AUTOMOTIVE SOFTWARE IN THE AGE OF AUTONOMOUS

I N T R O D U C T I O N

A U T O M O T I V E S O F T W A R E I N T H E A G E O F A U T O N O M O U S A U T O M O T I V E S O F T W A R E I N T H E A G E O F A U T O N O M O U S

C O N T R I B U T O R S A B O U T T H E R E S E A R C H T R E N D S I N A U T O N O M O U S I N V E S T M E N T S I N A U T O N O M O U S I N D U S T R Y A N A L Y S I S I N T E R V I E W K E Y T A K E A W A Y S

1 1

AUTOMOTIVE SOFTWARE IN THE AGE OF AUTONOMOUS

by Automotive IQ with special insight from Toyota, Volvo, and others

INDEXINTRODUCTION....................................................................................................................................................................................................... 3

CONTRIBUTORS ...................................................................................................................................................................................................... 4

ABOUT THE RESEARCH........................................................................................................................................................................................ 5

TRENDS IN AUTONOMOUS ................................................................................................................................................. 7

INVESTMENTS IN AUTONOMOUS .............................................................................................................................. 8

INDUSTRY ANALYSIS ...............................................................................................................................................................10

INTERVIEW ...............................................................................................................................................................................................20

KEY TAKEAWAYS .............................................................................................................................................................................22

INTRODUCING AUTOMOTIVE IQ ...................................................................................................................................................................23

A U T O M O T I V E S O F T W A R E I N T H E A G E O F A U T O N O M O U S

C O N T R I B U T O R S A B O U T T H E R E S E A R C H T R E N D S I N A U T O N O M O U S I N V E S T M E N T S I N A U T O N O M O U S I N D U S T R Y A N A L Y S I S I N T E R V I E W K E Y T A K E A W A Y SI N T R O D U C T I O N

INTRODUCTIONSince 2009, autonomous drive continues to be a major domain within the automotive industry, and has progressed from maybe possible to inevitable. Major significant OEM’s are pursuing the technology, racing towards the title as autonomous champion and mobility provider. Waymo emerged first, however its mono- poly has eroded of late as we see companies like Lyft and Uber also wanting a piece of the pie. Tech giants such as Intel, IBM and Apple are also taking part, followed by countless start ups having materialized laser sensors, data mapping, machine learning and service centers to manage these vehicles.

As we allow AI and autonomous systems to take over manual functions, industry challenges and uncertainty are inevitable. However with billions being poured into R&D from OEM’s such as Ford, GM, Nissan, Tesla, Mercedes etc., the startups are continuously improving the software needed for full autonomous cars.

I N D U S T R Y A N A L Y S I S I N T E R V I E W K E Y T A K E A W A Y S

3



A B O U T T H E R E S E A R C H T R E N D S I N A U T O N O M O U S I N V E S T M E N T S I N A U T O N O M O U S I N D U S T R Y A N A L Y S I S I N T E R V I E W K E Y T A K E A W A Y SC O N T R I B U T O R S

CONTRIBUTORS

ABOUT THE AUTHOR

Sagar Behere Architecture and Safety for

Autonomous Driving Toyota Research Institute

Carina Björnsson Technical expert methods

active safety Volvo Car Group

Siddartha Khastgir Principal engineer –

Research lead for V&V of CAV technologies

WMG, University of Warwick

Peter Els Freelancer

Writing about Cars

Dr.-Ing. Carsten Hass Engineering Manager Automated Driving &

Integral Cognitive Safety ZF TRW

Christoffer Augsburg Marketing Manager

Automotive IQ

Anna recently completed a Master’s double degree program in International Relations and Diplomacy in Paris. She is the Digital Editor of the Automotive IQ portal. Anna is responsible for building and maintaining the content management strategy and workflow, as well as developing and managing relation-ships with outside contributors for the Auto-motive IQ media vertical, which currently has a readership of 50k industry professionals. For any inquiries regarding the report or the possibility of seeing your work published on the Automotive IQ portal contact her [email protected]

4



C O N T R I B U T O R S T R E N D S I N A U T O N O M O U S I N V E S T M E N T S I N A U T O N O M O U S I N D U S T R Y A N A L Y S I S I N T E R V I E W K E Y T A K E A W A Y SA B O U T T H E R E S E A R C H

For the 2018 autonomous drive industry report, we asked the opinion of 50,000 industry experts based in over 200 countries.

A B O U T T H E R E S E A R C H

20 <39.55%

5-1011.19%

3-5 11.19%

0-3 18.66%

10-20 19.40%

Years of automotive practiceAn OEM

Other

Consulting/Engineering firm

A component supplier

A system supplier

Academia

22.14%

11.45%

3.05%

12.21%

13.74%

16.03%

21.37%

I work for...

Tool supplier

Which job title best describes your current position?

The 2018 autonomous drive industry report explores the major trends, challenges and investments that will impact the most this year’s automo-tive industry.

