Vehicle Narrow AI Agent Selection for Low-Power Perception
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Solution Overview
Problem
Current autonomous vehicle perception systems are computationally intensive due to the need for exhaustive labeling of raw sensor inputs, which leads to high power consumption and complexity in identifying and localizing multiple objects within the scene.
Innovation Solution
Implementing a perception unit that uses an ensemble of narrow AI agents, where anchors provide contextual cues to select relevant agents, reducing the need for detailed object detection and lowering power consumption by focusing on high-level contextual analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exhaustive labeling of raw sensor inputs is performed to identify and localize all objects, then measurement precision and reliability are improved, but use of energy and device complexity increase significantly
Solution Approach 1:
The perception system is segmented into multiple specialized narrow AI agents, each responsible for detecting specific object categories (e.g., pedestrians, vehicles, cyclists). This segmentation allows the system to process sensor data more efficiently by distributing detection tasks across specialized agents rather than using a single comprehensive model, reducing overall computational energy consumption while maintaining detection accuracy.
Solution Approach 2:
The system performs partial detection by activating only the subset of narrow AI agents relevant to the current scene context. Instead of running all possible detection models simultaneously, the system selectively engages only those agents needed for the current driving situation, significantly reducing power consumption while maintaining necessary detection coverage.
2Measurement precision
If exhaustive labeling of raw sensor inputs is performed to identify and localize all objects, then measurement precision and reliability are improved, but device complexity increases significantly
Solution Approach 1:
The perception system is divided into multiple independent narrow AI agents, each specialized for detecting specific object types. This segmentation reduces the complexity of individual agents compared to a single comprehensive model, making the system more manageable and easier to deploy while collectively achieving comprehensive detection coverage through the ensemble of specialized agents.
Solution Approach 2:
The ensemble of narrow AI agents provides universal detection capability across multiple object categories. Each agent is a simple, specialized component, but the collective ensemble achieves multi-functional detection coverage for various object types (pedestrians, vehicles, cyclists, etc.), reducing overall system complexity through modular design while maintaining comprehensive detection accuracy.
3Reliability
If comprehensive object detection is performed, then reliability of driving decisions is improved, but productivity and processing speed decrease
Solution Approach 1:
The system performs selective detection by activating only the necessary subset of narrow AI agents based on current scene requirements. This partial action approach maintains reliable detection for relevant objects while skipping unnecessary detections, thereby improving processing speed and productivity without compromising driving decision reliability for critical objects.
Solution Approach 2:
The perception system dynamically adapts its processing by activating different combinations of narrow AI agents based on real-time driving context and scene complexity. This dynamic approach ensures reliable detection when needed while optimizing processing speed by reducing computational load in simpler scenarios, achieving a balance between reliability and productivity.
Data Source
AI summary
A method for operating an ensemble of narrow AI agents related to a vehicle, the method may include obtaining one or more sensed information units; determining, by a perception unit and based on the one or more sensed information units, one or more relevant narrow AI agents of the ensemble that are relevant to a processing of the one or more sensed information units; wherein the ensemble is relevant to a first plurality of scenarios; processing the one or more sensed information units, by the one or more relevant narrow AI agents, to provide one or more narrow AI agent driving decisions; wherein each narrow AI agent is relevant to a respective fraction of the first plurality of scenarios; and processing, by a driving decision unit, the one or more narrow AI agent driving decisions to provide an output driving decision.