As intelligent driving moves from vision to reality, DEEPROUTE.Ai — a company on a mission to "build general artificial intelligence for the physical world" — finds itself at a pivotal crossroads.
The challenges go far beyond technical breakthroughs; they touch on fundamental tensions within the industry. How can end-to-end models with "black box" characteristics meet the stringent compliance standards of the automotive sector? And how can a delicate balance be struck between development speed, product quality, and cost control?
To tackle this paradox, DEEPROUTE.Ai embraced digital tools to connect agile development with the precision required in the automotive world. By integrating ASPICE standards into its algorithm development process through Meegle, the company achieved technical agility while ensuring comprehensive traceability at every stage. This innovative approach offers the industry a practical roadmap for fostering innovation without sacrificing control or compliance.
01 The Core Challenge: Agile Meets Automotive Standards
At this critical stage of bringing intelligent driving to market, DEEPROUTE.Ai faced a dual-front battle. Innovation demanded rapid iteration, yet the automotive sector required exhaustive documentation, verifiable testing, and uncompromising safety.
End-to-end models, by nature, are difficult to explain—a true “black box.” But to pass regulatory gates, every step of development needed to be transparent and traceable. Beyond that, DEEPROUTE.Ai had to tame overwhelming complexity: more than 3,800 pages of documentation and over 5,000 individual requirements, all of which needed to be tracked, validated, and delivered under intense time pressure.
Day-to-day, the team had to walk a tightrope. They needed to master rigid processes so that cross-functional collaboration remained smooth and error-free. At the same time, they had to push the limits of frontier technologies without falling into costly trial-and-error. Meanwhile, they had to keep cycle time, quality, and cost aligned as schedules compressed.
DEEPROUTE.Ai's solution was to merge the V-model with automotive compliance processes and build a bidirectionally traceable development system. Guided by an "API-first"culture, the team restructured workflows: delivery tracks strictly followed ASPICE, while algorithm R&D remained agile and experimental. Clear interface definitions and structured upstream-downstream handoffs kept workflows decoupled, but connected—ensuring innovation never derailed compliance.
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02 Breaking Through: Process Decoupling, Decision Restructuring and Continuous Certification
Faced with a flood of requirements and defect feedback, the team turned to Meegle's ASPICE plug-in and APIs. Requirements were broken down into modular items and then clustered through a tag-based management system.
Within Meegle, dozens of tag categories were created, each with thousands of sub-tags covering functions, scenarios, and modules. This made it possible to quickly match requirements and defects, generate corresponding views, and accelerate issue resolution. Testing issue categories were even converted into training tags for the models themselves—linking defect discovery directly back to development inputs.
More importantly, it established end-to-end traceability: every requirement could be linked to versions, test plans, and defect fixes. Defect resolution status was visible in real time — allowing issues to be resolved almost as soon as they arose.
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03 Sustaining Efficiency in Requirement Management
To accelerate requirement clustering, DEEPROUTE.Ai utilizes Meegle's tagging functionality for systematic requirement organization. By applying customized labels like "traffic lights" and "lateral/longitudinal perception," engineers instantly group related requirements, significantly improving task identification and processing efficiency.
For version control, the ASPICE plug-in automates baseline management through automatic version generation and revision tracking. This tool categorizes requirements by type – including customer needs, software system specifications, and technical requirements – while visualizing status hierarchies. When importing over 3,800 pages of functional specifications, the plug-in transformed documents into structured and traceable items.
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04 Defects as Clues for Quality Improvement
DEEPROUTE.Ai establishes end-to-end linkages between requirements, versions, and defects, treating all defects as valuable improvement assets. Engineers rapidly locate critical issues through scenario-specific tags like "traffic light misidentification" or "omission recognition," enabling perception teams to immediately pinpoint priority defects. These defect records are cross-referenced with version data to inform development iterations. Leveraging Meegle's workflows, the team processes high-priority defects (Class S/P0) within strict timeframes: diagnosis and assignment within 4 hours, root-cause analysis within 12 hours, and delivery of retest-ready fixes within 48 hours. This "zero-overnight-backlog" methodology converts defects into quality enhancement opportunities.
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05 Digital-First, Quality-Driven R&D
DEEPROUTE.Ai's journey proves that digital tools can resolve the tension between breakthrough innovation and regulatory rigor. By uniting agile R&D with ASPICE compliance, the company built a model for synchronized evolution of technology and management. A key enabler of this transformation was Meegle, which served as a bridge between cutting-edge innovation and automotive-grade rigor, making rapid response possible.
As Project Director reflected, "By leveraging Meegle to build a system with end-to-end traceability, we have fundamentally upgraded our R&D management. Every requirement now leaves a digital footprint, and each defect becomes a valuable data asset that drives quality improvements. We can resolve critical issues—from root-cause analysis to fix verification—within just 48 hours. Beyond the significant boost in R&D efficiency, the most notable change is our shift from 'reactive firefighting' to 'proactive prevention'."
This operational leap is already visible on the road. Nearly 100,000 vehicles equipped with DEEPROUTE.Ai's advanced driver-assistance systems are in use today. This year, this number is set to grow dramatically — with over 10 models and more than 200,000 vehicles expected to be delivered, bringing safer and smarter driving experiences to even more people.
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DEEPROUTE.Ai's journey proves that digital tools can resolve the tension between breakthrough innovation and regulatory rigor. By uniting agile R&D with ASPICE compliance, the company built a model for synchronized evolution of technology and management.
As AI continues to reshape mobility, DEEPROUTE.Ai is pushing forward, applying advanced digital practices and AI-driven tooling to make intelligent driving safer, smarter, and scalable. Their story is more than a case study—it is a playbook the entire industry can learn from, where efficiency and quality rise together, and innovation reaches the road without compromise.
