Robot Shuttles for Smart Cities 2021-2041: IDTechEx

Essential to smart cities, robot shuttles are a new form of transport at $20 billion hardware alone

Robot Shuttles for Smart Cities 2021-2041

Symmetrical, multipurpose, empowering, zero-emission, future transport, robotics, artificial intelligence, lidar, radar, camera, MaaS, 5G, 6G, battery, taxi, bus, compare robotaxis, Toyota, Honda, Amazon, GM, Baidu, Dubai


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Robot shuttles just got serious. The first large orders have been placed. More of the world's giant companies have entered the fray, some even likely to be both customers and manufacturers. On cue, analyst IDTechEx has launched a new report, "Robot Shuttles for Smart Cities 2021-2041" to explain the new realities and to present technology roadmaps and forecasts for 20 years ahead.
 
Robotaxis are a partially competing option. They are autonomous, battery-electric cars operated by the likes of Uber and taxi companies. Private cars are only used a few percent of the time and then only rarely full. When autonomous, they may be bought by people with less money because they can be lent to Uber as robotaxis when not in use. If you believe Elon Musk.
 
The new report maps and predicts how robot shuttles are very different, nothing short of a new form of transport being created from the ground up by about 40 organisations. These boxes on wheels are designed for smart cities and for more than road travel. Usually maximum speed is gated at 30-60 km/hr. Front-back-symmetrical means no U turns so they can quietly, cleanly go where cars are banned - down paths, into shopping malls, over plazas. Some even have crab-action sideways. Unlike a regular taxi or car, large sliding doors create fast entry/exit even of the disabled in wheelchairs. These doors do not swing open to kill cyclists nor do they prevent exit in confined spaces.
 
Uniquely, robot shuttles are made to be rapidly reconfigurable for multiple tasks even in one day with many formats, signage changes etc. Most have standing room and all-round large windows prevent claustrophobia compared to a robotaxi. The windows can contain the new microLEDs selling advertising. Standing room minimizes cost and road footprint per passenger.
 
See new infograms, tables and graphs comparing all options and new technology commitments from 2021 such as solar bodywork. Absorb other ideas and benchmarking from multi-lingual PhD level IDTechEx analysts across the world.
 
Latest news is that the projects are splitting into small and large sizes. 4-6 seaters are not very versatile and compete with robotaxis in locations penetrated. They have lowest cost but limited ability to get people with monster bags to the airport at speed. More mainstream is the larger ones typically 10-25 passengers including standing. These can additionally compete with school, micro and midi buses, even last mile package delivery and repurpose to mobile libraries, fast food, temporary event ticketing stands and more. However, it now emerges that some of this is achievable electrically but some is best done with new modular body replacement allowing repurposing through the day or the week. The report therefore covers all this new progress and platforms.
 
Questions answered by the report include:
  • Forecasts numbers and value 2021-2041?
  • Addressable market segments in detail and strategy options?
  • Potential smart city partners and services?
  • Results of trials from Japan to Europe and USA. Options of empowerment, inner city circulator etc.?
  • Technology roadmaps 2021-2041?
  • Detailed profiles on most players with IDTechEx SWOT reports matched to market?
  • Dead ends and doomed projects with reasons set against likely winners?
  • Leaders in modularisation, market trials, orders landed, speed of transitioning to second generation?
  • 360 degree wheels, new motors and batteries, autonomy technology detail, issues and economics, structural and transparent electronics, solar, smart windows. Who? When? Why?
 
The 595 page report "Robot Shuttles for Smart Cities 2021-2041" is unique in being so thorough and up-to-date and being written by globally acknowledged experts. It seeks to give an easily-absorbed commercial look at the technology, applications and business opportunities worldwide. That involves over 100 new infograms, graphs, comparison tables, photographs and diagrams. The report is so thorough, it even forecasts the associated business of autonomous buses and reveals the social benefits of everything planned. There is much news from 2021 assessed here and the report is regularly updated so you get the latest.
 
The Executive Summary and Conclusions explains the basics, gives over 20 key conclusions, detailed comparison tables of projects and all those forecasts, surfacing how they are evolving into key technology and applicational families now. It is sufficient in-itself for those with limited time. The Introduction then compares bus and robot shuttle types with detailed tables and images. See relative efficiencies, scope for vehicles that go where others are banned, vehicle populations worldwide, date and impact of peak car with reasons. Here are visions for robot shuttles and lessons from trials of very different business cases.
 
Chapter 4 is a long one because it covers 39 projects in 20 countries with trials, customers, technologies, uniques, shortcomings, dreams and the IDTechEx SWAT report in each case. Chapter 5 then concerns next technology other than autonomy for it is this that can sell more product by making shuttles more useful, lower cost, longer range. Indeed, vehicle-to-grid and window advertising with light-emitting video are among the ways of earning extra income streams that are described here. This is such a fertile area that many robot shuttle services such as school runs will offered free of charge.
 