The purpose of the survey is to explore how industry leaders see the autonomous-driving ecosystem develop, change, face and over-come challenges over the course of 2018. Based on their professional specialization, respondents were also asked to give their own comments on how to overcome the major challenges in the different segments of autonomous vehicle technology development as well as the technologies they’re most interested in learning more about.

Our respondent sample is split almost evenly between OEMs, Component suppliers, System suppliers and Academia, giving a comprehensive overview of the core of the automotive industry. With respect to the role of respondents, the majority of respondents work as engineers, managers and re- searchers, and 53% of them hold senior positions with over 20 years of experience in the automotive field.

The survey was conducted online by Survey-Monkey between February and March among a sample of 50,000 automotive industry experts.

5


A U T O M O T I V E S O F T W A R E I N T H E A G E O F A U T O N O M O U S A U T O M O T I V E S O F T W A R E I N T H E A G E O F A U T O N O M O U S

C O N T R I B U T O R S T R E N D S I N A U T O N O M O U S I N V E S T M E N T S I N A U T O N O M O U S I N D U S T R Y A N A L Y S I S I N T E R V I E W K E Y T A K E A W A Y S

Which region are you responsible for?

South America

1.5%

DACH1.5%

North America

22.56%

Global36.09%

A B O U T T H E R E S E A R C H

Asia7.52%

6 6

8.27%Other

29.32%EU



C O N T R I B U T O R S A B O U T T H E R E S E A R C H I N V E S T M E N T S I N A U T O N O M O U S I N D U S T R Y A N A L Y S I S I N T E R V I E W K E Y T A K E A W A Y ST R E N D S I N A U T O N O M O U S

Autonomous key trends - 2018 and beyondT R E N D S I N A U T O N O M O U S

Collision avoidance system91.82%

Other16.36%

Holographic windscreen13.64%

FOB display11.82%

Owner’s manual app10.91%

Remote vehicle shutdown10%

Active health monitoring27.27%

Biometric vehicle access and start30%

AI driven passive and active safety88.18%

Top trends that will affect autonomous driving customer acceptance - 3 choices per respondent

Collision avoidance systems and warning systems: automotive safety systems will affect this year’s autonomous driving customer acceptance the most.

At some point in the near future, autonomous vehicles will become a reality. Most of the OEMs have plans to roll out these vehicles within 10 years, however there are still serious concerns in the experimental phase that is blocking new developments. We surveyed the key decision makers from the OEMs on top trend preventing

fully autonomous vehicles and found that over 90% of the respondents indicated collision and avoidance systems as the biggest indicator, followed closely (87%) by the development of Artificial Intelligence driven passive and active safety technologies.

Siddartha Khastgir WMG, University of Warwick

“I am in complete agreement that collision avoidance and warning systems rank top of the chart. This is primarily because they have been in production for some time now and their incorporation in EURO NCAP has lent a high degree of credibility to these systems. Also, the safety benefits are much more tangible from a daily user’s point of view.

Equally to security systems, infotainment and connected services have also been subject to rapid development, with con-nectivity opening up a world of opportu-nity in the way in which cars interact with occupants, other vehicles, and infrastruc-ture. These changes are significant in terms of HMI development, since there is much more information to be exchanged between car and driver, and automakers are beginning to look at HMI in terms of a ‘conversation’ between the vehicle and occupants.”

Sagar BehereToyota Research Institute

“I consider as highly important the kind of HMI and user-experience that leads to an under-standing and trust of the system, because this is crucial in driving customer acceptance; you can have the most highly-performing AD system, but if it isn’t clear to the user what it’s doing and how it’s working, and if the user is constantly getting confused, it’s not going to drive acceptance of that system.”

7



C O N T R I B U T O R S A B O U T T H E R E S E A R C H T R E N D S I N A U T O N O M O U S I N D U S T R Y A N A L Y S I S I N T E R V I E W K E Y T A K E A W A Y SI N V E S T M E N T S I N A U T O N O M O U S

Safety first! Also when it comes to investmentsI N V E S T M E N T S I N A U T O N O M O U S

Collision avoidance system-warning systems91.13%

Holographic windscreen16.94%

Other14.52%

FOB display13.71%

Owner’s manual app12.1%

Remote vehicle shutdown6,45%

Active health monitoring27.42%

Biometric vehicle access and start33.06%

AI driven passive and active safety84.68%

Highest investments areas in 2018 - 3 choices per respondent

The consensus view amongst industry professionals responding to the IQ Automotive survey is that of all self-driving features collision avoidance and AI driven active and passive safety systems are by far the most appealing autonomous features drivers would be interested in.

According to a November 2017 forecast by Allied Market research, investment in the global collision avoidance system market was valued at $13.18 billion in 2016, and is projected to reach $24.99 billion by 2023, growing at a CAGR of 9.7% from 2017 to 2023.

