Connected And Automated Vehicles - The Market View - ARPA-E
Connected and Automated Vehicles – The Market View ARPA-E NEXTCAR Program Kickoff April 2017
Three main disruptive forces will fundamentally transform how people and things move in the future Electric Vehicles Mobility Value Chain Changing consumer and societal demands Moving people Moving goods Autonomous Vehicles Mobility as a Service 2016 KPMG LLP, a Delaware limited liability partnership and the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative (“KPMG International”), a Swiss entity. All rights reserved. NDPPS ID 543500 Collaboration in the future Mobility Ecosystem 2
This is one of the most pervasive global mega-trends and will have far reaching implications Industry execs believe that . Up to 50% Passenger miles travelled will increase . Up to Cost per mile could go down Up to 40% 10% of consumers will not want to own a car, as new mobility services begin to meet consumer needs as fleet services use vehicles more efficiently 5x Miles per vehicle could increase . following the growth of the mega-city and their suburbs The number of major ecosystem players will decline due to removing the driver cost, longer vehicle lives, new energy sources, technologies and mobility scaling ? as sector convergence leads to consolidation Sources: Department for Transport, KPMG Global Auto Executive Survey 2017,KPMG UK Mobility 2030 analysis 2016 KPMG LLP, a Delaware limited liability partnership and the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative (“KPMG International”), a Swiss entity. All rights reserved. NDPPS ID 543500 3
Autonomous drive is coming faster than many realize 2016 Autonomous features are already commercially available and impacting accident rates: 2030 Collision warning & brake support have been shown to reduce incidents of rear-end crashes with frontal impacts by 45%1 Fully autonomous cars without manual control options become available9 Tesla’s autopilot autonomy feature demonstrated a 50% reduction in the number of accidents2 2020 2025 Full and partially autonomous cars will be an 87 billion industry10 The number of car accidents falls by 15%8 Ford to sell line of fully autonomous vehicles13 The number of car accidents falls by 50%8 Fully Autonomous vehicles become commercially available in the U.S., Toyota, Nissan, and Volvo offer models, Tesla to follow soon after3, 2040 75% of the traffic will be full or partially autonomous vehicles12 The number of car accidents falls by 70%8 2026 2035 100% of cars sold will have some level of autonomy7 75% of all the passenger vehicles bought will be fully autonomous11 4, 6 9% of vehicles on the road will have some autonomous feature52) Eletrek; 3) Wired; 4) Nissan Motors; 5) Strategy Analytics; 6) CNN, Volvo; 7) Adam Jones, Morgan Stanley; 8) KPMG analysis; 9) HIS; 10) LUX Footnote: Source: 1) Evaluation of Rear-End Collision Avoidance Technologies, Volvo Car Corporation; Research; 11) Navigant Research; 12) Institute of Electrical and Electronics Engineers; 13) Raj Nair, Ford Group Vice President Source: 2016 KPMG LLP, a Delaware limited liability partnership and the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative (“KPMG International”), a Swiss entity. All rights reserved. NDPPS ID 543500 4
Uber demonstrated that Mobility as a Service (MaaS) can very quickly and materially unlock value (even with “driver required”) Uber captured 1B from a 200M local taxi market – how did they do that? Uber Global Bookings Revenue ( B) 1B in Bay Area Uber revenue (2015) 26.2 CAGR 236% New mobility revenue 10.8 Taxi cannibalization 200M Bay Area taxi market Disruption occurred in roughly five years after 2010 launch so this can happen very fast Note: Uber revenues are gross paid by passengers Sources: Business Insider; LA Times 2016 KPMG LLP, a Delaware limited liability partnership and the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative (“KPMG International”), a Swiss entity. All rights reserved. NDPPS ID 543500 2.9 0.7 2013 2014 2015 2016 (F) In fact, Uber is expanding very rapidly 5
