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AI in AUTOMOTIVEHow artificial intelligence will reshape the automobile industryn Technological Impactn OEM Strategiesn Supplier Profiles n Global Survey Results

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2018 TECHNOLOGY STRATEGIES REPORT

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ABOUT THIS REPORT

Artificial Intelligence, a technology made infamous by a string of Hollywood films and only recently beginning to live up to hype dating back to the 1950s, is poised to become the pilot of tomorrow’s autonomous vehicles. AI will be the car’s decision maker, drawing information from a variety of other AV-enabling technologies such as lidar range detectors, precision radar, global positioning systems and a connected ecosystem to deliver the world to work or play safely and leisurely.

But AI not only will know when to stop the car, turn left or avoid a stray cat. It also will make our interaction with the AV more pleasant by learning our musical tastes, daily routines, favorite restaurants and even when it might be most convenient to stop along the way.

With a connection to the Internet of Things, AI also will know ahead of our arrival home to open the garage door, turn up the air conditioning and even have the big game from earlier in the day recorded and cued up on the television.

AI is not just for piloting the car, either. It also promises make driving more enjoyable by vastly improving features such as voice-command systems that can frustrate car owners today, delivering better fuel economy and fewer tailpipe emissions through optimized performance, and routing us around an appointment-breaking traffic jam that may lie ahead.

However, its No. 1 advantage is avoiding the deadly mistakes human drivers make repeatedly.

This special report from Wards Intelligence examines how the automotive industry, which throughout its history has been focused primarily on the hardware of the car, will apply software-driven artificial intelligence to future vehicles.

The report examines motivating factors behind the industry’s leap into AI; lays out the various strategies automakers employ to ramp up their expertise in the field, as well as the many challenges they face; profiles key suppliers, both traditional and nontraditional, hoping to become the go-to shop for OEMs looking to integrate AI-driven autonomous systems into their vehicles; and offers insight into which industry players occupy a leading position in AI development and deployment.

The report also provides a snapshot of the AI field and its sudden rise to relevancy across industries. It presents key findings from a survey of auto

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industry professionals regarding the perceived need for AI in automated vehicles, their level of confidence in the technology and its outlook for success.

This report should be considered a barometer for companies to determine their competitiveness in the sector, as well as a primer and guidebook for academia and the investment community. It also serves as a companion report to The Autonomous Vehicle Roadmap. It is intended for product planners, market analysts, academics and others who would benefit from a detailed examination of the industry’s plans for implementing AI.

ABOUT THE AUTHOR James M. Amend is a veteran business journalist and WardsAuto senior editor who has covered the auto industry for nearly two decades. A Neal Award winner for excellence in business journalism, he has interviewed the industry’s top executives and reported on topics from design and engineering to marketing and finance for Wards.

CONTRIBUTORS: Tom Murphy, Managing Editor Drew Winter, Senior Editor David E. Zoia, Research Director Christie Schweinsberg, Senior Editor Susan Kozik, Director, Content Design

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TABLE OF CONTENTS

I. OVERVIEW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1• BUSINESSCASE - DRIVERLESS TAXI FLEETS - SUPPLIEROPPORTUNITY - TRUCKINGOPPORTUNITY - CONSUMEROPPORTUNITY

• CONSUMERACCEPTANCE - GENPACTSURVEY - FACEBOOK SCANDAL

• REGULATORYATTITUDE/ETHICALQUESTIONS - U.K.GUIDELINES - TROLLEYCARDILEMMA - BOSCHPERSPECTIVE - MUSKPERSPECTIVE

• STATEOFTECHNOLOGY - AUDI/DAIMLERDEPLOYMENTS - NVIDIATECHNOLOGY - DAIMLER/VOLKSWAGENDIGITALIZATION - CENTRALCOMPUTING

II. TECHNOLOGY . . . . . . . . . . . . . . . . . . . . . . . . . 17• BRIEFHISTORY• TALENTCRUNCH/COST• AIDEFINED• ADDITIONALKEYDEFINITIONS• AIFORAUTONOMY• OTHERAPPLICATIONS

III. AUTOMAKERS . . . . . . . . . . . . . . . . . . . . . . . . . 26• INTRODUCTION• BMW/FIATCHRYSLER• DAIMLER• FORD• GENERALMOTORS• HYUNDAI/KIA

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• PSAPEUGEOT-CITROEN• RENAULT-NISSAN-MITSUBISHI• TESLA• TOYOTA• VOLKSWAGEN• VOLVO

IV. SUPPLIERS . . . . . . . . . . . . . . . . . . . . . . . . . . . 48• INTRODUCTION• ADIENT• ALIBABA• APTIV• AURORAINNOVATION• BAIDU• BOSCH• CONTINENTAL• DENSO• FAURECIA• INTEL/MOBILEYE• MAGNA• NVIDIA• NUANCECOMMUNICATIONS• VALEO• VISTEON• WAYMO• ZF

