Autonomous Vehicle Motion Planning with Multi-Constraint Decisions
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating safely and efficiently due to the complexity of interacting with multiple objects and constraints in their environment, such as traffic rules and dynamic object positions, which existing systems struggle to manage effectively.
Innovation Solution
A motion planning system that uses a scenario generator to identify objects of interest, generate constraints in a multi-dimensional space, and determine navigation decisions to optimize the vehicle's path, ensuring compliance with various constraints and improving safety and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a motion planning system navigates multiple objects and constraints in the environment, then safety and efficiency are improved, but system complexity increases
Solution Approach 1:
The motion planning system is divided into distinct functional modules: a scenario generator that identifies objects of interest and generates constraints, and a constraint solver that determines navigation decisions. This segmentation allows each module to specialize in specific tasks, improving overall system reliability while managing complexity through modular design.
Solution Approach 2:
The patent introduces an intermediate representation of constraints in a multi-dimensional space that acts as a mediator between the complex environmental data and the navigation decisions. This intermediate layer simplifies the problem by transforming diverse constraints into a unified format that can be systematically processed, reducing the effective complexity of the planning system.
2Measurement precision
If the system generates constraints in multi-dimensional space for multiple objects of interest, then navigation decision accuracy is improved, but computational complexity increases
Solution Approach 1:
The system represents constraints in a multi-dimensional space that includes spatial dimensions and temporal dimensions. This dimensional expansion allows the system to capture complex relationships between objects and constraints more accurately, improving navigation decision precision while providing a structured framework for managing computational complexity.
Solution Approach 2:
The patent transforms the navigation problem by changing the parameters of representation from simple spatial coordinates to a multi-dimensional constraint space that includes temporal and relational parameters. This parameter transformation enables more accurate modeling of complex scenarios while providing a systematic approach to solving the expanded problem space.
3Reliability
If the system determines consistent navigation decisions relative to multiple objects of interest, then safety is improved, but processing time increases
Solution Approach 1:
The scenario generator performs preliminary actions by pre-identifying objects of interest and pre-generating constraints before the constraint solver determines navigation decisions. This preliminary processing organizes the problem data in advance, allowing the constraint solver to focus on finding consistent solutions more efficiently, thereby reducing overall processing time while maintaining safety.
Data Source
AI summary
The present disclosure provides autonomous vehicle systems and methods that include or otherwise leverage a motion planning system that generates constraints as part of determining a motion plan for an autonomous vehicle (AV). In particular, a scenario generator within a motion planning system can generate constraints based on where objects of interest are predicted to be relative to an autonomous vehicle. A constraint solver can identify navigation decisions for each of the constraints that provide a consistent solution across all constraints. The solution provided by the constraint solver can be in the form of a trajectory path determined relative to constraint areas for all objects of interest. The trajectory path represents a set of navigation decisions such that a navigation decision relative to one constraint doesn't sacrifice an ability to satisfy a different navigation decision relative to one or more other constraints.