In-Memory Content Classification for Autonomous Vehicles
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Solution Overview
Problem
Current autonomous driving systems lack effective mechanisms to filter out unwanted media content in real-time, potentially exposing passengers to objectionable material during autonomous vehicle operations.
Innovation Solution
Implementing an artificial neural network (ANN) within a vehicle's memory device to analyze and filter media content in real-time, using convolutional neural networks (CNN), deep neural networks (DNN), or spiking neural networks (SNN) to identify and modify or mask unwanted content before presentation, with customizable filtering based on the vehicle's occupants.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If media content is streamed in real-time during autonomous vehicle operations, then passengers have access to entertainment and information, but unwanted or objectionable content may be exposed to passengers
Solution Approach 1:
The system performs preliminary classification of media content using an artificial neural network before the content is presented to passengers. The ANN analyzes incoming media streams in real-time, identifying potentially objectionable content based on predefined criteria such as violence, pornography, or political sensitivity, and blocks or modifies such content before it reaches the passenger display devices.
Solution Approach 2:
The patent introduces an intermediary content classification system between the media source and the passenger display. This intermediary layer, implemented through the ANN-based filtering mechanism, acts as a mediator that selectively permits or blocks content based on its analysis, thereby protecting passengers from harmful content while maintaining access to appropriate material.
2Reliability
If content filtering is implemented to protect passengers, then passenger safety and comfort are enhanced, but system complexity increases
Solution Approach 1:
The content classification system operates autonomously using the artificial neural network to automatically analyze and classify media content without requiring manual intervention. The system self-adjusts its filtering based on the detected content characteristics and predefined safety criteria, reducing the need for complex manual configuration and monitoring infrastructure.
3Measurement precision
If artificial neural network is used for content classification, then content filtering accuracy is improved, but computational requirements and processing time increase
Solution Approach 1:
The system applies partial action by focusing the neural network's analysis on specific content features and classification categories relevant to safety concerns rather than analyzing every aspect of the content in equal detail. This selective approach maintains high accuracy for safety-critical classifications while reducing overall computational burden and energy consumption.
Data Source
AI summary
Systems, methods and apparatuses to classify and/or control content passing through a memory device. For example, a portion of a media stream received from a content source can be buffered in a memory device a predetermined time before presentation. An artificial neural network (ANN) in the memory device can identify a region in the buffered portion and analyze the region to determine a classification of content in the region. Within the memory device, the content in the region can be transformed according to a preference specified for the classification. For example, unwanted or objectionable content can be masked, distorted, skipped, replaced, and/or filtered. A modified version of the portion is generated from transforming the content in the region as output for presentation.