1. | EXECUTIVE SUMMARY AND CONCLUSIONS |
1.1. | Why autonomous cars |
1.2. | Levels of automation in cars |
1.3. | Future mobility scenarios: autonomous and shared |
1.4. | Travel demand and mobility as a service (MaaS) |
1.5. | Passenger car sales will peak earlier than expected |
1.6. | Autonomous driving testing race in California |
1.7. | Roadmap of autonomous driving functions |
1.8. | Overview of announced autonomous car launch time |
1.9. | The race for robotaxis |
1.10. | Autonomous driving is changing the automotive supply chain |
1.11. | Lidar cost will drop significantly |
1.12. | Global Lidar market size by technology |
1.13. | The evolving role of the automotive radar towards full 360degree imaging |
1.14. | Radar market forecasts 2020-2040 in all levels of autonomy/ADAS in vehicles and trucks (market value) |
1.15. | SWIR: incumbent and emerging technology options |
1.16. | Many layers of an HD map for autonomous driving |
1.17. | Application of AI to autonomous driving |
1.18. | There is no single AI solution to autonomous driving |
1.19. | Cybersecurity risks for autonomous cars |
1.20. | Why Vehicle-to-everything (V2X) is important for future autonomous vehicles |
1.21. | Use cases of 5G NR C-V2X for autonomous driving |
1.22. | Global autonomous passenger car sales forecast 2020-2040 |
1.23. | Global private autonomous car sales forecast 2020-2040 |
1.24. | Global shared autonomous car sales forecast 2020-2040 |
1.25. | Global autonomous passenger cars market forecast 2020-2040 - summary |
1.26. | Global autonomous car market forecast 2020-2040: AV sales and mobility service (market value) |
1.27. | Global autonomous car sales revenue forecast 2020-2040 by level of autonomy |
1.28. | Global autonomous car market forecast 2020-2040 (market value: $ million) - summary |
1.29. | Global autonomous car sales revenue breakdown 2020-2040 (market value) |
1.30. | Global autonomous car sales revenue breakdown 2020-2040 (market value) - summary |
1.31. | Private autonomous cars annual sales forecast by region 2020-2040 (thousand) |
1.32. | Shared autonomous cars annual sales forecast by region 2020-2040 (thousand) |
1.33. | Global autonomous car and robotaxi market forecast by region 2020-2040 - summary |
1.34. | Global autonomous car and robotaxi market value forecast by region 2020-2040 |
1.35. | Global autonomous car and robotaxi market value forecast by region ($ million) 2020-2040 - summary |
2. | INTRODUCTION |
2.1. | Why autonomous cars |
2.2. | Challenges to traditional OEMs |
2.3. | Future mobility scenarios: autonomous and shared |
2.4. | Product and value positioning of autonomous cars |
2.5. | OEMs are becoming mobility service providers |
2.6. | What are the levels of automation in cars |
2.7. | The automation levels in details |
2.8. | Functions of autonomous driving at different levels |
2.9. | Roadmap of autonomous driving functions |
2.10. | Chess pieces: autonomous driving tasks |
2.11. | Typical toolkit for autonomous cars |
2.12. | Anatomy of an autonomous car |
2.13. | Evolution of sensor suite from Level 1 to Level 5 |
2.14. | Two development paths towards autonomous driving |
2.15. | Autonomous driving is changing the automotive supply chain |
2.16. | Auto OEMs' partnerships in autonomous driving |
3. | AUTONOMOUS PASSENGER CARS: KEY PLAYERS AND CASE STUDIES |
3.1. | Overview of autonomous car launch time by OEMs |
3.2. | AV testing distance in California by companies |
3.3. | Waymo leading the game in terms of disengagement rate |
3.4. | AV testing by auto OEMs in 2018 |
3.5. | AV testing in Beijing, China |
3.6. | AV development in China: from testing to pilot services |