V. THREE QUESTION WITH… . . . . . . . . . . . 72• MARKREUSS,GM• DAVIDATKINSON,CONTINENTAL• JIMADLER,TOYOTA• DUSHYANTWADIVKAR,BOSCH

APPENDIX B . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77• WARDSINTELLIGENCE2018ARTIFICALINTELLIGENCE INAUTMOTIVEVEHICLESSURVEY

• GERMANFEDERALMINISTRYOFTRANSPORTANDDIGITALINFRASTRUCTURE,ETHICSCOMMISSIONONAUTOMATEDDRIVINGARTIFICALINTELLIGENCEGUILDELINES

AI WILL BEGIN WITH

BETTER VOICE

RECOGNITION AND INFO­

TAINMENT IN VEHICLES.

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STATE OF TECHNOLOGYArtificial intelligence in automotive largely remains in the development phase, but the industry appears bullish, expecting the technology to not only be the decision-maker for fully autonomous vehicles, but also improve the overall user experience, heighten productivity and safety on the assembly plant floor, streamline logistics and even help hire the next C-suite executive.

The new Audi A8 with Audi AI traffic jam pilot assist will operate the vehicle in slow-moving traffic up to 37 mph (60 km/h). A key requirement for the system is a precisely detailed, mapped image of the environment at all times. Artificial intelligence and the automaker’s central driver assistance controller (zFAS) plays a key role in formulating the map.

The Mercedes-Benz A-Class User Experience system (MBUX), powered by NVIDIA AI processing, is personalized and adapts to suit the user. A map display supplemented by augmented reality is a new navigation feature. The car’s front-facing camera captures a video image of the surroundings and the information is augmented with other helpful navigation information, such as the street name, house number and directional arrows, and automatically blended onto the touchscreen.

A virtual assistant from Mercedes-Benz called ask Mercedes also uses AI. It combines a chatbot with augmented reality functions: questions on displays, functions and use of the vehicle can be typed in on a smartphone screen or asked using the voice-recognition system. Controls and displays within the new S-Class, E-Class and A-Class can be explored using a smartphone camera. Ask Mercedes can be used via the smartphone app as well as other communications channels such as Facebook Messenger or Amazon Alexa.

Nuance supplies MBUX with the AI technology to enable natural-language understanding off the voice-recognition expert’s Dragon Drive platform. Dragon Drive combines advanced AI and NLU capabilities to understand individual preferences, personalize entertainment and execute complex, contextual commands. The system is directly embedded into the automotive head unit, and can be customized and developed for individual automaker design and branding needs.

Nuanace says the MBUX system is unique because it features new conversational capabilities that allow drivers to speak to their cars in a more fluid and conversational way.

The semi-autonomous and ADAS systems within the current Tesla Model

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DEEP LEARNINGDeep learning, a class of machine learning, employs an artificial neural network that mimics human nerve cells to exchange and process information over many layers until it arrives at the most probable outcome.

For example, if an autonomous vehicle were to encounter a pedestrian during inclement weather, making the person difficult to discern, each of the artificial neurons would examine a piece from the scene and assign a weight to its input, or a level of confidence in what it has identified. The process continues over many neurons and levels of filtering until it reproduces the scene. The network architecture then determines if it is correct and decision-making proceeds – should the car slow down, come to stop or continue along the journey.

Deep learning is especially effective in applications such as the image processing discussed above but also natural-language recognition, where individual speech patterns, accents or dialects are like fog clouding a driver’s commands.

Deep learning also makes decisions like a human would, which should ease the transition from piloted cars to autonomous.

INFERENCEInference is an exercise in AI where, based on learned data, an assumption can confidently be made by a machine from newly presented data without sending information back to its central computing system. Voice-activated assistants such as Amazon Alexa, Google image searches and e-mail spam filters use inference. In an autonomous vehicle, AI-related tasks performed by inference would include perception, localization and fusion. Those tasks would be performed within hardware, such as radar, to speed up the data processing.

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POLICYPolicy is the set of constraints, or ground rules, AI must follow. For example, with an autonomous vehicle the constraint may be traveling within a certain speed according to weather conditions or the presence of pedestrians. Policy plays a key role in path planning for autonomous vehicles.

PATH PLANNINGKnown also as motion planning, path planning lets autonomous machines find the shortest, optimal path between two points. For a vehicle, it would be the navigation of obstacles and relevant signs in the roadway. In a manufacturing setting, path planning could help a robot navigate the facility. Path planning also determines the finer movements a robot might make, such as moving an arm without colliding with people or equipment in a manufacturing setting.