Similarly, supporting the view expressed by respondents of the survey regarding the importance of AI driven passive and active safety systems, the global market for automotive artificial intelligence is expected to grow at a CAGR of 37.32% from 2017 to 2025.

As self-driving technology matures, consumers are beginning to show an interest in vehicles with higher levels of automation.

90.98% of respondents believe collision and avoidance systems will receive the most investments in 2018

Sources: Akshay Jadhav; Allied Market Research; Collision Avoidance System Market Overview; November 2017; https://www.alliedmarketresearch.com/collision-avoidance-system-market Markets and Markets; Automotive Artificial Intelligence Market by Offering, Process, Application and Region - Global Forecast to 2025; August 2017; https://www.marketsandmarkets.com/Market-Reports/automotive-artificial-intelligence-market-248804391.html

8


“For truly safe autonomous vehicles we need to adopt a multi-pronged approach.At WMG, we define a testing continuum from the virtual world to real-world testing. One common theme in both the worlds is the identification of test scenarios. While many organizations are working on scenario definition, a more collaborative approach needs to be adopted. Research programs in various countries are aiming to do this, and we look forward to the results from these projects.”

I N T R O D U C T I O N C O N T R I B U T O R S A B O U T T H E R E S E A R C H T R E N D S I N A U T O N O M O U S I N V E S T M E N T S I N A U T O N O M O U S I N D U S T R Y A N A L Y S I S I N T E R V I E W K E Y T A K E A W A Y S

AUTONOMOUS VEHICLE INDUSTRY BRAKEDOWN:Security & Safety

Sensors, Vision systems development andembedded systems

Algorithms, and AI: deep learning, neural networks

Autonomous vehicle data processing

Autonomous system connectivity

Mapping for autonomous driving



C O N T R I B U T O R S A B O U T T H E R E S E A R C H T R E N D S I N A U T O N O M O U S I N V E S T M E N T S I N A U T O N O M O U S I N T E R V I E W K E Y T A K E A W A Y SI N D U S T R Y A N A L Y S I S

Sagar Behere Toyota Research Institute

“When it comes to vehicle cyber security, sometimes, not always, the problem is made out to be more difficult or serious than it might be in practice; and the good news is there’s an increasing awareness of cyber-security and, also compared with some of the other challenges in autonomous driving, we may not need as much of a revolution in scientific under-standing to solve the problem, because with cyber-security there are many ways to tackle this - and the field of enterprise security and computer security has been generating a lot of answers.”

Top 3 challenges when it comes to security and safety for autonomous applications

22.22%

ISO26262 second edition too complex5.56%

72.22% Testing AI driven systems in real driving conditions

33.33% Lack of security expertise in the automotive world

33.33% Not enough standardization between safety and security overlaps

50% Too many intrusion points with minimum safety/security requirements in place

44.44% Verification of security

38.89% Safety and secure code

“The good news is: there’s an increasing awareness of cyber-security”

Other

10

Security & SafetyI N D U S T R Y A N A L Y S I S

The second part of our research focuses on the different segments of autonomous vehicle technology development. We asked our automotive experts community to pick their area of expertise within autonomous technology and point at the main challenges and trends. Security & Safety turned out to be the most popular field of specialization within the respondents.

Top challenges



C O N T R I B U T O R S A B O U T T H E R E S E A R C H T R E N D S I N A U T O N O M O U S I N V E S T M E N T S I N A U T O N O M O U S I N T E R V I E W K E Y T A K E A W A Y S

Carina Björnsson

”It might go, with some limits time wise. We do have a plan; and maybe it’s too optimistic. Hard to say as we are exploring new fields, and we will not release something that’s not safe, that’s a fact, so if it’s not safe we’re not ready.”

Volvo Car Group

How much is your company planning to invest to face these challenges?

Within which time frame will your company overcome these challenges?

3 < years 35.29%

5.88% 0 - 6 months

5.88% 1 - 2 years

Time Frame Industry Leaders

I N D U S T R Y A N A L Y S I S

A complex problem

(automotive ecosystem) has to be divided into more tractable components:

in-vehicle safety subsystems, in-vehicle non-safety subsystems, external non-vehicular

subsystems (OEM Security Operations Center - SOC, V2x, OEM supply chain, Tier 2 software development, Smart City/ITS, Mobile Service

Providers. Treatment of all above as mem-bers of the same ecosystem with common

security/safety procedures and V&V.

Software plat-

form that increases efficiency allowing for

additional safety to be added within same on-vehicle

compute foot-print in production

Communication and consortium activity - joint OEM activity

must be key!

0 – 50.000 500.000 - 750.000

750.000 - 1.000.000

46.67% 13.33% 13.33%

1.500.000 < 50.000 - 100.000

1.000.000 – 1.500.000

13.33% 6.67% 6.67%

52.94% 2 - 3 years

11

“I would expect several millions, at least. A lot depends on the scoping and what kind of company you’re in but if, for example, you consider a big OEM, I see significant investments.”

