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Development of a Scalable and Maintainable LIDAR Device Testing Management System with Modular Architecture and Cloud Integration

altoroslabs.com

Automotive

Mining

Challenges with Legacy Testing Systems and Scalability Limitations

The client faced critical challenges with their LabVIEW-based testing system, including vendor lock-in risks, lack of maintainability due to scarce expertise, inability to scale testing scenarios, and error-prone processes during LIDAR device validation for autonomous vehicles.

About the Client

Australian-based manufacturer of proprietary LIDAR scanning devices for automotive and mining industries, seeking to enhance testing processes for self-driving vehicle integration

Key Objectives for the New Testing System

Build a modular test lifecycle management system for LIDAR devices
Validate seamless integration with existing Microsoft Azure ecosystem
Support 30+ testing scenarios (expandable on demand) under diverse environmental conditions
Implement real-time monitoring with automated error detection
Ensure future-proof architecture for proprietary system development

Core System Functionalities and Features

Test scenario launcher with start/stop/cancel functionality
Automated system updates via launcher module
Real-time monitoring dashboard with error detection alerts
Centralized logging system for test events
Visual reporting engine using AngleSharp for HTML reports
REST API integration with Azure cloud analytics

Technology Stack Requirements

WPF (Windows Presentation Foundation)

Microsoft Azure

REST API

AngleSharp

Critical System Integrations

Microsoft Azure cloud ecosystem
Existing embedded LIDAR testing hardware

Architectural Priorities

High availability for continuous testing operations
Horizontal scalability for new testing scenarios
Modular design for easy maintenance/upgrades
Low-latency error detection mechanisms
Standardized data format unification across devices

Expected Business and Technical Outcomes

The new system will enable reliable validation of LIDAR technology for autonomous vehicles through 30+ expandable environmental simulations, reduce vendor lock-in risks through modular architecture, improve testing accuracy via real-time error detection, and provide a foundation for future proprietary development while maintaining Azure ecosystem compatibility.