3.7. | Volkswagen investing in autonomous driving |
3.8. | The world's first 'L3 autonomous car'? |
3.9. | Audi A8 autonomous sensor suite |
3.10. | Daimler-Bosch joint force in autonomous services |
3.11. | The world's first fully autonomous parking |
3.12. | BMW's strategy towards autonomous driving (1) |
3.13. | BMW's strategy towards autonomous driving (2) |
3.14. | BMW partners with Daimler for Level 4 autonomation |
3.15. | Ford 'Autonomous 2021' |
3.16. | Toyota's investment in autonomous driving |
3.17. | Toyota's dual approach to autonomy |
3.18. | e-Palette - Toyota's multipurpose mobility platform |
3.19. | Nissan-Renault-Mitsubishi roadmap to autonomy |
3.20. | Renault's Eyes-off/Hands-off system |
3.21. | Honda catching up in the autonomy race |
3.22. | Hyundai's strategic partnerships on autonomous driving |
3.23. | Hyundai-Aptiv joint venture for L4+ autonomous driving |
3.24. | Volvo betting on highly autonomous driving |
3.25. | PSA's Autonomous Vehicle for All program |
3.26. | BYD's open-source AV technology platform |
3.27. | Geely's four-step plan for autonomous driving |
3.28. | Changan is testing AVs on the 5G-V2X platform |
4. | ROBOTAXI: AUTONOMOUS MOBILITY AS A SERVICE |
4.1. | OEMs are becoming mobility service providers |
4.2. | Mobility services launched by auto OEMs |
4.3. | Mobility service cost: autonomous vs non-autonomous |
4.4. | Overview of robotaxi launch time announced by AV companies |
4.5. | Waymo started semi-commercial robotaxi service |
4.6. | Waymo Driver's sensor architecture |
4.7. | Waymo's strategic partnerships |
4.8. | GM is betting on autonomous cars through Cruise |
4.9. | Cruise sensor architecture |
4.10. | Uber is building autonomous car on Volvo platform |
4.11. | Change in sensor suite in Uber's autonomous cars |
4.12. | Zoox is developing self-driving cars from scratch |
4.13. | Will Tesla robotaxi hit the road in 2020? |
4.14. | Tesla autopilot sensor suite |
4.15. | Chinese Pony.ai is testing robotaxi in China and US |
4.16. | Pony.ai's sensor fusion |
4.17. | Chinese AutoX launched the first robotaxi service in CA |
4.18. | AutoX is expanding its robotaxi business |
4.19. | AutoX sensor set |
4.20. | Baidu robotaxi service: Apollo Go |
4.21. | DiDi's plan to launch robotaxi services |
4.22. | WeRide plans to launch robotaxi in China by 2020 |
4.23. | Aptiv aims to achieve fully driverless by 2020 |
4.24. | Yandex launched robotaxi service in Russia |
4.25. | Renault and Nissan preparing for robotaxi service |
4.26. | Voyage targeting on robotaxi in retirement community |
4.27. | Sensor configuration of Voyage autonomous car |
4.28. | Voyage's strategic partnership with Enterprise |
5. | ENABLING TECHNOLOGIES: LIDARS, RADARS, CAMERAS, AI SOFTWARE AND COMPUTING PLATFORM, HD MAP, TELEOPERATION, CYBERSECURITY, 5G AND V2X |
5.1. | Overview |
5.1.1. | Chess pieces: autonomous driving tasks |
5.1.2. | Typical toolkit for autonomous cars |
5.1.3. | Anatomy of an autonomous car |
5.1.4. | Evolution of sensor suite from Level 1 to Level 5 |
5.1.5. | What is sensor fusion? |
5.1.6. | Sensor fusion: past and future |
5.2. | Lidars |
5.2.1. | 3D Lidar: market segments & applications |
5.2.2. | 3D Lidar: four important technology choices |
5.2.3. | Comparison of Lidar, Radar, Camera & Ultrasonic sensors |
5.2.4. | Automotive Lidar: SWOT analysis |
5.2.5. | Automotive Lidar: operating process & requirements |
5.2.6. | Emerging technology trends |
5.2.7. | Comparison of TOF & FMCW Lidar |
5.2.8. | Laser technology choices |
5.2.9. | Comparison of common Laser type & wavelength options |
5.2.10. | Beam steering technology choices |
5.2.11. | Comparison of common beam steering options |
