Autonomous Vehicle Yield Model for Scalable Decision Control
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Solution Overview
Problem
Existing autonomous vehicle systems rely heavily on hand-crafted rules-based algorithms for yield decisions, which are time-consuming to develop and difficult to scale and adapt to different environments, leading to inefficiencies and limitations in navigation.
Innovation Solution
Implementing a machine-learned yield model that processes feature data from sensors and map information to provide yield decisions for autonomous vehicles, reducing reliance on rule-based systems and enabling adaptive, efficient navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hand-crafted rules-based algorithms are used for yield decisions, then the system can provide deterministic control logic, but the development time increases and scalability to different environments deteriorates
Solution Approach 1:
The patent replaces hand-crafted rules-based algorithms (mechanical/systematic approach) with a machine-learned yield model (data-driven approach). The machine learning model processes sensor data and map information to generate yield decisions, eliminating the need for manual rule creation and enabling automatic adaptation to different environments while maintaining reliable control.
2Ease of operation
If hand-crafted rules-based algorithms are used for yield decisions, then the system can provide explicit control logic, but the adaptability to different environments deteriorates
Solution Approach 1:
The machine-learned yield model uses parameter changes to adapt to different environments. Instead of hard-coded rules, the model learns optimal yield decision parameters from training data across various environments. The model can adjust its behavior based on input features such as sensor data, map information, and environmental conditions, providing both explicit control logic and environmental adaptability.
3Adaptability or versatility
If machine-learned yield model is implemented, then the adaptability and scalability improve, but the computational complexity increases
Solution Approach 1:
The machine learning model is trained in advance on comprehensive datasets covering various driving scenarios and environments. This preliminary training action allows the model to encode complex decision-making patterns during the training phase, reducing the computational burden during real-time execution. The model receives sensor data and map information as input and generates yield decisions efficiently based on learned patterns rather than complex real-time calculations.
Data Source
AI summary
The present disclosure provides autonomous vehicle systems and methods that include or otherwise leverage a machine-learned yield model. In particular, the machine-learned yield model can be trained or otherwise configured to receive and process feature data descriptive of objects perceived by the autonomous vehicle and/or the surrounding environment and, in response to receipt of the feature data, provide yield decisions for the autonomous vehicle relative to the objects. For example, a yield decision for a first object can describe a yield behavior for the autonomous vehicle relative to the first object (e.g., yield to the first object or do not yield to the first object). Example objects include traffic signals, additional vehicles, or other objects. The motion of the autonomous vehicle can be controlled in accordance with the yield decisions provided by the machine-learned yield model.