Glossary and definition 2

Table of contents 4

About the authors 5

Companies cited in this report 6

What we got right, What we got wrong 7

Report scope, objectives and methodology 8

Who should be interested by this report 16

Yole Group related reports 17

Three-Slide summary 20

Executive summary 24

Context 78

Market forecasts 105

• Initial statements
• Artificial Intelligence computing for automotive forecasts
• Cameras for automotive
• Computing hardware for autonomy 2019 market shares
• AI computing for automotive forecasts by segment
• AI computing for automotive forecasts by type of camera
• AI computing for automotive forecasts by type of hardware
• Key points

Market trends 138

• Introduction
• ADAS vehicles
• Robotic vehicles
• Infotainment
• Conclusion

Technology trends 186

• Introduction
• Centralization
• Acceleration
• Infotainment

Ecosystem 232

• Introduction, merge and acquisitions analysis
• Autonomy
• Infotainment

Conclusion 281

• Challenge and stakes
• The value chain follows the data flow
• Summary

Yole Développement presentation 290

WINNERS AND LOSERS IN THE RACE FOR AUTONOMY

Artificial Intelligence (AI) is gradually invading our lives through everyday objects like smartphones, smart speakers, and surveillance cameras. The hype around AI has led some players to consider it as a secondary objective, more or less difficult to achieve, rather than as a central tool to achieve the real objective: autonomy. Finally, after an initial inventory, the players that have grasped this aspect are already leading the race.

The impact of COVID-19 is still uncertain, but we can already affirm that it will have a profound effect – with autonomy-related research likely to slow down at least this year and next due to cash shortages. For a player like Tesla, which has built all of its autonomous stack internally (software and hardware) and thus holds sole ownership, the strategy of moving forward in small steps will pay off since this strategy is not “side research” but rather an integral part of the Tesla project, just like electrification. In fact, the impact of this crisis will probably accentuate Tesla’s market leadership, which is already estimated at several years.

In the second line, we find OEMs which develop their own autonomous software stack, but with hardware provided to them by other industry players. Lack of cash can slow down some programs, but others have been active for several years now and will probably not be stopped. And even if they are delayed, autonomy is an integral part of these OEMs’ middle-term strategies – just as it is for Tesla. As for those companies with speculative programs that do not have the quest for autonomy as a cornerstone, it is very likely that these research programs, if they exist, will at best be postponed until the crisis has disappeared completely. These companies will be the big losers in the autonomy race and will have to rely even more on computing players to provide them with full autonomous solutions / functionalities.

THE MARRIAGE OF AI AND COMPUTING IS THEREFORE AT THE HEART OF THIS RACE

The evolution of the number of functionalities, along with their increased complexity, requires the contribution of dedicated solutions for the software carrying these functionalities. Firstly, in terms of software, it is becoming increasingly difficult to not provide a touch of neural networks. Even if the “black box” aspect of neural networks is less discussed, it can still remain an obstacle to implementation, particularly in the widely accepted “safety first” trend.

In this report, Yole Développement (Yole) focuses largely on the integration of accelerators (or neural engine / neural processing unit – different marketing names for same type of architecture) in ADAS solutions. These units, which have started appearing in mobile phone processors, are dedicated to the calculation of deep learning algorithms – the most famous of which is an AI method used today for object recognition in images. Tesla integrated these accelerators and AI into its Full Self-Driving (FSD) chip last year. For most OEMs, this solution will be realized by 2021 – 2022, since these special units are now integrated into all current or future ADAS chipsets at Mobileye, Xilinx, TI, Toshiba, Ambarella, and Renesas. This trend to integrate more and more AI and therefore accelerators follows in a linear fashion the rise in autonomy. Other trends such as centralization will gradually redraw the future of computing.

A MARKET DIVIDED BETWEEN CENTRALIZED PLATFORMS AND VISION PROCESSORS

Yole suggests that two paths are available: either a single computer-type NVIDIA or FSD, descending from the high-end segment of robotic vehicles (i.e. the “brain” of a car); or, the number of vision processors incorporating accelerators is multiplied, a path already embraced by several OEMs. Competition will arise between these two platforms and determine the revenue. The 2025 market for AI, including ADAS and robotic vehicles, is estimated at more than $2.7B – of which $2.4B will be “ADAS only”.

This is only the beginning, and the challenges surrounding AI and its impact on the automotive industry are already being felt. Some companies have a considerable lead, and it will be difficult to catch up with them, especially without integrating AI and the computing that goes with it.

So, what are the challenges? Who can keep up? And has the impact of COVID-19 and the resulting lack of cash permanently wiped some players off the map? This report will give a detailed answers to these questions.

Alphabet, Algolux, Amazon, AMD, Apple, ARM, Baidu, Bosch, BMW, Continental, Delphi, EasyMile, Eyesight Faurecia, Ford, Fujitsu, General Motors, Google, Infineon, Intel, Intel MobilEye, Kalray, Lyft, Melexis, Mercedes-Benz, Microship, Microsoft, Navya, NEC, Nio, Nissan, Nuance, NVIDIA, NXP, Parrot, PSA, Qualcomm, Renesas, Samsung, Sony Softkinetic, STMicroelectronics, Tesla, Texas Instruments, Toshiba, Toyota, Uber, Valeo, Videantis, Volkswagen, Volvo, Waymo, Xilinx and many more…