Autonomous Driving Behavior Decisions With ROI-Based Object Prioritization
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Solution Overview
Problem
Existing methods and systems for autonomous vehicles fail to consistently account for the environment and objects in their vicinity when making behavioral, traffic, and object-based decisions, leading to inconsistent adherence to rules and guidelines.
Innovation Solution
A system and method that involve identifying regions of interest, determining decisions using a spline-based Frenet frame projector for object motion prediction, and prioritizing decisions based on traffic and object rules to ensure safe and rule-compliant navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing methods and systems are used for autonomous vehicle decision-making, then the system structure is relatively simple, but the consistency of rule adherence and decision reliability deteriorates
Solution Approach 1:
The decision-making system is segmented into multiple independent decision modules, each responsible for specific aspects of autonomous vehicle operation. These modules process different types of decisions independently and contribute to the overall decision-making process, improving reliability through modular architecture while managing system complexity.
Solution Approach 2:
The patent implements a hierarchical decision-making architecture where decision modules are nested within a structured framework. The nested doll principle is applied by organizing decision modules at different levels of abstraction, with higher-level modules coordinating lower-level modules, thereby improving decision consistency through structured hierarchy while containing complexity at each level.
2Adaptability or versatility
If multiple decisions are made simultaneously for different objects and rules, then the navigation capability is enhanced, but the difficulty of coordinating and prioritizing decisions increases
Solution Approach 1:
The patent introduces priority parameters and coordination mechanisms that dynamically adjust the weight and importance of different decisions based on situational context. By changing parameters such as decision priority, time sensitivity, and spatial relevance, the system can coordinate multiple simultaneous decisions effectively, enhancing navigation versatility while managing coordination complexity through parameter-based control.
Data Source
AI summary
A method may include obtaining input information relating to an environment in which an autonomous vehicle (AV) operates in which the input information describes at least one of: a state of the AV, an operation of the AV within the environment, a property of the environment, or an object included in the environment. The method may include identifying a region of interest that represents a section of the environment based on the input information and identifying a portion of the environment based on the identified region of interest. The portion of the environment may include an object that affects operation of the AV. The method may include determining a first decision that relates to the object and sending an instruction to a control system of the AV describing a given operation of the AV responsive to the object according to the first decision.