5.2.12. | Photodetector technology choices |
5.2.13. | Comparison of common photodetectors & materials |
5.2.14. | 106 Lidar players by geography |
5.2.15. | Lidar hardware supply chain for L3+ vehicles |
5.2.16. | Beam steering technology |
5.2.17. | Mechanical Lidar players, rotating & non-rotating |
5.2.18. | Micromechanical Lidar players, MEMS & other |
5.2.19. | Pure solid-state Lidar players, OPA & liquid crystal |
5.2.20. | Pure solid-state Lidar players, 3D flash |
5.2.21. | Players by technology & funding secured |
5.2.22. | Lidars per vehicle by technology & common fonfigurations |
5.2.23. | Lidar configuration diagrams: L3, L4 & L5 vehicles |
5.2.24. | Average Lidar cost per vehicle by technology |
5.2.25. | L3 private vehicle market share by Lidar technology |
5.2.26. | L4 & L5 private vehicle market share by Lidar technology |
5.2.27. | L4 & L5 shared mobility market share by Lidar technology |
5.2.28. | Global Lidar unit sales by L3+ vehicle type |
5.2.29. | Global Lidar market size by L3+ vehicle type |
5.2.30. | Global Lidar unit sales by technology |
5.2.31. | Global Lidar market size by technology |
5.3. | Radars |
5.3.1. | Towards ADAS and autonomous driving: increasing sensor content |
5.3.2. | Towards ADAS and autonomous driving: increasing radar use |
5.3.3. | SRR, MRR and LRR: different functions |
5.3.4. | The evolving role of the automotive radar towards full 360degree imaging |
5.3.5. | Automotive radars: role of legislation in driving the market |
5.3.6. | Automotive radars: frequency trends |
5.3.7. | Automotive radars: frequency trends |
5.3.8. | Automotive radars: frequency trends |
5.3.9. | Radar: which parameters limit the achievable KPIs |
5.3.10. | Impact of frequency and bandwidth on angular resolution |
5.3.11. | Why are radars essential to ADAS and autonomy? |
5.3.12. | Towards autonomy: Increasing semiconductor use |
5.3.13. | Performance levels of existing automotive radars |
5.3.14. | Radar players and market share |
5.3.15. | Radar market forecasts 2020-2040 in all levels of autonomy/ADAS in vehicles and trucks (unit numbers) |
5.3.16. | Radar market forecasts 2020-2040 in all levels of autonomy/ADAS in vehicles and trucks (market value) |
5.3.17. | Radar market forecasts 2020-2040 in all levels of autonomy/ ADAS in vehicles and trucks (market value)- moderate |
5.3.18. | Radar market forecasts 2020-2040 in all levels of autonomy/ ADAS in vehicles and trucks (market value)- aggressive |
5.3.19. | Radar semiconductor market share forecast (GaAs, SiGe, Si) |
5.3.20. | Ten year (unit number) market forecasts for automotive radars |
5.3.21. | Benchmarking of semiconductor technologies for mmwave radars |
5.3.22. | The choice of the semiconductor technology |
5.3.23. | The choice of the semiconductor technology |
5.3.24. | SiGe: current and emerging performance levels |
5.3.25. | SiGe: current and emerging performance levels |
5.3.26. | SiGe: overview and comparison of manufacturers |
5.3.27. | SiGe BiCMOS: Infineon Technology |
5.3.28. | SiGe BiCMOS: NXP |
5.3.29. | SiGe BiCMOS: ST Microelectronics |
5.3.30. | A closer look at SiGe vs Si CMOS |
5.3.31. | A closer look at SiGe vs Si CMOS |
5.3.32. | Emerging all Si CMOS radar IC packages: NXP |
5.3.33. | Emerging all Si CMOS radar IC packages: ADI |
5.3.34. | Emerging all Si CMOS radar IC packages: TI |
5.3.35. | Many chip makers are on-board |
5.3.36. | Packaging trends: from discreet bare die (COB) to wafer-level packaging and beyond? |
5.3.37. | Packaging trends: AiP goes commercial? |
5.3.38. | Packaging trends: from discreet bare die (COB) to wafer-level packaging and beyond? |
