Processing Circuit On-the-fly Configuration for ADAS Data Security
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
As advanced driver assistance systems (ADAS) and autonomous vehicle (AV) systems progress towards fully autonomous operation, there is a need to protect the data generated by these systems effectively.
Innovation Solution
The system includes one or more cameras and an associated processor that monitor the vehicle's environment, processing images to train neural networks or deep learning algorithms to estimate a future path of the vehicle, thereby enhancing autonomous navigation and driver assist technologies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data protection measures are implemented in ADAS and AV systems, then data security is improved, but system complexity increases
Solution Approach 1:
The system divides data protection into separate functional modules: a configuration manager for secure configuration storage, a processing circuit for encrypted data processing, and a dedicated security layer for key management. This segmentation isolates security functions from main processing, reducing overall system complexity while maintaining comprehensive data protection.
Solution Approach 2:
A configuration manager acts as an intermediary between secure configuration storage and processing circuits. This mediator handles all security-sensitive operations including key generation, encryption, and configuration updates, thereby protecting data without requiring security logic to be distributed throughout the entire system.
2Measurement precision
If neural networks are trained with more environmental data, then path estimation accuracy is improved, but data processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-training neural networks with extensive environmental data during system initialization or offline periods. Pre-trained models and lookup tables are stored in configuration memory, allowing the processing circuit to make rapid path estimations during actual operation without requiring real-time processing of large datasets.
Solution Approach 2:
The system uses partial action by implementing a two-stage processing approach: first using pre-trained models for quick initial path estimation, then selectively refining results with additional data processing only when confidence thresholds are not met. This avoids processing all possible data in every situation while maintaining high accuracy when needed.
Data Source
AI summary
A method for on-the fly updating of a processing circuit, the method includes monitoring, by multiple coroutines and during a monitoring period, a progress of multiple suspend-update-resume sequences executed by the processing circuit, wherein at least some of the multiple execute and suspend-update-resume sequences partially overlap and are not mutually synchronized, and wherein each suspend-update-resume sequence comprises on-the-fly updates; and determining, by a merged coroutine, timings of the multiple suspend-update-resume sequences, wherein the determining comprises performing multiple calculation iterations, wherein a calculation iteration of the multiple calculation iterations comprises calculating, in a an iterative manner, a timing of a next suspend-update-resume sequence to be executed out of the multiple suspend-update-resume sequences, and wherein the calculating is responsive to timing offsets between different suspend-update-resume sequences.