Toyota Research Institute

Sagar Behere

Tips to overcome the biggest challenges Investments

Who do you consider a market leader in security and safety for autonomous applications?

16.67%

30.77% Other

7.69%



C O N T R I B U T O R S A B O U T T H E R E S E A R C H T R E N D S I N A U T O N O M O U S I N V E S T M E N T S I N A U T O N O M O U S I N T E R V I E W K E Y T A K E A W A Y S

“We need to create a framework where the IP of manufacturers is protected but the collaboration ensures we solve the challenges faster”


1.000.000 – 1.500.000

0 - 50.000

50.000 - 100.000

23.08% 15.38% 15.38%

100.000 - 250.000

1.500.000 < 250.000 - 500.000

750.000 - 1.000.000

15.38% 15.38% 7.69% 7.69%

12

Sensors, Vision systems development and embedded systems


Sensors, Vision systems development and embedded systems follow closely the Security & Safety segment for popularity amongst our automotive experts community. Respondents were asked to identify the main challenges, possible solutions and trends related to testing sensors. The results are summarized below.


“I think your survey accurately captures the current challenges in ADAS & AD. Testing sensor combinations and dependence of failure of one or more on the functionality needs to be evaluated. As men-tioned earlier, with an ever increasing ODD, the state space has grown exponentially.”

What do you think are the top 3 challenges when it comes to sensors, vision systems development and embedded systems for autonomous applications?

Testing and validation of complex sensor combinations76.92%

Choosing the right sensors for the perception system38.46%

Challenges in data interface standardizations23.08%

Finding the right sensor fusion approach

Environmental models

7.69%

7.69%

38.46%

Integration into the overall system architecture

Cost of certain sensors such as Lidar

46.15%

Sensor fusion with data from other sources than sensors61.54%

Top challenges

How much is your company planning to invest to face these challenges?

Investments





“I agree with the time frame as we need to develop new tools and methods now to be able to realize the AD future in the next 5-10 years. However, I personally believe the level of investment needs to be much higher. But more importantly, we need to have a collaborative approach to solving these challenges. We need to create a framework where the IP of manufacturers is protected but the collaboration ensures we solve the challenges faster.”

Time Frame

Tips to overcome the biggest challenges

Slim design and more

redundancy

Testing devices

adaptation

13

Within which time frame will your company overcome these challenges?

16.6

7%

16.6

7%

16.6

7%

16.6

7%

33.3

3%

Industry Leaders

Who do you consider a market leader in sensors, vision system development and embedded systems?

9.09% 81.82%+Other

0 - 6 months 3 < years2 - 3 years1 - 2 years6 months -

1 year




Dr.-Ing. Carsten Hass ZF TRW

“I agree the timeframe for testing and validation needs to be tackled immediately. From my perspective, investments mentioned here are lower bound estimates. These will be higher if we’re talking about data-taking, data-storage, and the infrastructure for the test and validation data.”

“Nvidia’s Drive PX Pegasus system, which is as small as a taxi license plate but can do 320 trillion operations per second”

14

Algorithms, and AI: deep learning, neural networksI N D U S T R Y A N A L Y S I S

As automated vehicles evolve into autonomous self-driving ‘robo-cars’ the safety of their passengers will largely depend on the successful application of artificial intelligence and in particular the algorithms applied to deep learning neural networks.

In the autonomous car, one of the major tasks of AI deep learning algorithms is continuous rendering of the vehi-cle’s environment and predicting possible changes that could impact the vehicle’s progress.

Moreover, the neural networks themselves need to be complex enough (have sufficient number of parameters) to learn from vast datasets without forgetting past expe-rience. In this scenario, as you increase the dataset size by a factor of n, the computational requirements increaseby a factor of n2, creating a complex engineering chal-lenge.

What is the biggest challenge when it comes to verify systems that are based on self-learning algorithms, and what are some of the solutions?

Dr.-Ing. Carsten Hass, Engineering Manager Auto-mated Driving & Integral Cognitive Safety at ZF TRW, explains:

It’s for this reason that suppliers are developing increasingly powerful computing modules, such as Nvidia’s Drive PX Pegasus system, which is as small as a taxi license plate but can do 320 trillion operations per second, to accelerate deep machine learning, analytics, and engineering applications.

ZF, another leading supplier is right now using

But AI is not only used for navigation and control of the AV, it’s finding wider applications within the vehicle, specifi-cally for biometrics, which has been highlighted as one of the key features that consumers would be interested in, in the IQ Automotive survey.

