Singapore, 23 October 2025 – Grab Holdings Ltd., Southeast Asia’s biggest ride-hailing and delivery platform, is making a strategic move into autonomous mobility by investing in U.S.-based self-driving technology specialist May Mobility Inc. as part of a broader partnership to introduce robotaxi services in the region by 2026, subject to regulatory approvals.
Under the agreement, Grab will provide May Mobility with access to its mapping data and routing infrastructure, enabling the U.S. company to adapt its autonomy system for Southeast Asia’s unique traffic, road layouts and driver-behaviour conditions, such as left-hand driving. May Mobility, for its part, will integrate its autonomous driving stack into Grab’s fleet-management and passenger-matching platforms, aligning self-driving capabilities with Grab’s existing mobility ecosystem.
The move reflects Grab’s long-term ambition to shift from being purely ride-hailing and delivery-focused to becoming a broader “mobility services” provider, leveraging autonomous vehicles (AVs) to gain a competitive edge and reduce reliance on human drivers. Meanwhile, May Mobility gains regional market access and a strategic partner deeply embedded in Southeast Asia’s transportation infrastructure.
Industry observers say the timing is significant: Southeast Asia’s rapid urbanisation, rising congestion and aggressive push for smart-city infrastructure make it a high-potential market for robotaxi deployment. However, the path to regular commercial service remains contingent on regulatory frameworks, local infrastructure upgrades, consumer acceptance and operational safety validation.
Regulators in Singapore and across the region will be closely watching how the partnership navigates these challenges. For Grab, the robotaxi initiative offers a potential cost-structure transformation deeper into “fleet as a service” models. For May Mobility, partnering with a regional heavyweight like Grab could fast-track its global expansion beyond the United States.
Key Takeaways
- Strategic Investment & Collaboration: Grab’s undisclosed-amount investment in May Mobility is part of a multiyear collaboration combining Grab’s regional strength with May Mobility’s self-driving technology.
- Regional Adaptation Focus: The partnership emphasises adapting technology for Southeast Asia, especially in mapping, left-hand driving conditions and integrating with Grab’s existing mobility network.
- Robotaxi Launch Horizon: The target of a robotaxi roll-out by 2026 sets a clear, ambitious timeline, highlighting how quickly the mobility sector is evolving in Asia.
- Regulatory & Infrastructure Hurdles: While the ambition is high, major factors remain unresolved, including local laws governing driverless vehicles, insurance frameworks, consumer trust, and appropriate urban infrastructure.
- Implication for Mobility Ecosystem: If successful, this venture could reshape Southeast Asia’s mobility landscape, reducing the cost of ride-hailing, enabling new transport models and accelerating smart-city deployment.
Why It Matters for Asia
For Asia, and particularly Southeast Asia, this development is a key signal that the future of mobility is shifting. Rather than simply importing Western AV models, regional players are aligning global technology with local market realities. The Grab-May Mobility partnership reflects that convergence: global tech meets local scale.
Cities in the region face deep traffic congestion, growing demand for efficient public and private transport, and intensifying pressure on sustainability and emissions. Robotaxi services, if implemented safely and effectively, could help relieve urban pressure, provide affordable mobility, and underpin next-generation mobility infrastructure.
The collaboration also underscores Asia’s attractiveness for mobility innovation. With heavy investment flows, agile regulation and large unmet transport demand, Southeast Asia is emerging as a proving ground for new mobility solutions. Partnerships like this one could accelerate the region’s transformation and help it leapfrog legacy transport models.
