7 Business Models for Autonomous Driving
By: Nitin Kumar
Tags: Self Driving Cars, Business Models, Innovation, Autonomous Vehicles, Technology

Image created by author using Pixabay

Understanding business models in autonomous driving

Introduction

Autonomous driving is one of the most disruptive developments witnessed. It will change the way we think of cities, roads, parking, commuting, leisure and ownership, and it will spawn new kinds of jobs as the business models unfold. Many open questions have answers when it comes to business models, infrastructure, technology direction and development.

Image from author’s original article on Forbes

TaaS (Transportation As A Service)

Autonomous driving is one of the most disruptive developments witnessed. It will change the way we think of cities, roads, parking, commuting, leisure and ownership, and it will spawn new kinds of jobs as the business models unfold. Many open questions have answers when it comes to business models, infrastructure, technology direction and development.

Operating System-Enabled Value Chain

Many automotive original equipment manufacturers (OEMs) believe transportation is set for fundamental disruption and will be very different in the future. Some OEMs like BMW sell directly to consumers today but are also experimenting with ridesharing, fleet sales and time splitting, creating transportation as a service (TaaS) business models. On-demand business models will alter profit pool configurations in the transportation industry, with value shifting to technology, data, design and platforms and away from dealerships or hardware.

Operating systems are the heartbeat of any ecosystem, but technology players have monetized them differently. Apple (iOS) charges a premium for hardware, and Google (Android) drives more advertising revenue, while Microsoft (Windows) is monetized directly. Control of the OS is vital to the autonomous driving value chain, as it affects customer experiences and relationships through control.

Both Detroit and Silicon Valley are in a race to create and control this OS. While Tesla is mimicking the iOS model, Baidu is looking at open-source technology to monetize ancillary products/services, and Uber is building a partner ecosystem to create scale. Multiple models will likely emerge, coexist and compete, affecting capitalization, scale, margins, marketing, sales and future investments.

Fleets And Logistics

The Tesla-style business model should be aesthetically appealing given the hardware focus and building an exceptional design, whereas the Baidu model will likely hinge on a cheaper design while driving up accessories, services and support. The right package and quantity of sensors will also depend on the OS strategy adopted, affecting value for ancillary players. Open and closed strategies for the OS (e.g., iOS vs. Android) will also create consequences like safety legislation, competitive dynamics and standardization of infrastructure.

The existing approach to logistics would change with the elimination of constraints around commercial fleets today, as driving hour limitations create better asset utilization, less downtime, faster movement of goods, reduced congestion and improved operating costs. There has already been discussion regarding governments considering autonomous-only zones and a reduced need for parking lots.

Data-Driven Value Chain

New business specialties and opportunities will emerge in areas like urban planning and the management/monitoring of fleets through new players. Fleets create business models around access frequency, usage time or miles consumed. Given the long depreciation cycles of existing fleets and cost factors to create AV fleets, there are semi-autonomous or retrofit capabilities as a short-term revenue stream.

Autonomous vehicles will consume and generate data at unprecedented levels. The data comes from various sources and components like cameras, radars, lidars, GPS, sensors, maps and smart infrastructure. All this data must be aggregated in real time and trained to navigate roads, avoid obstacles, provide safety, comply with rules and create personalized experiences. The volume, velocity and intake of this data will require new infrastructure, software-defined processing capabilities and new business models. For example, many partnerships have evolved to co-create new datasets or have access to it like HD maps.

Software-Defined Value Chain

Initially, humans and machine learning models must train this data — the human-in-the-loop method. While deep learning will occur in the cloud, machine learning executes on the vehicle itself. The ownership, consumption and monetization of all this data will inform business models as multiple new startups emerge only around this data value chain.

Technology companies create value by commoditizing suppliers and monetizing consumers through efficient channels. In transportation, ridesharing companies like Uber and Lyft have also aggregated demand through a channel (platform technology) and commoditized the supply side, making the type of vehicle less important for riders. Uber and Lyft both want to consolidate their positions as the future focal point of transportation by investing in autonomous driving initiatives.

Internet Of Vehicles And V2X

There are many players — like Google, legacy OEMs, new players like Tesla and auto suppliers — competing in the value chain. The electrification of cars and fleets and the viability of new powertrains will have a direct affect on fixed and operating costs of AVs; a new transportation and mobility ecosystem is expected to be faster, cheaper, cleaner, safer, more customized and more efficient in all aspects. The higher end of the value chain will create stronger positions to foundations to accelerate in this nascent but promising sector.

Regulation-Driven Business Models

Business models leveraging multiple modes of C-V2X include infotainment, traffic information, real-time mapping, telematics and data analytics. Network-based data analytics, monitoring and opportunities also exist. While V2I might be delayed due to lack of incentives with local governments to progress and their budget deficits, they will be significant influencers of business models (access to services, access to space, etc.).

Regulators have struggled with Big Tech and vice versa, and rapid technology advances are challenging to keep current. Regulators are important stakeholders in the autonomous driving revolution and influence players to migrate to lower pollution, less congestion and efficient commutes. They also must consider the transition time from human-driven to autonomous driving while factoring a period of coexistence.

The distribution of economic value will be interesting for regulators as the space evolves. For example, if taxes are levied on miles traveled, then staying closer to centers of action will be incentivized. If taxes are driven by hours parked or space occupied, business models will shift to minimize idle time. It will be a “chicken vs. egg” situation between industry guiding regulation and letting regulation direct the future of the industry.

Conclusion

Business models and profit motives will inform technology, regulatory and societal adoption of autonomous driving. This is an ecosystem play with value created outside of traditional automakers, such as technology, media, telecom, insurance, health care, energy and government.

Other Relevant Articles:

1. The Autonomous Driving Technology Stack

2. Autonomous Driving: Why Big Data Value is a Myth

3. Seven Data Challenges in Autonomous Driving

4. Seven Autonomous Driving Business Models

5. 12 Technologies Enabling Autonomous Driving Business Models

6. Pathways to Adoption For Autonomous Driving Infrastructure

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