“The major challenges are the point of miss-classifica-tion. In some cases, you do not have the relationship between your output and input parameters and that makes it way more complex; the solution would be to limit your competence-frame and test for completeness there. For example, regression testing on a specific neural-net-based detection feature would be feasible,

“the classical static and dynamic black-and-white box tests for algorithm development, also vehicle testing for the full system - that means algorithm in the environ-ment of a car. But on top we do scenario based HiL, MiL, SiL - hardware-in-the-loop, model-in-the-loop and soft-ware-in the-loop testing on databases with real-world data and synthetically generated data. This is what comes on top as a new method.”

a complete neural-net based lateral control algorithm would be more challenging.”


I N V E S T M E N T S I N A U T O N O M O U S I N T E R V I E W K E Y T A K E A W A Y ST R E N D S I N A U T O N O M O U SI N T R O D U C T I O N C O N T R I B U T O R S A B O U T T H E R E S E A R C I N D U S T R Y A N A L Y S I SH

15

• Cost of the hardware• Building up sufficient data points to train the AI• AI models have difficulty in transferring their ex- periences from one set of circumstances to another. That means companies must commit resources to train new models even for use cases that are similar to previous ones

“I would agree that testing and validation of machine learning, is one major topic. It’s not just for machine learning but it’s also for testing the complete system, the algorithm in the vehicle as a full system - on the road and in a simulation environment.”

Using AI to perform facial recognition manufacturers can monitor attentiveness (Espescially usefull in L3 and 4 automation) as well as customising comfort features to specific drivers and passengers.

Moreover, artificial intelligence solutions are also being applied in ways that could enable cars to be an exten-sion of a virtual personal assistant: IBM is working with several automakers on solutions encompassing Watson that could turn the AV into an extended personal assistant.

In another example of AI, ZF’s ProAI hardware underpins Car eWallet, a payment system that lets cars pay for par-king, tolls and other services when they’re in autonomous mode. It is also the engine for Baidu’s autonomous pro-gram, which includes a valet parking program.

However, there are several challenges that manufacturers need to address. Amongst the most important being:

While the mass adoption of AI enhanced by deep learning neural networks is still some time away, the industry is attracting significant investment.

Last year investors pumped in over $15.2B in funding to AI startups across multiple industries. It was a 141% spike in funding from 2016. Over 1,100 new AI companies have raised their first rounds of funding since 2016. And that’s more than half the historic number of AI startups that have ever raised an equity round.

Sources: Terry Costlow; SAE International; ZF’s CES blitz underscores industry shifts; January 2018; https://www.sae.org/news/2018/01/zfs-ces-blitz-underscores-industry-shifts Ryan Daws; IoT news; Ford pumps $1bn into ex-Google, Uber engineers’ AI startup; February 2017; https://www.iottechnews.com/news/2017/feb/13/ford-pumps-1bn-ex-google-uber-engineers-ai-start-up/ Aditya Chaturvedi; Geospatial world; 13 major Artificial Intelligence trends to watch for in 2018; February 2018; https://www.geospatialworld.net/blogs/13-artificial-intelligence-trends-2018/ Adam Grzywaczewsk; NVidia; Training AI for Self-Driving Vehicles: the Challenge of Scale; October 2017; https://devblogs.nvidia.com/train-ing-self-driving-vehicles-challenge-scale/

Image source: Geospatial

2013 2014 2015 2016 2017

$1,739

310

482

635

888

1349

$3,477 $4,569 $6,255 $15,242




The majority of manufacturers are currently developing sensor-based autonomous systems using cameras, lidar, radar and ultra-sonic sensors that generate vast amounts of data: Intel estimates that a single autono-mous vehicle will generate 4 terabytes of data every day.

• In-vehicle for mission-critical processes such as sensor fusion

• Cloud processing for information such as telemetry and maps

The importance of data and its processing can be clearly seen by recent investments such as Ford’s funding of a US$200 million data center to store consumer data associated with connected and autonomous vehicles, and acquisitions by Qualcomm, Samsung, Intel and GM amounting to nearly US$70 billion for technology suppliers with strong competitive positions in the autonomous driving market.

Furthermore, Gartner research said last year that the worldwide public cloud-services market was worth an estimated $246.8 billion.

Cloud-based networking and connectivity are set to play a pivotal role in supporting V2V and V2X communication.

Intel estimates that a single autonomous vehicle will generate 4 terabytes of data every day

4,000 GBAutonomous Vehicles

Per day--- each day

~10-100 KBRadar

Per second

~10-100 Sonar

Per second

~50 KBGPS

Per second

~10-70 Lidar

Per second~20-40

Cameras

Per second

Image source: Intel

16

Autonomous vehicle data processingI N D U S T R Y A N A L Y S I S

In order to perform functions autonomously self-driving cars will need to process vast amounts of data, in real time and with low latency. To do this these cars will rely heavily on connectivity, smart maps, the ability to process massive amounts of data, and artificial intelligence to lighten the code.

The coming flood of data in autonomous vehicles




Currently it is estimated that between 60 and 80 percent of cars sold in 2017 contained installed telematics, and by 2022 75 percent of connected car packages will be sold as part of smaller, less expensive cars.

