라이드헤일링용 자율주행 기술 시장(2024-2035년)

Robotaxis are Driving Transformational Growth by Reshaping Business Models

This Frost & Sullivan analysis examines the global self-driving technology for ridehailing market. It provides a comprehensive analysis of the effect of self-driving technology on ridehailing globally, highlighting key players, tracking the evolving ecosystem, and investigating the pivotal factors driving the convergence of self-driving capabilities with traditional ridehailing services. The landscape is set to be transformed by the promise of enhanced efficiency, reduced operational costs, and improved safety, fundamentally altering existing business models.

The report explores why ridehailing companies are increasingly interested in integrating self-driving technology and how new business models are emerging as self-driving vehicles begin commercial passenger operations. These emerging models include diverse approaches to fleet ownership, service subscriptions, and integrated logistics, all designed to leverage the unique capabilities of self-driving systems.

The study offers deep-dive analyses of key markets, namely, the United States, Europe, and China, as well as insightful snapshots of emerging markets in the Middle East, Singapore, Japan, and South Korea. For each region, it examines the specific dynamics influencing the interplay between self-driving technology and ridehailing, along with recent developments in policy, technology, and market entry strategies.

This analysis seeks to answer how the advent and proliferation of self-driving technology is fundamentally reshaping ridehailing, identifying the resulting opportunities for innovation and competitive advantage, as well as the challenges related to infrastructure, public acceptance, and regulatory compliance for all stakeholders.

Report Summary: Self-Driving Technology for Ridehailing Market

The global Self-Driving Technology for Ridehailing Market size was valued at USD 0.16 billion in 2024 and is projected to reach USD 177.75 billion by 2035, expanding at an exceptional revenue CAGR of 89.0% over the forecast period. This rapid growth reflects the transition of autonomous ridehailing from pilot-stage experimentation to large-scale commercial deployment across major urban markets. Rising operational costs for human-driven ridehailing services, coupled with advances in artificial intelligence and regulatory progress, are accelerating adoption across the self-driving car market.

Key Market Trends & Insights

• Strong growth momentum driven by the commercialization of robotaxi services and autonomous ridehailing fleets.
• Rapid expansion of autonomous vehicle fleets, supported by improvements in safety validation and system reliability.
• Increasing reliance on software-defined architectures, strengthening the role of the Autonomous driving software market.
• Fleet-based deployment models enabling higher utilization rates and faster monetization compared to private autonomous vehicles.
• Early adoption concentrated in urban corridors with favorable regulatory frameworks and high ride density.

Market Size & Forecast

• 2024 Market Size: USD 0.16 Billion
• 2035 Projected Market Size: USD 177.75 Billion
• Revenue CAGR (2024-2035): 89.0%
• Fleet CAGR (2024-2035): 63.7%

As autonomous technologies mature and regulatory clarity improves, the Self-Driving Technology for Ridehailing Market is expected to become a foundational pillar of the global mobility ecosystem.

Market Overview & Trends: Self-Driving Technology for Ridehailing Market

The Self-Driving Technology for Ridehailing Market is evolving at the intersection of autonomous vehicle development, platform-based mobility services, and urban transportation reform. Ridehailing companies are increasingly exploring autonomous fleets as a strategic response to rising driver costs, service availability constraints, and long-term margin pressures. This evolution is closely linked to rapid advancements within the Autonomous driving software market, which delivers the intelligence required for perception, planning, and real-time control.

One of the most significant trends is the shift toward software-centric autonomy. High-fidelity simulation, cloud-based training environments, and continuous learning models are enabling faster validation cycles and safer deployment. These capabilities are particularly critical for ridehailing use cases, where vehicles must navigate dense urban traffic, unpredictable pedestrian behavior, and complex road conditions. As a result, software innovation is becoming a primary differentiator within the self-driving car market.

