Autonomous Driving: Focus Shifts from 'Expectations of Full Automation' to 'Winning Strategies for Commercialization'

McKinsey & Company (Representative in Japan: Naoyuki Iwatani) has announced the release of its latest white paper, "The Next Evolution of Autonomous Vehicles: Insights from Industry Leaders," which analyzes the perspectives of industry leaders on the deployment timeline, development investment, technology stack, and procurement strategies for autonomous driving technology.

This white paper summarizes the findings of the third edition of the Autonomous Vehicle Industry Leader Survey, conducted by the McKinsey Center for Future Mobility (MCFM) in collaboration with The Autonomous and Ride AI. The survey, conducted in January 2025, included 91 decision-makers from startups, major corporations, universities, and mapping/navigation organizations in the automotive, transportation, and software sectors across Europe, North America, and Asia.

The white paper was jointly authored by Taketo Sumikawa (Senior Partner, Tokyo Office), Takuya Yamashina (Partner, Kansai Office), Ani Kher, Kersten Heineke, and Martin Kellner from the Automotive & Industrial Machinery Research Group, and supervised and translated by Masatake Koizumi (Associate Partner, Tokyo Office) and Sayuri Katsura (C&I Specialist, Tokyo Office).

■ Key Discussion Points Available for Interviews
McKinsey's experts in the automotive and mobility domain are available for background explanations and individual interviews on the following themes:

- Why is the deployment timeline for autonomous driving being pushed back?
- Among robotaxis, L2+/ADAS, and autonomous trucks, which areas will see commercialization first?
- Who will bear the development burden of "over $3 billion" in autonomous driving development?
- What impact will the formation of China's unique technology stack have on Japanese automakers and suppliers?
- What are the differences between E2E AI and hybrid approaches, and what are the issues regarding safety and regulatory compliance?
- What should Japanese companies prioritize in terms of investment, collaboration, and organizational capabilities over the next 3-5 years?

▼ White Paper
https://www.mckinsey.com/jp/~/media/mckinsey/locations/asia/japan/our%20insights/where-to-next-insights-from-autonomous-vehicle-experts_jp_vf.pdf

Autonomous driving is entering a phase where it can no longer be discussed solely in terms of simple expectations about 'when full automation will arrive.' While technological advancements continue, such as the market introduction of L3 vehicles and the expansion of commercial robotaxi operations, the timeline for widespread adoption is being pushed back for some use cases. Uncertainties surrounding development and validation costs, the divergence of technology stacks by region, and safety and regulatory compliance are making strategic decisions for companies more complex.

This white paper visualizes the gap between 'expectations' and 'implementation,' offering perspectives for Japanese automakers, suppliers, and technology companies to consider where to invest, where to collaborate, and which markets offer winning strategies.

■ Key Findings
- For many autonomous driving use cases, the timeline for widespread adoption is delayed by an average of 1-2 years compared to the 2023 survey. Full deployment of L4/L5 robotaxis is predicted for 2030, while pilot testing of urban L4 private vehicles and fully autonomous trucks to final destinations are expected by 2032.
- In the mass-market passenger vehicle segment, 49% of experts anticipate L2+ becoming mainstream by 2035, with only 39% expecting L3 or higher to dominate the market.
- The potential for cost overruns in development increases with higher levels of autonomous driving. Software development alone for robotaxis and fully autonomous trucks could require investments exceeding $3 billion.
- The biggest pain point in ADAS/AD development is "high cost." Software development, integration, testing, validation, and data collection/storage are identified as cost factors with significant room for improvement.
- 61% of experts predict the formation of unique technology stacks in China. Autonomous driving technology is likely to diverge based on regional requirements, regulations, and supply chains, rather than converging on a single global standard.
- In software development, a hybrid approach combining traditional algorithms with E2E (End-to-End) AI systems is expected to become dominant, rather than relying solely on E2E AI systems.

■ Strategic Implications for Japanese Companies
Instead of waiting for the arrival of full automation, companies are urged to prioritize investments based on use cases that realistically generate value in the mass-market vehicle segment (L2+/ADAS), premium vehicles (L3), urban robotaxis, and autonomous trucks.

Furthermore, amidst the intensifying ecosystem competition surrounding software, semiconductors, sensors, mapping/data, and validation platforms, it is crucial for Japanese companies to concentrate on areas where they hold a competitive advantage and leverage collaboration and standardization in non-core areas, rather than attempting to develop everything in-house.

Specifically, the potential divergence of technology stacks and regulatory environments in the US, China, and Europe presents a challenge that compels Japanese automakers to reconsider their traditional model of deploying identical specifications globally. Viewing autonomous driving not merely as technological development but as a business design encompassing customer value, responsibility sharing, safety, and profitability will be critical for future competitive advantage.

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