Industry
Client Overview
A top-tier German automotive manufacturer known for its commitment to engineering excellence and safety. With an ambitious roadmap for Level 3 autonomous driving, the client needed to develop a perception system capable of meeting the highest functional safety standards (ASIL-D) while delivering state-of-the-art performance in complex urban environments. Their internal teams were experts in vehicle dynamics and control systems but faced a steep learning curve and talent gap in certifiable AI software development.
Client Testimonial
"Partnering with CIS was a strategic imperative. Their rigorous, CMMI Level 5 approach to software development, combined with their deep expertise in the ISO 26262 V-model, gave us the confidence to execute our L3 vision. They delivered a system that was not only performant but also fully traceable and auditable. They are a world-class engineering organization." - Director, Autonomous Driving
Problem
The client needed to build a multi-sensor fusion and perception module for their next-generation ADAS platform. The system had to achieve ASIL-D classification, requiring extreme rigor in process, documentation, and validation that their existing software partners could not provide.
Key Challenges
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01
Stringent Safety Requirements : Achieving ASIL-D for a non-deterministic AI system was a monumental challenge.
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Complex Sensor Fusion : Integrating data from cameras, LiDAR, and radar to create a single, reliable environmental model.
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Massive Data Validation : Needing a robust pipeline to process petabytes of driving data and validate model performance against safety goals.
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Traceability & Documentation : Creating a complete, auditable trail from high-level safety goals down to individual lines of code and test cases.
Our Solution
CIS deployed a dedicated POD of 45 engineers, including AI specialists, embedded developers, and certified functional safety managers.
Safety-First Architecture : We co-designed a software architecture with built-in redundancy and fail-operational mechanisms, ensuring that no single failure could compromise the system's safety.
Advanced Sensor Fusion : Developed sophisticated algorithms using extended Kalman filters and neural networks to fuse sensor data, providing high-confidence object detection and tracking even when individual sensors failed or provided conflicting information.
MLOps & Validation Pipeline : We built a scalable MLOps pipeline on AWS to automate data ingestion, labeling, model training, and validation. This included the use of synthetic data generation to test thousands of rare but critical edge-case scenarios.
Automated Compliance Documentation : We utilized internal tools to automate the generation of ISO 26262 work products, maintaining a real-time, bidirectional traceability matrix that linked requirements in Jama to code in Git and tests in our validation suite.
Implementation & Execution
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Safety Target Profiling
Conducted a joint Hazard Analysis and Risk Assessment (HARA) to define top-level safety goals.
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Architecture Decomposition
Decomposed safety goals into functional and technical safety requirements.
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Process-Driven Coding
Implemented the software according to a strict, ASPICE-compliant process.
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Exhaustive Module Coverage
Developed over 10,000 unit and integration tests to achieve 100% code coverage on critical modules.
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Dynamic System Hardening
Performed extensive fault injection testing to validate the system's response to hardware and software failures.
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Regulatory Package Assembly
Delivered a complete safety case package, including all required documentation for submission to regulatory bodies.
Positive Outcome
1. Successful ASIL-D Certification
The client's system successfully passed its functional safety audits, a key milestone for their L3 program.
2. 40% Faster Validation
Our automated MLOps pipeline reduced the time for a full regression test and validation cycle from 5 days to 72 hours.
3. Improved Perception Accuracy
The final model achieved a 15% improvement in detecting vulnerable road users (pedestrians, cyclists) in low-light conditions compared to the previous benchmark.
4. Reusable Safety Framework
The client was able to leverage the architecture and processes we developed as a framework for other safety-critical AI projects across the organization.
Why Choose Us
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Unwavering Process Maturity
Bakes comprehensive documentation into CMMI Level 5 processes to guarantee auditable compliance.
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Full-Spectrum AI Expertise
Deploys niche multi-domain specialists spanning computer vision, MLOps, and deep learning algorithms.
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Guaranteed IP & Data Security
Restricts access via isolated environments backed by secure, 100% in-house engineering teams.
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Flexible, On-Demand Scaling
Provisions adaptive, cross-functional engineering PODs that ramp up instantly based on scope.
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Proven Global Delivery
Leverages round-the-clock software engineering workflows designed for seamless cross-border collaboration.
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Deep Automotive Domain Context
Operates with native knowledge of safety-critical embedded systems, vehicle networks, and compliance.
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Risk-Free Engagement Path
Validates engineering capabilities upfront through 2-week paid trials and scoped pilot proofs.
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Zero-Cost Talent Replacement
Mitigates risk with an immediate resource substitution policy featuring fully subsidized knowledge transfers.
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A True Technology Partner
Consults continuously on technical architecture, platform future-proofing, and executive alignment strategies.
Conclusion
This project demonstrates CIS's unique ability to operate at the intersection of cutting-edge AI and mission-critical functional safety. We enabled our client to achieve a key strategic objective that was previously blocked by internal resource and expertise constraints, solidifying their leadership in the race to autonomy.