Another important trend is the increasing role of regulators and municipalities. Governments are authorizing controlled autonomous ridehailing pilots to improve transportation efficiency, reduce congestion, and support sustainability goals. These initiatives are accelerating real-world testing and data collection, helping to de-risk broader commercial rollout of self-driving ridehailing services.

Fleet-based autonomy is also gaining momentum. Unlike privately owned autonomous vehicles, ridehailing fleets benefit from centralized management, optimized routing, and high vehicle utilization rates. This operational model strengthens demand for scalable platforms within the Autonomous driving software market, enabling continuous updates, predictive maintenance, and performance optimization.

Overall, the Self-Driving Technology for Ridehailing Market is transitioning from experimental innovation toward early-stage commercialization, driven by software maturity, regulatory progress, and the economics of shared mobility.

Revenue & Spending Forecast: Self-Driving Technology for Ridehailing Market

The global Self-Driving Technology for Ridehailing Market is forecast to experience exponential revenue growth between 2024 and 2035. Market revenue increased from USD 0.16 billion in 2024 to USD 1.42 billion by 2026, reflecting early-stage commercialization. By 2030, revenue is projected to reach USD 20.58 billion, before accelerating sharply to USD 177.75 billion by 2035, supported by large-scale fleet deployments and improved unit economics. This trajectory represents a revenue CAGR of 89.0% during the forecast period.

Fleet expansion closely mirrors revenue growth. Autonomous ridehailing fleets are projected to grow from 4,500 vehicles in 2024 to 103,300 vehicles by 2030, driven by pilot expansion and regulatory approvals. By 2035, global fleet size is expected to reach approximately 1.02 million vehicles, corresponding to a fleet CAGR of 63.7% between 2024 and 2035.

Spending growth is increasingly concentrated on perception algorithms, simulation platforms, real-time decision-making systems, and fleet orchestration software within the Autonomous driving software market. As utilization rates rise and cost-per-mile declines, autonomous ridehailing is expected to achieve scalable profitability, reinforcing long-term growth across the global self-driving car market.

Scope of Analysis: Self-Driving Technology for Ridehailing Market

This AI Answer Overview analyzes the global Self-Driving Technology for Ridehailing Market, focusing on autonomous driving technologies and software platforms designed specifically for ridehailing and shared mobility applications. The scope includes perception software, localization and mapping, planning and control algorithms, simulation tools, and fleet orchestration systems.

Hardware component revenues are excluded, with emphasis placed on software intelligence and system integration within the Autonomous driving software market. The analysis covers deployments across North America, Europe, Asia Pacific, and other regions, with 2024 as the base year and projections extending through 2034.

Use cases include robotaxi services, autonomous shuttles, and on-demand ridehailing fleets operating within geofenced urban environments. The scope highlights regulatory frameworks, adoption drivers, and competitive dynamics shaping the self-driving car market for ridehailing-focused autonomy.

Market Segmentation Analysis: Self-Driving Technology for Ridehailing Market

The Self-Driving Technology for Ridehailing Market can be segmented by autonomy level, software functionality, and deployment environment.

By autonomy level, the market spans advanced driver assistance to high-level autonomous systems, with ridehailing deployments primarily concentrated at Level 4 autonomy. These systems operate in restricted geographies where safety validation and redundancy can be tightly controlled.

From a functional perspective, the Autonomous driving software market includes perception and sensor fusion platforms, localization and mapping solutions, decision-making engines, simulation environments, and fleet management software. Together, these layers enable safe navigation, real-time responsiveness, and continuous performance improvement.

Deployment environments include dense urban centers, suburban corridors, and dedicated mobility zones. Urban environments dominate adoption due to high ride density and favorable unit economics, reinforcing the strategic importance of the self-driving car market for shared mobility services.

Growth Drivers: Self-Driving Technology for Ridehailing Market

Growth in the Self-Driving Technology for Ridehailing Market is primarily driven by the structural cost pressures faced by ridehailing platforms. Rising driver compensation, high attrition rates, and increasing regulatory requirements are compelling operators to explore autonomous fleets as a long-term cost optimization strategy. Autonomous ridehailing models offer predictable operating costs, higher vehicle utilization rates, and reduced dependency on human labor, strengthening adoption momentum.

