From Components to Competences: The Intelligence Architecture of Future Mobility

Automotive Engineering Skills and Job Roles of the Future?

2020-01-01
Jakub Stolfa, Svatopluk Stolfa, Richard Messnarz, Omar Veledar, Damjan Ekert, Georg Macher, Utimia Madaleno
Summary
Problem
Method
Results
Takeaways
Abstract

This paper introduces a comprehensive "Intelligence Architecture" for future automotive engineering, developed under the EU's DRIVES project. It maps out a strategic framework of new job roles and skill sets—ranging from AI experts to cybersecurity engineers—necessary to support the transition toward smart, clean, and autonomous vehicles.

TL;DR

The automotive industry is undergoing a paradigm shift where the "soul" of the car is moving from the engine to the algorithms. This paper outlines the DRIVES Framework, a strategic blueprint that redefines the vehicle not as a collection of parts, but as a product of specialized human intelligence. It introduces over 30 new job roles—from AI Ethics to Sensor Fusion—essential for the 2030 autonomous era.

Background: The End of the Traditional Supply Chain

In the classic automotive model, OEMs (Original Equipment Manufacturers) dictated specifications to a tiered supply chain. However, the rise of "Smart, Clean, and Autonomous" demand has rendered this hierarchy obsolete. As seen with partnerships like Google and Renault or Sony’s Vision-S concept, the future car is a Competence-Based Architecture. The bottleneck is no longer factory capacity, but the availability of a workforce skilled in cross-domain mechatronics and digital twins.

Methodology: The Intelligence Architecture

The authors propose a multi-view architecture that integrates human skills directly into the product lifecycle.

1. Engineering and R&D (The Brain)

This layer moves beyond mechanical design into specialized digital domains:

  • E2 & E4: AI and Machine Learning Experts: Transitioning "Black Box" models into trustworthy, explainable systems for autonomous driving.
  • E5: Sensor Fusion: The critical ability to merge Lidar, Radar, and Camera data into a single environmental reality.
  • E9-E11: The Cybersecurity Trinity: Moving security from an "afterthought" to a core design requirement involving managers, engineers, and testers.

Intelligence architecture in engineering Figure 1: The proposed Intelligence Architecture, showing the integration of cross-functional skill pools into the engineering phase.

2. Digital Production and Maintenance

The factory of the future requires Robotics Engineers (P6) who understand software simulation and Predictive Maintenance Experts (M3) who use run-time data streams and similarity queries to detect faults before they occur.

Critical Insight: Why This Matters

The most striking takeaway is the "Knowledge-Based Strategy." The paper argues that to stay competitive, European carmakers must transform their development strategy into a Knowledge Intelligence Architecture.

Intelligence areas (DRIVES job roles) in engineering Figure 2: Specific job roles mapped to intelligence areas, highlighting the heavy emphasis on ADAS, AI, and Connectivity.

Conclusion and Future Outlook

The DRIVES project isn't just an academic exercise; it's a survival guide for an industry facing a "skills gap" crisis. By standardizing these roles across Europe via digital badges, the framework enables a fluid, highly-specialized labor market.

Limitations: While the framework is robust, its success depends on the speed of Academic and VET (Vocational Education and Training) providers to update their curricula—a process that historically lags behind the pace of Silicon Valley innovation.

The Takeaway for Engineers: If you are in the automotive sector, your value is shifting from "knowing the part" to "managing the data and cross-domain implications." Skills in Automotive SPICE, Functional Safety (ISO 26262), and Cybersecurity are no longer optional—they are the new fundamentals.

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Contents
From Components to Competences: The Intelligence Architecture of Future Mobility
1. TL;DR
2. Background: The End of the Traditional Supply Chain
3. Methodology: The Intelligence Architecture
3.1. 1. Engineering and R&D (The Brain)
3.2. 2. Digital Production and Maintenance
4. Critical Insight: Why This Matters
5. Conclusion and Future Outlook