5.3.39. | Packaging trends: from discreet bare die (COB) to wafer-level packaging and beyond? |
5.3.40. | Comparison of die vs packaged options |
5.3.41. | eWLP vs flip chip and BGA in terms of insertion loss |
5.3.42. | Radar packaging: Material opportunities |
5.3.43. | Glass and panel level packaging of radars? |
5.3.44. | Function integration trend: from discreet to full chip-level function integration |
5.3.45. | Function integration trends: towards true radar-in-a-chip |
5.3.46. | Evolution of radar chips towards all-in-one designs |
5.3.47. | Evolution of radar chips: all-in-one designs |
5.3.48. | Board trends: from separate RF board to hybrid to full package integration? |
5.3.49. | Hybrid board is the norm |
5.3.50. | Hybrid board: what is it |
5.4. | Cameras |
5.4.1. | Camera technology: an overview of the market |
5.4.2. | Camera technology: CMOS is the bright spot in semiconductor sales landscape |
5.4.3. | How many camera needed in various levels of autonomy |
5.4.4. | CMOS image sensors vs CCD cameras |
5.4.5. | Key components in a CMOS image sensor (CIS) |
5.4.6. | Front vs backside illumination |
5.4.7. | Process flow for back-side-illuminated CMOS image sensors |
5.4.8. | Global vs Rolling Shutter |
5.4.9. | Global shutter: pixel size limitation and read-out mechanism |
5.4.10. | TPSCo: leading foundry for global shutter FSI CMOS on 65nm node |
5.4.11. | TPSCo: its best-in-class performance and partners |
5.4.12. | Sony: pixel architecture and performance of FSI global-shutter CMOS |
5.4.13. | Sony: back-side-illuminated stacked global shutter CMOS (breakthrough?) |
5.4.14. | Sony: BSI global shutter CMOS with stacked ADC |
5.4.15. | Omnivision: 2.2um GS CIS for automotive |
5.4.16. | Hybrid organic-Si global shutter CIS with high-res and low-noise |
5.4.17. | Hybrid QD-Si GS CIS at IR and NIR: achieving small pixels by physical separation of charge conversion and storage |
5.4.18. | Hybrid QD-Si GS CIS at IR and NIR: achieving small pixels by physical separation of charge conversion and storage |
5.4.19. | Hybrid QD-Si GS CIS at IR and NIR: achieving small pixels by physical separation of charge conversion and storage |
5.4.20. | Why one needs NIR sensing in machine vision |
5.4.21. | NIR sensing: limitation of Si CMOS |
5.4.22. | OmniVision: making silicon CMOS sensitive to NIR |
5.4.23. | OmniVision: making silicon CMOS sensitive to NIR |
5.4.24. | Deep trench isolation: innovation to reduce cross-talk |
5.4.25. | Deep trench isolation: innovation to reduce cross-talk |
5.4.26. | What is SWIR or short-wave-infra-red? |
5.4.27. | Why SWIR in autonomous mobility |
5.4.28. | Other SWIR benefits: better animal or on-road hazard detection |
5.4.29. | SWIR sensitivity of different materials (PbS QDs, Si, polymers, InGaAs, HgCdTe, etc) |
5.4.30. | SWIR: incumbent and emerging technology options |
5.4.31. | The challenge of high resolution, low cost IR sensors |
5.4.32. | Silicon based SWIR sensors: innovation |
5.4.33. | Silicon based SWIR sensors: innovation |
5.4.34. | Why colloidal quantum dots? |
5.4.35. | Quantum dots: choice of the material system |
5.4.36. | Advantage of solution processing: ease of integration with ROIC CMOS? |
5.4.37. | How is the QD layer applied? |
5.4.38. | Other ongoing challenges |
5.4.39. | Emberion: QD-graphene SWIR sensor |
5.4.40. | Emberion: QD-Graphene-Si broadrange SWIR sensor |
5.4.41. | SWIR Vision Sensors: first QD-Si cameras and/or an alternative to InVisage? |
5.4.42. | SWIR Vision Sensors: first QD-Si cameras and/or an alternative to InVisage? |
5.4.43. | SWIR Vision Sensors: first QD-Si cameras and/or an alternative to InVisage? |
5.4.44. | SWIR Vision Sensors: first QD-Si cameras and/or an alternative to InVisage? |