BLACK BOXThe black box dilemma refers to the unseen decision making performed by deep learning algorithms. It is not to be confused with black box data recorders in cars and aircraft. The deep learning black box is the “dark space” between where data goes into the machine and the output, or decision, occurs. It is a dilemma because if the machine makes a mistake, it is nearly impossible to determine how it arrived at the wrong decision. One method to uncover the mistake is to feed the machine successive, slightly nuanced inputs in a process of elimination. New advances in simulation for gathering autonomous-vehicle testing data, such as NVIDIA’s Drive Constellation, may alleviate the black box dilemma. It is worth noting the decision making of human neural networks, which are used as the basis for the artificial neural networks of deep learning, also are not understood.

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FORDFord invested $1 billion in Argo AI in February 2017 to heighten its capabilities in the field. The investment will occur over a five-year period with the goal of developing a virtual-driver system for a Level 4 autonomous vehicle coming from the automaker in 2021.

Argo AI was founded by former Google and Uber leaders and boasts some of the most experienced roboticists and engineers working in the field of AVs. The team of experts in robotics and artificial intelligence is led by Argo AI founders Bryan Salesky, company CEO, and Peter Rander, chief operating officer. Both are alumni of Carnegie Mellon National Robotics Engineering Center, a leading AI institution.

The investment called for the team behind Ford’s virtual-driver system, which is the machine-learning software for its AVs, to combine with the robotics talent and expertise of Argo AI. While Ford focuses on the hardware of the virtual-driver system, as well as on systems integration, manufacturing, exterior and interior design and regulatory policy management, the automaker’s software team will join with Argo AI experts on the software side.

The work between Ford and Argo AI initially is exclusive to the automaker, although in the future Argo AI could license the solution to other OEMs. Ford became a majority shareholder in Argo AI with its investment.

Argo AI was expected to have more than 200 employees at its Pittsburgh

Ford working

with Argo AI.

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NUANCE COMMUNICATIONSBurlington, MA-based Nuance Communications has been supplying industries with products driven by artificial intelligence for 20 years, including automatic speech recognition, natural language understanding, text-to-speech and biometrics.

It currently focuses on enhancing consumer experiences across interactive voice response, web, mobile, Internet of Things and messaging channels. Its machine-learning algorithms enable conversational dialog, where a conversation is required for the system to discover the customer’s interest or need, gather the necessary information to assist and deliver the desired outcome through self- or assisted-service. It also can deliver personalized, “one-shot” answers using natural-language understanding to derive concepts and meaning from user intents in addition to complex answers that require database queries.

The company’s Dragon Drive AI platform for voice interaction targets automotive customers with content-management capabilities, secures third-party access, learns user preferences, recognizes individual user voices and even where that person may be sitting in the vehicle. Dragon Drive will connect with the home to perform functions such as alarm activation and lighting from the vehicle. Its AI-driven natural-language understanding is designed to help drivers focus more closely on the roadway, as well as provide valuable and timely vehicle diagnostic information.

Nuance says its security protocols are among the industry’s best and considers itself unique for Dragon Drive’s ability to tailor brand-specific voice interactions covering more than 50 languages.

Its latest AI-driven product appears on the new Mercedes-Benz A-Class. The Mercedes-Benz User Experience System within the A-Class features new conversational capabilities that allow drivers to speak to their cars in a more fluid and conversational way.

Nuance operates the Cognitive Innovation Group, a company laboratory focused on the latest advancements in artificial intelligence across markets and industries.

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will have to grow that expertise.

WARDS INTELLIGENCE: What steps are AI experts taking to expand the profession?

Atkinson: Continuing-education courses in artificial intelligence are so important. We’ve made a strategic alliance with Udacity, for example, to deliver those courses within Continental. We’re experimental. But AI people are expensive, too, and a lot of companies are not quite yet prepared to pay for that. It will be that there will be companies specializing in delivering this sort of technology who will become partners. We are actively involved, as are other automakers and suppliers, with these emerging companies that have the expertise. AI and other technologies are converging to enable powerful and valuable new applications that people want and will help achieve social goals such as safety. All commercial and social sectors will be transformed on a scale equal to or greater than the changes from the Internet and smartphones. The automotive sector will have many new business opportunities to be captured by companies that are smart, agile, adaptable and quick to weather disruptions.

JIM ADLERManaging director- Toyota AI Ventures

WARDS INTELLIGENCE: Could you describe the current AI landscape, that is in terms of the number of companies out there doing meaningful work the auto industry could leverage?

Adler: Venture-capital funding in automotive technology is at an all-time high, over $1.3 billion in 2017 according to Pitchbook, and there is no shortage of entrepreneurs working in AI and mobility. High-quality startups are in high demand, and there’s competition among investors to identify and back the best startups – the ones with disruptive technology, talented teams and innovative business models. For us, it’s about partnering with people who have the talent, tenacity and traction to execute on their vision. Within the perception-prediction-planning automated driving stack, one area where we think there’s more

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