The two primary technologies being considered in connected vehicles are:

• Dedicated Short Range Communications (DSRC) in the 5.9GHz band

• Cellular V2X which will rely heavily on 5 G networks

The Automotive Edge Computing Consortium estimates that data sent to the cloud by connected vehicles will reach 10 exabytes per month by 2025 - 10,000 times what’s being collected in early 2018.

This flow of data gives rise to considerable security and privacy concerns. The incentive to manage anxieties over these issues will come not only from a desire to fulfill customer expectations. There’s also the potential for re- gulators to step in and establish minimum requirements.

Connectivity allows autonomous cars to accurately plot their position by accessing maps in real time.

I am reluctant to use car-related connected services because I want to keep my privacy

I am afraid that people can hack into my car and manipulate it (eg, the braking system) if the car is connected to the internet

Image source: Mckinsey

By 2022, 75% of connected car packages will be sold as part of smaller, less expensive cars

17

Autonomous system connectivityI N D U S T R Y A N A L Y S I S

In a recent Automotive Product Development and Launch Cycles survey sponsored by Jabil, 53 percent of respondents believed that better connectivity was the primary driver of technology innovation in the industry.

Ger

man

y

US

Braz

il

Chin

a

5145

37

21

ø 37

ø 54

Ger

man

y

US

Braz

il

Chin

a

43

5359 59




18

Mapping for autonomous drivingI N D U S T R Y A N A L Y S I S

Manufacturers rely on maps to accurately determine the position of the vehicle in its environment. Known as localization this is a critical prerequisite for effective AV navigation.

Perception Control

PID

MPC

Others

Planning

Route Planning

Sensors

Lane detection

Traffic Light Detection & Classification

Traffic Sign Detection & Classification

Object Detection & Tracking

Free Space Detection

Detection

Localization

Camera

Radar

Others

GPS

Lidar

Prediction

Trajectory Planning

Behaviour Planning

Map

Image source: Udacity





The sector is attracting significant investment from new players such as Argo AI LLC, a year-old startup with $1 billion, and Mapbox with a

$164 million investment.

19

Sources: Stephen Lawson; The Connected Car; Toyota Pledges DSRC Connected-Car System for US Vehicles in 2021; April 2018; https://www.theconnectedcar.com/author.asp?section_id=627&doc_id=742305& Terry Costlow; SAE International Autonomous vehicle engineering; Big Data, Big Challenges; March 2018; https://www.sae.org/news/magazines Chad Morley; IoT for all; 7 Connected car trends fueling the future; March 2018; https://medium.com/iotforall/7-connected-car-trends-fueling-the-future-946b05325531 Mark Bergen; Hyperdrive/ Bloomberg; Nobody wants to let Google win the war for maps all over again; February 2018;https://www.bloomberg.com/news/features/2018-02-21/nobody-wants-to-let-google-win-the-war-for-maps-all-over-again Akshay Jadhav; Allied Market Research; Collision Avoidance System Market Overview; November 2017;https://www.alliedmarketresearch.com/collision-avoidance-system-market Jim Colquitt; The Invesco white paper series; Driverless cars: How innovation paves the road to investment opportunity; November 2017; https://www.invesco.com/corporate/dam/jcr:dd5f58ed-02bc-4b5a-9af1-c86ee19e2737/II-AUTODR-WP-2-E.pdf Todd Simon; Massive autonomous vehicle sensor data: What does it mean?; May 2017; https://www.datanami.com/2017/05/15/massive- autonomous-vehicle-sensor-data-mean/ E-Spin; Autonomous car : Perception,localization,mapping; March 2018; https://www.e-spincorp.com/2018/03/07/autonomous-car- perceptionlocalizationmapping/

AV developers are pursuing two mapping options:

• Granular, high-definition maps constructed using vehicles equipped with lidar and cameras

• Feature mapping, which doesn’t necessarily need lidar, but only maps certain road features to enable navigation using cameras and (sometimes) radar

Mapmakers can enhance both approaches by using a fleet of vehicles, either manned or autonomous, with the sen-sor packages required to collect and update the maps continuously. Or OEMs could ‘crowd source’ real-time detail from information gathered by the fleet of connected AVs – such as Tesla recently announced.

For accurate localization, companies compare the infor-mation gathered by onboard sensors (including GPS) to corresponding HD maps. This provides a reference point the vehicle can use to identify, with great accuracy, exact-ly where it is located (including lane information) and in what direction it’s headed.

While Google may be perceived to be the leaders in mapping, the sector is attracting significant investment from new players such as Argo AI LLC, a year-old startup with $1 billion backing by Ford, and Mapbox with a $164 million investment.