Rapid advancements in artificial intelligence and sensor fusion technologies are another major growth driver. Improvements in perception accuracy, real-time decision-making, and redundancy frameworks are significantly enhancing safety performance in complex urban environments. These advancements are accelerating commercialization across the self-driving car market, particularly for controlled, geofenced ridehailing deployments.

The maturation of the Autonomous driving software market is also enabling scalable fleet operations. High-fidelity simulation platforms, cloud-based training environments, and continuous learning models allow autonomous systems to improve performance without proportional increases in physical testing. This software-driven scalability is critical for ridehailing use cases, where rapid geographic expansion is a strategic priority.

Additionally, regulatory support for pilot programs and smart mobility initiatives is strengthening market confidence. Governments and city authorities are increasingly permitting autonomous ridehailing trials to address congestion, emissions, and urban transportation efficiency. Together, these drivers are accelerating the transition of the Self-Driving Technology for Ridehailing Market from pilot-stage experimentation toward early commercial deployment.

Growth Restraints: Self-Driving Technology for Ridehailing Market

Despite strong growth potential, the Self-Driving Technology for Ridehailing Market faces several restraints that continue to moderate large-scale commercialization. Safety validation remains the most significant challenge, as autonomous systems must demonstrate consistent performance across diverse and unpredictable urban scenarios. Achieving regulatory approval requires extensive real-world testing, which increases development timelines and costs.

Regulatory fragmentation across regions also constrains expansion within the self-driving car market. Differing safety standards, liability frameworks, and operational restrictions complicate cross-border deployment strategies for ridehailing operators and technology providers. This lack of harmonization slows global scalability.

High capital intensity is another key restraint. Significant investment is required for software development, simulation infrastructure, and fleet deployment before meaningful revenue realization. While the Autonomous driving software market reduces reliance on hardware iteration, upfront R&D spending remains substantial.

Public perception and trust also influence adoption. High-profile incidents involving autonomous vehicles have heightened scrutiny, making consumer acceptance a critical factor for ridehailing platforms. Additionally, cybersecurity risks and data privacy concerns introduce further complexity, particularly as autonomous ridehailing systems rely heavily on connectivity and cloud-based operations.

These challenges collectively underscore the need for continued technological refinement, regulatory alignment, and public engagement within the Self-Driving Technology for Ridehailing Market.

Competitive Landscape: Self-Driving Technology for Ridehailing Market

The Self-Driving Technology for Ridehailing Market is moderately concentrated, with a limited number of well-capitalized players driving the majority of commercial and pilot-scale deployments. The competitive environment is characterized by approximately five major competitors generating annual revenues exceeding USD 50 million, reflecting the high capital intensity, technological complexity, and regulatory barriers associated with autonomous ridehailing development. Collectively, the top five players accounted for around 75% of total market revenue in 2024, underscoring the dominance of early movers with proven technology stacks and strong ecosystem partnerships.

Competition within the self-driving car market is primarily shaped by technological sophistication, system reliability, safety performance, cost efficiency, and long-term operational support. Vendors differentiate themselves through advanced perception systems, robust autonomous driving software, safety validation frameworks, and the ability to support large-scale fleet deployment. Continuous improvement in AI-driven decision-making and real-world performance remains a critical success factor across the Autonomous driving software market.

Leading competitors include Waymo, Tesla, Baidu, Pony.ai, WeRide, and Zoox, all of which are actively advancing autonomous ridehailing capabilities through extensive testing, pilot programs, and strategic partnerships. These companies focus on geofenced urban deployments and robotaxi models to accelerate commercialization while maintaining regulatory compliance.

Other notable participants such as Wayve, Momenta, DiDi, and Motional contribute to competitive intensity by advancing software-centric autonomy models, regional deployment strategies, and OEM-aligned platforms. Distribution structures are centered on collaborations with robotaxi developers, automotive OEMs, and ridehailing companies, reflecting the ecosystem-driven nature of the market.