. Uber networks already provide access to 69% of US population in and around most cities New York City MSA Chicago MSA Type A: Dense urban center, large suburban metro area, high public transit usage Los Angeles MSA Type B: Significant urban center, sprawling suburban metro area, medium public transit usage Type C: Unclear urban center, vast suburban metro area, low public transit usage 50 miles 40 20 miles 30 20 10 miles 10 10 20 30 40 10 50 20 30 60 miles 40 50 60 miles Average Wait for Closest Uber: 6:43 Average Wait for Closest Uber: 6:09 Minutes Wait: Average Wait for Closest Uber: 5:24 n 12,928 n 27,908 Less than 3:00 3:00 – 5:00 5:00 – 7:00 n 18,107 7:00 – 10:00 More than 10:00 Not Available 1) Census tracts were sampled a minimum of four times. Those that returned an available Uber at least once were defined as having regular Uber service 2) Average wait time calculation is population weighted and only includes areas where Uber is available 2016 KPMG LLP, a Delaware limited liability partnership and the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative (“KPMG International”), a Swiss entity. All rights reserved. NDPPS ID 543500 2017 KPMG LLP, a Delaware limited liability partnership and the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative (“KPMG International”), a Swiss entity. All rights reserved. 6 6
This combined with autonomy will drive over a trillion mile increase in Passenger Miles Traveled (PMT) for underserved populations U.S. personal miles traveled (PMT) per capita 2014–2050 (Kmiles) “I do not have to take keys away from dad” Parents can be everywhere at the same time 79 % of people asked in focus groups would want mobility options for seniors 82 % of people asked in focus groups would want mobility options for kids Safe independence for the kids Convenience of “my time” Independence for seniors Note: (a) Discounted 25 percent from U.S. Bureau of Transportation Statistics (BTS) total Vehicle-miles traveled (VMT) for 1995, 2001, 2009, 2014 (assumed to be commercial miles), (b) multiplied by NHTS occupancy rates applied 2009 rate to 2014 numbers. Source: U.S. BTS data, NHTS data, U.S. Census data, KPMG Analysis 2016 KPMG LLP, a Delaware limited liability partnership and the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative (“KPMG International”), a Swiss entity. All rights reserved. NDPPS ID 543500 7
By 2040 autonomy and mobility as a service adoption will reshape car ownership models and reset the market New Passenger Vehicle Sales Passenger Vehicle Car Parc Passenger Vehicle VMT 20 350 4.0 0.4% 3.5 300 2.5 2.0 Million Vehicles 3.0 Million Vehicles Trillion Miles 0.2% 1.8% 250 200 150 15 10 1.5 100 1.0 5 50 0.5 0.0 2015 2020 2025 2030 2035 2040 VMT grows more rapidly than PMT due to a drop in average occupancy per vehicle 0 2015 2020 2025 2030 2035 2040 Growth in the car parc declines as AV MaaS vehicles eliminate the need for a portion of personally owned vehicles Source: KPMG Analysis Note: Passenger vehicle VMT analysis excludes non-MaaS commercial POV 2016 KPMG LLP, a Delaware limited liability partnership and the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative (“KPMG International”), a Swiss entity. All rights reserved. NDPPS ID 543500 0 2015 2020 2025 2030 2035 Non-autonomous vehicle sales fall, but are replaced by autonomous personal and MaaS vehicles POV AV POV AV MaaS 8 2040
We will likely see a fundamental reinvention of the car around mission segments Shift from multi-mission share by vehicle class US light vehicle market shares by segment 100% to mission, environment and premium tailored solutions Total Market: 17.5M units Other(1) 75% Volkswagen Hyundai Renault-Nissan 50% Honda Mission Fiat Chrysler Toyota 25% Ford GM 0% A/B C D Pickup SUV Other Global light vehicle market shares by segment 100% (Necessity, nonengaging driving, engaging driving, specialty) Premium Total Market: 88.7M units Environment Other(1) 75% Honda Fiat Chrysler Ford 50% (Dense urban, inner ring, outer ring, exurban, etc.) (Luxury, performance, technology, etc.) Hyundai GM Renault-Nissan 25% Toyota Volkswagen 0% A B C D Pickup SUV Other Notes: (1) Other in US includes Fuji Heavy, BMW, Daimler, Mazda, Mitsubishi, Tata and other OEMs. Other in global includes PSA, Suzuki, SAIC, BMW, Daimler, Mazda and other OEMs. Source: LMC Global Automotive Sales Forecast (Q4 2015) 2016 KPMG LLP, a Delaware limited liability partnership and the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative (“KPMG International”), a Swiss entity. All rights reserved. NDPPS ID 543500 9