I N T R O D U C T I O N C O N T R I B U T O R S A B O U T T H E R E S E A R C H T R E N D S I N A U T O N O M O U S I N V E S T M E N T S I N A U T O N O M O U S I N D U S T R Y A N A L Y S I S K E Y T A K E A W A Y SI N T E R V I E W

Sagar Behere Toyota Research Institute

I N T E R V I E W K E Y T A K E A W A Y SI N D U S T R Y A N A L Y S I S

20 A U T O M O T I V E S O F T W A R E I N T H E A G E O F A U T O N O M O U S

What do OEMs wantI N T E R V I E W

In relation to Automotive Software in the age of Autonomous, Automotive IQ interviewed Sagar Behere.

How do you define an Operational Design Domain, and how is it used in the context of testing ADS and AD?

The Operational Design Domain specifically describes and defines the conditions under which the AD system is intended to operate. It consists of a number of different things but broadly speaking it’s about where it’s okay to test AD function and when is it okay to test it; it’s not just about okay to test, but it defines the scope of operation. The follo- wing are typically considered in an ODD: the type of roads, whether urban, highway, interior and so on; also what are the maximum speeds of operation? Also the when? - what type of weather, daytime or night time, and so on. Basically, the use of the ODD is to decide whether it’s okay to test the vehicle or not, based on whether the conditions are prevalent for which the system was designed. But it’s also a way for the system to understand when it’s okay to operate and when it is not okay to operate.

What are good safety-case arguments when testing prototype AD systems on public roads?

I will answer this and the next question together. When testing prototype systems on public roads the one thing we always want to ensure is correct and complete evidence and arguments and proof, that the prototype AD vehicle doesn’t pose an unrea- sonable threat or risk to traffic, the environment and to traffic participants inside and outside the vehicle. If possible, already at the test-stage, the system should make it safer for traffic and for the human; how you go about it really depends on which elements in the system, including the driver, you are counting on to make the system safe.. In the early stages of prototype-vehicles, you define that safety is largely handled by the human driver, and so the safety arguments in that case would rely on ensuring there’s always sufficient time for the human driver to understand maybe a potentially dangerous situation is emerging, and the human driver also then has the opportunity to take control of the vehicle and bring it to a safer state. The arguments here would define: Can you drive the vehicle in a way that there’s always enough time for the human to detect something is going wrong? Can you guarantee the vehicle will always remain controllable, like it won’t “run away” from the human driver? And, thirdly, is the human driver always clear what the vehicle is doing; what mode it is in, and so on? For example, you should try to avoid a vehicle that’s only partially autonomous because of some fault, so

Mr. Behere is the Senior Manager for Systems Engi- neering at Toyota Research Institute, and currently the architect and systems inte- grator for complex, safety- critical systems.

He holds a PhD in architectures for highly automated systems, and is skilled within areas such as software and firmware development, control system designs, cyber-security, embedded hardware etc.

I N T R O D U C T I O N C O N T R I B U T O R S A B O U T T H E R E S E A R C H T R E N D S I N A U T O N O M O U S I N V E S T M E N T S I N A U T O N O M O U S I N D U S T R Y A N A L Y S I S K E Y T A K E A W A Y SI N T E R V I E W

21 A U T O M O T I V E S O F T W A R E I N T H E A G E O F A U T O N O M O U S

maybe the steering is autonomous but the braking and acceleration are not.

Now, as the capabilities of the AD system increases, eventually need to permit the system to perform actions from which you know a human driver may be unable to recover in case they’re wrong; and to fully exploit the capabilities of this technology you even-tually need to let it make commands that can be very dangerous if they’re wrong. So, at this time you start building the safety case for saying: What makes those commands definitively be correct and safe? Why would they not be wrong? For example, if you look at many autonomous-driving programmes, when the AD system wants to change lanes, in the early phases of development it depends on the human driver to verify that the situation is safe, and the human driver typically indicates this by giving the turning signal telling the AD system it’s okay to change lanes, and then it performs a lane change. But as the system develops further you don’t want the human driver to be the part that says this is safe, you want the system itself to determine when it’s safe. And so then the safety case would turn on: Can the system assess the situation with sufficient accura-cy? What are some of the malfunctions that could prevent the car from knowing it’s not safe to make a lane change? -and so on. So, it really depends on who is responsible for safety?

Is there any step change in the system architecture when specific ODD expansions are introduced?

I think so. Normally, when you’re driving on high-ways, it’s considered to be a more structured and well-defined environment and so it’s easier to define what the correct behaviour of the system should be, and what are some of the safety steps in case there’s a problem?. When you go from highways to urban environments, there’s a substantial increase in the difficulty and complexity of the autonomous- driving task, and this, in turn, necessitates a change in the architecture; so although highway driving is at higher speeds, it’s a more structured environment; but in urban driving - if you drive through downtown Dusseldorf, for example, you’ll see a lot of situations which might not be easy for an AD system to under-stand. This capability of understanding what’s going on and therefore deciding what’s the right thing to do requires a level of semantics which I don’t think our cars have yet, at least the production cars. And so that’s a big challenge.

