Crowd-Sourced Validation Engine for Autonomous Vehicle Software Updates
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Solution Overview
Problem
Current technical computing environments lack efficient mechanisms for crowd-sourced validation and software updates, particularly in complex systems like autonomous vehicles, where human computation and validation are essential for ensuring safety and quality assurance.
Innovation Solution
A technical computing environment (TCE)-based platform that utilizes a collaborative engine to receive user requests, process them into actionable formats, and generate software updates, incorporating crowd-validation through human computation, incentivizing users to contribute by providing rewards and utilizing a network for developer collaboration and information sharing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional programming environments are used for developing complex systems like autonomous vehicles, then developers have full control over code development, but the process becomes time-consuming and lacks efficient crowd-sourced validation mechanisms
Solution Approach 1:
The patent segments the software validation process into multiple independent test cases that can be distributed to different users. Each user receives a portion of the overall validation task, allowing parallel processing and significantly reducing total validation time while maintaining comprehensive coverage of the software system.
Solution Approach 2:
The patent implements automated feedback mechanisms where test results are immediately processed and communicated back to developers. This continuous feedback loop enables rapid iteration and validation, transforming the traditional sequential development-validation cycle into a concurrent process that accelerates productivity.
2Reliability
If manual validation processes are used for software updates, then quality control can be maintained, but the process lacks automation and efficiency
Solution Approach 1:
The patent enables the validation system to perform self-service through automated test execution and result analysis. The system automatically generates test cases, executes them across multiple platforms, collects results, and provides comprehensive validation reports without requiring manual intervention, thereby maintaining high quality assurance while achieving full automation.
Solution Approach 2:
The patent replaces manual mechanical validation processes with automated computational systems. Instead of human testers manually executing test scenarios, the system uses automated agents to perform validation tasks, eliminating human error and increasing both automation level and reliability simultaneously.
3Reliability
If crowd-sourced validation is implemented, then validation coverage and reliability improve, but system complexity and coordination overhead increase
Solution Approach 1:
The patent introduces an intermediary platform that manages the complexity of crowd-sourced validation. This intermediary system coordinates between multiple users, distributes test cases, aggregates results, and provides unified reporting, thereby enabling reliable crowd-sourced validation while hiding the coordination complexity from individual participants and the overall system.
4Adaptability or versatility
If conventional development environments are used, then developers maintain full control over the process, but collaboration and information sharing among developers are limited
Solution Approach 1:
The patent creates a universal platform that serves multiple functions: code distribution, automated validation, result aggregation, and collaborative communication. This multi-functional system enables diverse collaboration activities (pair programming, code review, joint validation) without requiring separate tools for each function, thereby enhancing adaptability while managing complexity through integration.
Data Source
AI summary
Technologies for customized crowd-sourced update and validation include a computing device having a technical computing environment (TCE)-based engine that receives user information from one or more user devices, executes a TCE model with the user information to generate behavior data of the TCE model, and generates a software update for the TCE model based on the behavior data. The TCE model may be a model for an autonomous system such as a self-driving vehicle, and the software update may be a safety update for the autonomous vehicle. The user information may include sensor data, such as distance detection sensor data. The computing device may transmit an incentive such as a software update, feature update, or safety software update to the user devices. The computing device may also receive information associated with the TCE model from one or more developer devices. Other embodiments are described and claimed.