Changes will be at least as impactful in commercial vehicles and logistics 2016 KPMG LLP, a Delaware limited liability partnership and the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative (“KPMG International”), a Swiss entity. All rights reserved. NDPPS ID 543500 10
Amazon and Uber have already started to disrupt the parcel delivery market Amazon Flex (Sept. 2015 ) Uber RUSH (Oct. 2015 ) Allows same day delivery, available in 30 US cities Costs Amazon 2-3 per package1,3 Amazon pays the driver hourly fees based on demand (no fixed cost) Same day delivery available in NYC, San Francisco and Chicago Cost to the sender can be as low as 5-7 per package2,3 Offers real time tracking Uber pays the driver 75-80% of the fee (no fixed cost) UPS Ground: 8 for next day delivery within a local area3 Note: *Cost from an Amazon facility to the delivery location based on the 18-25 cost to deliver 7 to 15 packages per hour; 2) minimum charge for the first mile; 3) Average UPS Ground shipment cost per package in 2015 was 7.98; 4) guaranteed delivery time is by end of day next day for shipment within Manhattan based on UPS website Sources: Uber, Amazon websites 2016 KPMG LLP, a Delaware limited liability partnership and the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative (“KPMG International”), a Swiss entity. All rights reserved. NDPPS ID 543500 11
Some segments of the local pick-up / delivery market, exposed to MaaS disruption, present significant growth opportunities B2B Parcel / Small Packages Sensitive Materials Large / Heavy Packages P2P Express / Overnight Multi-day Express / Overnight Restocking during peak demand Recurring inventory shipments Retail deliveries from local shops Scheduled restaurant food shipments Grocery and flower deliveries Baked goods from retailers Flowers delivered to family members Baked goods delivered to friends Fragile impromptu inventory restocking Fragile inventory restocking Pottery, glassware, etc. Pottery, glassware, etc. Fragile personal items Fragile personal items Inter-firm legal documents Inter-firm legal documents Court documents Court documents Intra-firm legal documents Intra-firm legal documents Recurring large inventory shipments Furniture deliveries Furniture deliveries Personal Furniture shipment Personal Furniture shipment Restaurant restocking Perishables during the day Fragile Packages B2C Impromptu inventory restocking Multi-day Express / Overnight Multi-day Retail deliveries from Birthday gift deliveries Birthday gift deliveries local shops High Growth Area 2016 KPMG LLP, a Delaware limited liability partnership and the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative (“KPMG International”), a Swiss entity. All rights reserved. NDPPS ID 543500 12
What could this mean? The UK may be informative: Annual PDT per Capita by Mission by Year (km)(a) 11,135 333 975 2,188 3,276 -6% -12% -2% 4,363 10,439 342 886 School / Religion 1,922 Shopping / Errands 2,188 78 148 -12% 1,922 65 150 Transport Someone 484 3,217 4,072 -7% 2008 Annual PDT per Capita by Shopping/Errands by Year (km)(a) 2014 Work Social -11% Other non-escort Personal business medical 430 Food shopping 538 Personal business other 739 Non food shopping 553 -3% 925 2008 -20% 2014 2016 KPMG LLP, a Delaware limited liability partnership and the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative (“KPMG International”), a Swiss entity. All rights reserved. NDPPS ID 543500 13
Deep Learning development will be critical to this future to enable vehicles to handle complex situations Input Layer A neural network is organized into layers, with each layer using the layer directly upstream as inputs Information is fed into the input layer Artificial neurons (nodes) within several “hidden layers” trigger when a certain characteristic within the input data is identified Hidden layers Nodes identify a characteristic by comparing a weighted total of identified upstream characteristics to a threshold value While upstream nodes identify basic characteristics, such as a gradient in a picture, downstream nodes are able to identify more complex objects, such as an eye Output Layers Training the network involves adjusting the input weights between nodes so that it gives the desired response when presented with particular inputs Deep neural networks allows computers to handle traditionally difficult tasks such as identifying animals, people, and objects in a picture, despite them having no set shape, color, etc. 2016 KPMG LLP, a Delaware limited liability partnership and the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative (“KPMG International”), a Swiss entity. All rights reserved. NDPPS ID 543500 2017 KPMG LLP, a Delaware limited liability partnership and the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative (“KPMG International”), a Swiss entity. All rights reserved. 14 14