Will we ever be able to conclude that an autonomous vehicle is fully safe in any scenario? If yes, when do you predict this will come?

I think this question is phrased in a way that the answer has to be, “No!”. Because if you say: Will it be fully safe in any scenario? - well, it would be very difficult to say: “Yes, this is going to be the case.” But I believe we can definitely come to a conclusion some day that autonomous vehicles are safer than the average human being in any scenario; and the magnitude by which these vehicles are safer than human beings will keep increasing as time passes; so, initially, there might be a time when you make some arguments they are just as safe as human beings, if not safer, but then you can say they’re maybe are twice as safe, three times as safe, and safety is not a single dimension; there are many, many different aspects of safety and maybe they’re safer in some aspects and less safe in other aspects. But someday we should be able to reasonably state that autonomous vehicles are safer along most of the dimensions than human beings - definitely safer enough that they will make traffic, as a whole, safer.



C O N T R I B U T O R S A B O U T T H E R E S E A R C H T R E N D S I N A U T O N O M O U S I N V E S T M E N T S I N A U T O N O M O U S I N D U S T R Y A N A L Y S I S I N T E R V I E W K E Y T A K E A W A Y S

22

As indicated by the respondents of the IQ Automotive survey, by far the most valued self-driving functions remain collision avoidance and AI driven passive and active safety systems.

The ever increasing computational power of modern GPUs and CPUs enables manufacturers to reduce latency and improve redundancy while still driving down costs of safety systems. The evolution of machine learning into self-learning neural network AI, blends the precision of automation with the cognitive capabilities of humans to continually improve safety and reduce traffic fatalities. Although Biometrics is a relatively new development, the advances being made, in particular in AI driven facial recognition, have seen biometric vehicle access enter the top 3 trends.

The top challenges this year remain the limited computing power and related costs to process the massive amounts of data generated by autonomous vehicles.

This also has an impact on teaching the AI neural network algorithms, where millions of data points have to be collected, to make vehicles contextually aware of their environment as well as train them to respond appropriately. With the industry relying heavily on road testing as final verification of the system, the uncertainties around global road testing regulations is both impacting on consumer confidence and the ability of manufacturers to speed up adoption of higher levels of automation. Related to regulation is the setting of cohesive and clear standards for manufacturers to follow when operating test fleets.

Seen as pivotal technologies driving the automotive future connectivity, AI and data pro-cessing are attracting sizeable investment.

The Auto-Tech segment, which includes invest-ments in connectivity, autonomous, software, and intellectual property, accounted for $23.3 billion, or 43.9% of all Automotive deals in 2017: The two largest deals being Intel’s acquisition of MobileEye and Samsung’s acquisition of Harman Interna-tional. This trend is set to continue as evidenced by ZF Friedrichshafen’s acquisition of Beespeed Automatizari’s automotive unit and Continental’s acquisition of EasyMile. At the same time certain sectors are experiencing a geographical shift in investment: In 2017 China’s artificial intelligence startups took 48% of all dollars going to AI startups globally. In deep learning also China published six times more patents than the US.

Future trends Major challenges Future investments

K E Y T A K E A W A Y S

Sources: Jeff Zaleski; PWC; Global automotive deals insights: Year-end 2017; February 2018; https://www.pwc.com/us/en/industrial-products/publications/assets/pwc-automotive-industry-mergers-acquisitions-q4-2017.pdf Aditya Chaturvedi; Geospatial world; 13 major Artificial Intelligence trends to watch for in 2018; February 2018; https://www.geospatialworld.net/blogs/13-artificial-intelligence-trends-2018/

I N T R O D U C T I O N C O N T R I B U T O R S A B O U T T H E R E S E A R C H T R E N D S I N A U T O N O M O U S I N V E S T M E N T S I N A U T O N O M O U S I N D U S T R Y A N A L Y S I S I N T E R V I E W K E Y T A K E A W A Y S

INTRODUCING AUTOMOTIVE IQAutomotive IQ is an online portal dedicated to producing in-depth value-adding content for the global automotive industry.

We provide industry professionals and senior business decision makers with exclusive information, news and industry trends through presentations, articles, interviews, webinars, white- papers, seminars and conferences. Visit www.Automotive-IQ.com today, join our community for free and connect with over 50.000 automotive professionals.

BECOME A MEMBER

Follow Automotive IQ to keep up-to-date with the latest news!

https://www.automotive-iq.com/

https://www.automotive-iq.com/become-a-member-today

https://www.facebook.com/Automotive-IQ-828886833858677/

https://plus.google.com/+Automotiveiqnews

https://www.linkedin.com/groups/3561792/profile

https://twitter.com/Automotive_IQ

AUTOMOTIVE SOFTWARE IN THE AGE OF AUTONOMOUS

Documents

Transcript of AUTOMOTIVE SOFTWARE IN THE AGE OF AUTONOMOUS