5.4.45. | QD-ROIC Si-CMOS integration examples (IMEC) |
5.4.46. | QD-ROIC Si-CMOS integration examples (RTI International) |
5.4.47. | QD-ROIC Si-CMOS integration examples (ICFO) |
5.4.48. | QD-ROIC Si-CMOS integration examples (ICFO) |
5.5. | AI software and computing platform |
5.5.1. | Terminologies explained: AI, machine learning, artificial neural networks, deep neural networks |
5.5.2. | Artificial intelligence: waves of development |
5.5.3. | Classical method: feature descriptors |
5.5.4. | Typical image detection deep neutral network |
5.5.5. | Algorithm training process in a single layer |
5.5.6. | Towards deep learning by deepening the neutral network |
5.5.7. | The main varieties of deep learning approaches explained |
5.5.8. | There is no single AI solution to autonomous driving |
5.5.9. | Application of AI to autonomous driving |
5.5.10. | End-to-end deep learning vs classical approach |
5.5.11. | Imitation learning for trajectory prediction: Valeo (1) |
5.5.12. | Imitation learning for trajectory prediction: Valeo (2) |
5.5.13. | Hybrid AI for Level 4/5 automation |
5.5.14. | Hybrid AI for object tracking |
5.5.15. | Hybrid AI for sensor fusion |
5.5.16. | Hybrid AI for motion planning |
5.5.17. | Autonomous driving requires different validation system |
5.5.18. | Validation of deep learning system? |
5.5.19. | The vulnerable road user challenge in city traffic |
5.5.20. | Multi-layered security needed for vehicle system |
5.5.21. | Aurora: building the full-stack AD solution |
5.5.22. | Argo AI: fully integrated AD driving system for OEMs |
5.5.23. | Drive.ai: AD retrofitting kit |
5.5.24. | Momenta: the Chinese AD solution provider |
5.5.25. | Sensor fusion for Mpilot Highway and Parking |
5.5.26. | HoloMatic: the Xuanyuan platform |
5.5.27. | The coming flood of data in autonomous vehicles |
5.5.28. | Computing power needed for autonomous driving |
5.5.29. | Horizon Robotics: the Chinese embedded AI chip unicorn |
5.5.30. | The paradigm shift of AI computing |
5.5.31. | Horizon Robotics: software and hardware roadmap |
5.5.32. | By-wire and AV domain computer |
5.5.33. | Autonomous driving datasets |
5.5.34. | Waymo open dataset |
5.5.35. | Pandaset by Hesai and Scale |
5.5.36. | Oxford radar Robotcar dataset |
5.5.37. | Astyx Dataset HiRes2019 |
5.5.38. | Berkeley DeepDrive or BDD100K |
5.5.39. | Karlsruhe Institute of Technology and Toyota dataset |
5.5.40. | Cityscapes dataset presented in two 2015 and 2016 papers |
5.5.41. | Mapillary dataset presented in a 2017 paper |
5.5.42. | Apolloscape dataset by Baidu |
5.5.43. | Landmarks and Landmarks v2 by Google |
5.5.44. | Level 5 dataset by Lyft |
5.5.45. | nuScenes dataset by Aptiv |
5.5.46. | Datasets by University of Michigan and Stanford University |
5.5.47. | Sydney Urban Objects by the University of Sydney |
5.6. | High-definition (HD) map |
5.6.1. | Lane models: uses and shortcomings |
5.6.2. | Localization: absolute vs relative |
5.6.3. | RTK systems: operation, performance and value chain |
5.6.4. | Sensors (GPS): price and market adoption (in unit numbers) evolution of GPS sensors |
5.6.5. | HD mapping assets: from ADAS map to full maps for level-5 autonomy |
5.6.6. | Many layers of an HD map for autonomous driving |
5.6.7. | HD map as a service |
5.6.8. | Who are the players? |
5.6.9. | Key business model differentiation between HD mapping players |
5.6.10. | Campines relying on vertical integration to build HD maps (TomTom. AutoNavi, Google, Here Technologies, etc.) |
5.6.11. | Campines relying on camera to build HD maps (IvI5, Atlatec, Carmera, Mapper) |
5.6.12. | Campines relying on camera to build HD maps (IvI5, Atlatec, Carmera) |