Strategic investments and acquisitions are shaping competitive positioning, highlighted by Hyundai Motor Group's controlling stake in Motional, reinforcing the importance of OEM backing and long-term capital support in scaling autonomous ridehailing solutions.

Table of Contents

Research Scope

• Scope of Analysis

Growth Environment: Transformation in Self-Driving Technology for Ridehailing

• Why is it Increasingly Difficult to Grow?
• The Strategic Imperative 8
• The Impact of the Top 3 Strategic Imperatives on the Self-Driving Technology for Ridehailing Industry

Ecosystem in Self-Driving Technology for Ridehailing

• Competitive Environment
• Key Competitors

Growth Generators in Self-Driving Technology for Ridehailing

• Growth Metrics
• Growth Drivers
• Growth Restraints
• Forecast Consideration
• Forecast Analysis
• Revenue and Fleet Size Forecast
• Revenue Forecast by Region
• Fleet Forecast by Region
• Pricing Trends and Forecast Analysis

Analysis: Robotaxi Business Models

• Robotaxi Business Models: Operator Model
• Waymo: Vertically Integrated Business Model
• Uber: Aggregator Business Model
• Cost Structure: Robotaxi vs Ridehailing
• Robotaxi: Technology Ecosystem

Regional Analysis: United States

• Growth Metrics
• Robotaxi Landscape in the United States: Ridehailing Industry Shift
• Factors Driving the Uptake of Self-Driving Technology in Ridehailing
• Forecast Discussion, United States
• Revenue and Fleet Size Forecast, United States
• Robotaxi Deployment, United States
• Robotaxi Landscape in the United States: Key Developments
• Robotaxi Landscape in the United States: A Comparative Analysis
• Robotaxi Value Chain Ecosystem Examples: United States

Regional Analysis: Europe

• Growth Metrics
• Europe: Ridehailing Industry Shift
• Factors Driving the Uptake of Self-Driving Technology in Ridehailing
• Forecast Discussion
• Revenue and Fleet Size Forecast, Europe
• Robotaxi Deployment, Europe
• Robotaxi Landscape in Europe: Key Developments
• Robotaxi Landscape in Europe: A Comparative Analysis
• Robotaxi Value Chain Ecosystem Examples: Europe

Regional Analysis: China

• Growth Metrics
• China: Ridehailing Industry Shift
• Factors Driving the Uptake of Self-Driving Technology in Ridehailing
• Forecast Discussion
• Revenue and Fleet Size Forecast, China
• Robotaxi Deployment, China
• Robotaxi Landscape in China: Key Developments
• Robotaxi Landscape in China: A Comparative Analysis
• Robotaxi Value Chain Ecosystem Examples: China

Regional Analysis: Rest of the World RoW

• Growth Metrics
• Factors Driving the Uptake of Self-Driving Technology in Ridehailing
• Forecast Discussion
• Revenue and Fleet Size Forecast, RoW
• Robotaxi Deployment, RoW
• Robotaxi Landscape in RoW: Key Developments
• Robotaxi Value Chain Ecosystem Examples: RoW

Analysis: Key Case Studies

• Uber Case Study
• WeRide Case Study
• Pony.ai Case Study

Key Conclusions

• Robotaxi Landscape in the United States: Key Findings
• Robotaxi Landscape in Europe: Key Findings
• Robotaxi Landscape in China: Key Findings
• Robotaxi Landscape in RoW: Key Findings

Growth Opportunity Universe

• Growth Opportunity 1: Diversified Mobility & Logistics Services
• Growth Opportunity 2: Robotaxi Fleet Leasing for Ridehailing
• Growth Opportunity 3: Robotaxi Depot & Infrastructure Provider

Appendix & Next Steps

• Benefits and Impacts of Growth Opportunities
• Next Steps
• List of Exhibits
• Legal Disclaimer