implements adaptive precision levels where constraint influence measurements are performed at all trajectory points only when necessary (e.g., during planning or hazard detection), while using coarser approximations during routine navigation. This partial application of high-precision analysis maintains safety without constant computational burden.
3Productivity
If the system analyzes motion plans for multiple autonomous vehicles in a fleet, then the overall navigation efficiency of the fleet is improved, but the data processing load increases
Solution Approach 1:
The patent merges constraint analysis across multiple vehicles by identifying and sharing common constraint evaluations. When multiple vehicles operate in similar environments or face identical constraints (e.g., same traffic rules, weather conditions, road geometry), the system computes these constraints once and reuses the results across all relevant vehicles, reducing redundant processing while maintaining individualized motion planning.
Data Source
AI summary
The present disclosure is directed to analyzing motion plans of autonomous vehicles. In particular, the methods, devices, and systems of the present disclosure can: receive data indicating a motion plan, of an autonomous vehicle through an environment, based at least in part on multiple different constraints of the environment; and determine, for each constraint of the multiple different constraints, a measure of an influence of the constraint on the motion plan.