autonomous cars Archives

Introduction

Connected cars have been around for about two decades. GM first launched its OnStar in-vehicle communications service in 1996. Although the vast majority of the 1.4 billion cars on the world’s roads still lack embedded cellular connectivity, there is growing demand from drivers for wireless safety and security features, and streamed entertainment and information services. Today, many people simply use their smartphones inside their cars to help them navigate, find local amenities and listen to music.

The falling cost of cellular connectivity and equipment is now making it increasingly cost-effective to equip vehicles with their own cellular modules and antenna to support emergency calls, navigation, vehicle diagnostics and pay-as-you-drive insurance. OnStar, which offers emergency, security, navigation, connections and vehicle manager services across GM’s various vehicle brands, says it now has more than 11 million customers in North America, Europe, China and South America. Moreover, as semi-autonomous cars begin to emerge from the labs, there is growing demand from vehicle manufacturers and technology companies for data on how people drive and the roads they are using. The recent STL Partners report, AI: How telcos can profit from deep learning, describes how companies can use real-world data to teach computers to perform everyday tasks, such as driving a car down a highway.

This report will explore the connected and autonomous vehicle market from telcos’ perspective, focusing on the role they can play in this sector and the business models they should adopt to make the most of the opportunity.

As STL Partners described in the report, The IoT ecosystem and four leading operators’ strategies, telcos are looking to provide more than just connectivity as they strive to monetise the Internet of Things. They are increasingly bundling connectivity with value-added services, such as security, authentication, billing, systems integration and data analytics. However, in the connected vehicle market, specialist technology companies, systems integrators and Internet players are also looking to provide many of the services being targeted by telcos.

Moreover, it is not yet clear to what extent the vehicles of the future will rely on cellular connectivity, rather than short-range wireless systems. Therefore, this report spends some time discussing different connectivity technologies that will enable connected and autonomous vehicles, before estimating the incremental revenues telcos may be able to earn and making some high-level recommendations on how to maximise this opportunity.

Executive Summary

The role of cellular connectivity

High level recommendations

Contents

Introduction

The evolution of connected cars

How to connect cars to cellular networks

What are the opportunities for telcos?

How much cellular connectivity do vehicles need?

Takeaways

The size of the opportunity

How much can telcos charge for in-vehicle connectivity?

How will vehicles use cellular connectivity?

Telco connected car case studies

Vodafone – far-sighted strategy

AT&T – building an enabling ecosystem

Orange – exploring new possibilities with network slicing

SoftBank – developing self-driving buses

Conclusions and Recommendations

High level recommendations

STL Partners and Telco 2.0: Change the Game

Figure 1: Incremental annual revenue estimates by service

Figure 2: Autonomous vehicles will change how we use cars

Figure 3: Vehicles can harness connectivity in many different ways

Figure 4: V2X may require large numbers of simultaneous connections

Figure 5: Annual sales of connected vehicles are rising rapidly

Figure 6: Mobile connectivity in cars will grow quickly

Figure 7: Estimates of what telcos can charge for connected car services

Figure 8: Potential use cases for in-vehicle cellular connectivity

Figure 9: Connectivity complexity profile criteria

Figure 10: Infotainment connectivity complexity profile

Figure 11: In-vehicle infotainment services estimates

Figure 12: Real-time information connectivity complexity profile

Figure 13: Real-time information services estimates

Figure 14: The connectivity complexity profile for deep learning data

Figure 15: Collecting deep learning data services estimates

Figure 16: Insurance and rental services’ connectivity complexity profile

Figure 17: Pay-as-you-drive insurance and rental services estimates

Figure 18: Automated emergency calls’ connectivity complexity profile

Figure 19: Automated emergency calls estimates

Figure 20: Remote monitoring and control connectivity complexity profile

Figure 21: Remote monitoring and control of vehicle services estimates

Figure 22: Fleet management connectivity complexity profile

Figure 23: Fleet management services estimates

Figure 24: Vehicle diagnostics connectivity complexity profile

Figure 25: Vehicle diagnostics and maintenance services estimates

Figure 26: Inter-vehicle coordination connectivity complexity profile

Figure 27: Inter-vehicle coordination revenue estimates

Figure 28: Traffic management connectivity complexity profile

Figure 29: Traffic management revenue estimates

Figure 30: Vodafone Automotive is aiming to be global

Figure 31: Forecasts for incremental annual revenue increase by service

The enduring value of connected assets

In the digital economy, the old adage knowledge is power applies as much as ever. The ongoing advances in computing science mean that knowledge (in the form of insights gleaned from large volumes of detailed data) can increasingly be used to perform predictive analytics, enabling new services and cutting costs. At the same time, the widespread deployment of connected devices, appliances, machines and vehicles (the Internet of Things) now means enterprises can get their hands on granular real-time data, giving them a comprehensive and detailed picture of what is happening now and what is likely to happen next.

