Narrow AI Perception Router for Autonomous Driving
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Solution Overview
Problem
Current autonomous vehicle perception systems are computationally intensive and power-consuming due to their need to exhaustively analyze and label raw sensor inputs to identify various objects and entities in the scene, which is inefficient and resource-heavy.
Innovation Solution
Implementing a perception unit that uses an ensemble of narrow AI agents, where only relevant agents are activated based on high-level contextual cues, reducing the need for detailed object detection and localization, thereby lowering power consumption and computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exhaustive object detection and localization is performed to accurately identify all objects and entities in the scene, then measurement precision and reliability are improved, but computational intensity and power consumption increase significantly
Solution Approach 1:
The perception system is segmented into multiple specialized modules: a perception router that performs lightweight contextual analysis, and multiple narrow AI agents (object detection agent, lane detection agent, traffic light agent, etc.) that handle specific detection tasks. This segmentation allows the system to activate only the necessary agents based on scene context, reducing overall computational intensity and power consumption while maintaining detection precision for relevant objects.
Solution Approach 2:
Instead of performing exhaustive detection of all possible objects in every scene, the system performs partial action by activating only the subset of narrow AI agents relevant to the current scene context. The perception router analyzes high-level contextual cues and selectively triggers specific detection agents, thereby reducing computational workload and power consumption while maintaining sufficient detection precision for driving-critical objects.
2Measurement precision
If a complicated computationally-intensive perception model is used to identify and localize all objects, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The monolithic complicated perception model is segmented into a simple perception router and multiple specialized narrow AI agents. Each agent has a specific function (object detection, lane detection, traffic light detection, etc.), making individual agents simpler and more manageable. This segmentation reduces overall device complexity while maintaining or improving localization accuracy through specialized processing.
Solution Approach 2:
The system dynamically adapts its complexity by selectively activating narrow AI agents based on scene context. The perception router continuously analyzes contextual cues and adjusts which agents are active, allowing the system to operate with minimal complexity in simple scenes and scale up complexity only when necessary, thereby reducing average device complexity while maintaining precision when needed.
3Reliability
If exhaustive labeling of raw sensor inputs is performed to provide complete information to route planning, then reliability is improved, but productivity decreases due to resource consumption
Solution Approach 1:
The system extracts only the essential and relevant information needed for reliable driving decisions, rather than performing exhaustive labeling of all sensor inputs. The perception router identifies key contextual elements, and only the corresponding narrow AI agents process and label relevant objects, thereby maintaining driving decision reliability while significantly improving processing efficiency and productivity.
Solution Approach 2:
Instead of labeling all possible objects exhaustively, the system performs partial action by labeling only those objects and entities that are relevant to the current driving context and decision-making. This selective labeling approach maintains sufficient reliability for safe driving while dramatically improving processing efficiency and reducing resource consumption.
Data Source
AI summary
A method for operation of narrow artificial intelligence (AI) agents for at least partially autonomous driving, the method includes: (a) receiving, by multiple perception modules, multi-domain information about elements affecting a vehicle; wherein each one of the multiple perception module is associated with a dedicated domain of the multi-domain information; (b) generating, by the multiple perception modules, class signatures that are indicative of classes of the elements of the multi-domain information; (c) determining a multi-domain identifier that identifies the generated class signatures of the multiple perception modules; and (d) identifying, based on the multi-domain identifier, one or more narrow AI agents that are relevant to a processing of at least a part of the multi-domain information. The identifying triggering execution of further processing the at least a part of the multi-domain information by the identified one or more narrow AI agents to provide one or more narrow AI driving related decision.