5.6.13. | Companies building a map for specific firms: DeepMap |
5.6.14. | Enabling edge-level calculations |
5.6.15. | Semi- or fully automating the data-to-map process |
5.6.16. | Semi- or fully automating the data-to-map process |
5.7. | Teleoperation |
5.7.1. | Ottopia's advanced teleoperation for autonomous cars |
5.7.2. | Features of Ottopia's teleoperation platform |
5.7.3. | Business model of Ottopia |
5.7.4. | Phantom Auto's teleoperation as back-up for AVs |
5.7.5. | Phantom Auto gaining momentum in logistics |
5.8. | Cybersecurity |
5.8.1. | Cybersecurity risks for autonomous cars |
5.8.2. | Typical attack surfaces of a CAV |
5.8.3. | Vulnerable targets for hackers - connected ECUs |
5.8.4. | 5StarS - consortium for cybersecurity assurance |
5.8.5. | Arilou's in-vehicle cybersecurity solutions |
5.8.6. | Argus's multi-layered cybersecurity solutions (1) |
5.8.7. | Argus's multi-layered cybersecurity solutions (2) |
5.8.8. | TowerSec's intrusion detection and prevention solution |
5.8.9. | C2A Security's in-vehicle cybersecurity protection |
5.8.10. | Regulus's cyber defense for GNSS sensors |
5.9. | 5G and V2X |
5.9.1. | Why Vehicle-to-everything (V2X) is important for future autonomous vehicles |
5.9.2. | Two type of V2X technology: Wi-Fi vs cellular (1) |
5.9.3. | Regulatory: Wi-Fi based vs C-V2X |
5.9.4. | C-V2X assist the development of smart mobility |
5.9.5. | How C-V2X can support smart mobility |
5.9.6. | C-V2X includes two parts: via base station or direct communication |
5.9.7. | C-V2X via base station: vehicle to network (V2N) |
5.9.8. | 5G technology enable direct communication for C-V2X |
5.9.9. | Architecture of C-V2X technology |
5.9.10. | Use cases and applications of C-V2X overview |
5.9.11. | C-V2X for automated driving use case (1) |
5.9.12. | C-V2X for automated driving use case (2) |
5.9.13. | Use cases of 5G NR C-V2X for autonomous driving |
5.9.14. | Other use cases |
5.9.15. | Case study: 5G to provide comprehensive view for autonomous driving |
5.9.16. | Case study: 5G to support HD content and driver assistance system |
5.9.17. | Timeline for the deployment of C-V2X |
5.9.18. | Progress so far |
5.9.19. | Landscape of supply chain |
5.9.20. | 5G for autonomous vehicle: 5GAA |
5.9.21. | Ford C-V2X from 2022 |
6. | MARKET FORECAST |
6.1. | Travel demand and mobility as a service (MaaS) |
6.2. | Travel demand and MaaS - summary (in trillion miles) |
6.3. | Passenger car sales will peak earlier than expected |
6.4. | Global passenger car sales forecast 2020-2040 - moderate (unit numbers) |
6.5. | Global passenger car sales forecast 2020-2040 - aggressive (unit numbers) |
6.6. | Global passenger car sales forecast 2020-2040 (thousand units) - summary |
6.7. | Global autonomous passenger car market forecast 2020-2040 (unit numbers) |
6.8. | Global private autonomous car market forecast 2020-2040 by level of autonomy (unit numbers) |
6.9. | Private-owned autonomous cars: a 20-year view |
6.10. | Global shared autonomous car market forecast 2020-2040 (unit numbers) |
6.11. | Shared autonomous cars: a 20-year view |
6.12. | Global autonomous passenger car market forecast 2020-2040 (unit numbers) - summary |
6.13. | Global autonomous car market forecast 2020-2040: AV sales and mobility service (market value) |
6.14. | Global autonomous car sales revenue forecast 2020-2040 by level of autonomy |
6.15. | Global autonomous car market forecast 2020-2040 (market value: $ million) - summary |
6.16. | Global autonomous car sales revenue breakdown 2020-2040 (market value) |
6.17. | Global autonomous car sales revenue breakdown 2020-2040 (market value) - summary |