Autonomous Vehicle Motion-Plan Validation for Constraint Compliance
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Solution Overview
Problem
Autonomous vehicles lack effective methods to validate motion plans against predetermined constraints, such as collision avoidance and traffic laws, which can lead to unsafe operations and inefficient resource usage.
Innovation Solution
A computer-implemented method and system that uses a machine-learning model to generate and validate motion plans for autonomous vehicles, determining whether execution would violate constraints like collision avoidance, traffic laws, and vehicle parameters, and deciding whether to execute or modify the plan based on these determinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If motion plans are generated without validation against predetermined constraints, then computing resources are conserved and generation speed is improved, but safety is compromised and constraint violations occur
Solution Approach 1:
The patent applies preliminary action by validating motion plans against predetermined constraints before execution. The system proactively checks whether generated motion plans violate any constraints (collision avoidance, traffic laws, vehicle parameters) and prevents execution of invalid plans, thereby ensuring safety without requiring complex real-time intervention systems.
2Reliability
If motion plans are validated against predetermined constraints, then safety is improved and constraint compliance is ensured, but computing resources are consumed and processing time increases
Solution Approach 1:
The patent applies partial action by implementing selective validation - the system validates motion plans against constraints only when necessary, rather than performing exhaustive validation on every single plan. This approach ensures constraint compliance for critical safety constraints while avoiding unnecessary computation for less critical scenarios, thereby maintaining productivity.
3Manufacturing precision
If comprehensive constraint validation is performed, then motion plan quality is improved and unsafe plans are prevented, but system complexity and computational overhead increase
Solution Approach 1:
The patent applies local quality by focusing validation efforts on specific critical constraints rather than uniformly validating all aspects of every motion plan. The system identifies and prioritizes validation of local critical areas (such as collision avoidance and traffic law compliance) while reducing validation intensity in less critical areas, thereby ensuring motion plan quality without excessive energy consumption.
Data Source
AI summary
The present disclosure is directed to validating motion plans for 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 of the autonomous vehicle; receive data indicating one or more inputs utilized in generating the motion plan; and determine, based at least in part on the data indicating the motion plan and the data indicating the input(s), whether execution of the motion plan by the autonomous vehicle would violate one or more predetermined constraints applicable to motion plans for the autonomous vehicle.