With Deep Learning, miles are like gold The emergence of autonomous vehicles is placing more emphasis on the ‘nervous system’ consisting of sensors, CPU, and software 2016 KPMG LLP, a Delaware limited liability partnership and the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative (“KPMG International”), a Swiss entity. All rights reserved. NDPPS ID 543500 2017 KPMG LLP, a Delaware limited liability partnership and the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative (“KPMG International”), a Swiss entity. All rights reserved. 15 15
Deep Learning summary From man to machine, driving is rapidly changing. Vehicle operation and ownership is changing. Mobility services will be a major new market and therefore a critical battleground. Most car companies won’t simply be automakers anymore. 0101000101010 0101000101010 1000101010 Investment focus is changing. Miles are like gold! 2016 KPMG LLP, a Delaware limited liability partnership and the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative (“KPMG International”), a Swiss entity. All rights reserved. NDPPS ID 543500 2017 KPMG LLP, a Delaware limited liability partnership and the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative (“KPMG International”), a Swiss entity. All rights reserved. 16 16
Deep Learning summary Power of the fleet will eclipse the importance of the individual car. Highly secure, fleet-wide software architectures will be a new basis of competition. Ideation and innovation will change. The nervous system will become the center of the 2016 KPMG LLP, a Delaware limited liability partnership and the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International vehicle design. Cooperative (“KPMG International”), a Swiss entity. All rights reserved. NDPPS ID 543500 Talent is the new arms race. 2017 KPMG LLP, a Delaware limited liability partnership and the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative (“KPMG International”), a Swiss entity. All rights reserved. 17 17
Key questions/ideas: How can we most sustainably support the massive increase coming in PMT/VMT? If a car “can’t” crash what will it look like? The economics of electric vehicles dramatically change with a low speed “autonomous taxi bot” for urban areas. What technologies could make the promise real? Mission specific vehicles could unlock significant societal value. How can you support and exploit this idea? 2016 KPMG LLP, a Delaware limited liability partnership and the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative (“KPMG International”), a Swiss entity. All rights reserved. NDPPS ID 543500 18
I see. I think. I drive. I learn. Conclusion Deep learning enabled cars only 2016 KPMG LLP, a Delaware limited liability partnership and the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative (“KPMG International”), a Swiss entity. All rights reserved. NDPPS ID 543500 2017 KPMG LLP, a Delaware limited liability partnership and the U.S. member firm of the KPMG network of independent member firms affiliated with KPMG International Cooperative (“KPMG International”), a Swiss entity. All rights reserved. 19 19
Those that returned an available Uber at least once were defined as having regular Uber service 2) Average wait time calculation is population weighted and only includes areas where Uber is available . Uber RUSH (Oct. 2015 ) Same day delivery available in NYC, San Francisco and Chicago Cost to the sender can be as low as . 5-7 per .
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Dr. Sunita Bharatwal** Dr. Pawan Garga*** Abstract Customer satisfaction is derived from thè functionalities and values, a product or Service can provide. The current study aims to segregate thè dimensions of ordine Service quality and gather insights on its impact on web shopping. The trends of purchases have
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