A handful of companies already have a very detailed picture of their markets thanks to far-sighted decisions to add connectivity to the products they sell. Komatsu, for example, uses its Komtrax system to track the activities of almost 430,000 bulldozers, dump-trucks and forklifts belonging to its customers. The Japan-based company has integrated monitoring technologies and connectivity into its construction and mining equipment since the late 1990s. Komatsu says the Komtrax system is standard equipment on “most Komatsu Tier-3 Construction machines” and on most small utility machines and backhoes.

Komatsu’s machines ship with GPS chips that can pinpoint their position, together with a unit that gathers engine data. They can then transmit the resulting data to a communication satellite, which relays that information to the Komtrax data centre.

The data captured by Komtrax (and other Internet of Things solutions) has value on multiple different levels:

It provides Komatsu with market intelligence
It enables Komatsu to offer value added services for customers
It gives detailed data on the global economy that can be used for computer modelling and to support the development of artificial intelligence

Market intelligence for Komatsu

For Komatsu, Komtrax provides valuable information about how its customers use its equipment, which can then be used to refine its R&D activities. Usage data can also help sales teams figure out which customers may need to upgrade or replace their equipment and when.

Komatsu’s sales and finance departments use the findings, for example, to offer trade-ins and sales of lighter machines where heavy ones are underused. Its leasing firm can also use the information to help find customers for its rental fleet.

Furthermore, Komatsu is linking market information directly with its production plants through Komtrax (see Figure 1). It says its factories “aggressively monitor and analyse the conditions of machine operation and abrasion of components” to enable Komatsu and its distributors to improve operations by better predicting the lifetime of parts and the best time for overhauls.

Figure 1: How Komatsu uses data captured by its customers’ equipment

Source: Komatsu slide adapted by STL Partners

Value added services for customers

The Komtrax system can also flag up useful information for Komatsu’s customers. Komatsu enables its customers to access the information captured by their machines’ onboard units, via an Internet connection to the Komtrax data centre.

Customers can use this data to monitor how their machines are being used by their employees. For example, it can show how long individual machines are sitting idle and how much fuel they are using. Komatsu Australia, for example, says Komtrax enables its customers to track a wide range of performance indicators, including:

Location
Operation map (times of day the engine was on/off)
Actual fuel consumptionAverage hourly fuel consumption
Residual fuel level
High water temperature during the day’s operation
Dashboard cautions
Maintenance reminders/notifications
“Night Time” lock
Calendar lock
Out of Area alerts
Movement generated position reports
Actual working hours (engine on time less idle time)
Operation hours in each work mode (economy, power, breaker, lifting)
Digging hours
Hoisting hours
Travel hours
Hydraulic relief hours
Eco-mode usage hours
Load frequency (hours spent in four different load levels determined by pump pressures or engine torque)

Content:

Introduction
Executive Summary
The enduring value of connected assets
Tapping telecoms networks
Enabling Deep Neural Networks
Real world data: the raw material
Learning from Tesla
The role of telcos
Conclusions and Recommendations

Figures:

Figure 1: How Komatsu uses data captured by its customers’ equipment
Figure 2: Interest in deep learning has risen rapidly in the past two years
Figure 3: Deep learning buzz has helped drive up Nvidia’s share price
Figure 4: The key players in the development of deep learning technology
Figure 5: Mainstream enterprises are exploring deep learning
Figure 6: The automotive sector is embracing Nvidia’s artificial intelligence
Figure 7: Google Photos learns when users correct mistakes
Figure 8: Tesla’s Autopilot system uses models to make decisions
Figure 9: Tesla is collecting very detailed data on how to drive the world’s roads

Tag: autonomous cars

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