The following chapters are a very detailed look at autonomy technology revealing cost and performance issues and best routes to safe, affordable, acceptable, insurable autonomous robot shuttles everywhere. The report then ends with detail on those new bright transparent micro LED displays that can make the many windows readable in the dark from inside and outside to loudly proclaim repurposing (UPS, School Bus etc.) and sell that advertising.
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Table of Contents
1.EXECUTIVE SUMMARY AND CONCLUSIONS
1.1.Purpose of this report
1.2.SAE levels of automation in land vehicles
1.3.Thirteen primary conclusions
1.3.1.The dream and the basics for getting there
1.3.2.Specification of a robot shuttle
1.3.3.Very different from a robotaxi
1.3.4.Smart shuttles will address megatrends in society
1.3.5.Robot shuttle business cases from bans and subsidies
1.3.6.Robot shuttle business cases from exceptional penetration of locations
1.3.7.Intensive use business cases are compelling
1.3.8.Campuses are not a quick win
1.3.9.The robot shuttle opportunity cannot be addressed by adapting existing vehicles
1.3.10.The leaders so far
1.3.11.Upfront cost and other impediments
1.3.12.Dramatic technical improvements are coming
1.3.13.Autonomous vehicle use of 5G and 6G communications
1.4.Two generations of robot shuttle
1.4.1.Envisaged applications compared
1.4.2.Second generation robot shuttle 2025-2040
1.5.Robot shuttles: the good things
1.5.1.Many benefits
1.5.2.Building on the multi-purposing of the past
1.5.3.Heart of the ideal robot shuttle
1.5.4.Archetypal large robot shuttles
1.5.5.Archetypal small robot shuttles
1.6.Robot shuttles: the bad things
1.7.Analysis of 39 robot shuttles and their dreams
1.8.Geographical, size, deployment distribution of 39 robot shuttles
1.8.1.Manufacture by country
1.8.2.Manufacture by major region
1.8.3.Designs by size
1.8.4.Number of robot shuttles deployed globally by manufacturer 2021
1.9.Timelines and forecasts
1.9.1.Technology and launch roadmap 2020-2041
1.9.2.Predicting when the robot shuttle has lower up-front price than a legal diesel midibus 2020-2041
1.9.3.Hype 2018-2041
1.9.4.Robot shuttles total market size in unit numbers thousand 2019-2041
1.9.5.Robot shuttles total market size in US$ million 2019-2041
2.INTRODUCTION
2.1.Bus and robot shuttle types compared
2.2.Bus population worldwide by types 2020
2.3.Pure electric buses for lowest TCO
2.4.Peak car coming: global passenger car sales forecast 2020-2040 - moderate scenario (unit numbers)
2.5.Background to robot shuttles
2.6.Tough for robot shuttles to compete
2.7.Second generation robot shuttles
2.8.Michigan Mobility Challenge: seniors, disabled, veterans
2.9.Texas trials: downtown circulator
2.10.Trials in Japan
2.11.Schaeffler: mechanically repurposed shuttle
2.12.Einride Sweden: not quite a robot shuttle
2.13.Rinspeed dreams embrace robot shuttles
3.ROBOT SHUTTLES IN ACTION - 39 TYPES IN 15 COUNTRIES
3.1.2getthere Netherlands
3.2.5GX shuttle SKT Korea
3.3.ANA collaboration Japan
3.4.Apollo Apolong: Baidu King Long China
3.5.Apple VWT6 USA
3.6.Astar Golden Dragon China
3.7.Aurrigo UK
3.8.AUVE Tech. Estonia
3.9.BlueSG/ Nanyang France Singapore
3.10.Capri AECOM UK
3.11.Coast Autonomous
3.12.Cruise Origin USA
3.13.DeLijn Belgium
3.14.e-BiGO Dubai
3.15.eGo Mover Germany
3.16.E-Palette Toyota
3.17.EZ10 EasyMile France
3.18.GACHA Sensible4 Finland
3.19.Heathrow pod ULTraFairwood UK
3.20.Hino Poncho SB Drive Japan
3.21.IAV HEAT Germany
3.22.iCristal Torc Robotics USA
3.23.KAMAZ shuttles Russia
3.24.KTI Hyundai Korea
3.25.LG Korea
3.26.Myla: May Mobility USA
3.27.Navya France
3.28.NEVS Sweden/ China
3.29.Ohmio Automation New Zealand
3.30.Olli: Local Motors USA
3.31.Optimus Ride USA
3.32.Ridecell Auro USA
3.33.Scania NXT - a second generation robot shuttle Sweden
3.34.Sedric Germany
3.35.ST Engineering Land Systems Singapore
3.36.Tony: Perrone Robotics USA
3.37.Volkswagen ID Buzz Germany
3.38.Yutong Xiaoyu China
3.39.Zoox USA
4.TOOLKIT FOR NEW EARNING STREAMS FROM NEW TECHNOLOGIES IN SECOND GENERATION
4.1.Overview
4.2.New earning stream from reprogrammable intelligent surfaces
4.3.Challenges being addressed
4.4.How eight key enabling technologies for robot shuttles are improving to serve 10 primary needs
4.5.How to reduce diesel shuttle parts by 90% with advanced electrics
4.6.Big change in relative importance of parts
4.7.Future electric vehicle powertrains - relevance to robot shuttles
4.8.Platform evolution
4.8.1.Overview
4.8.2.Toyota REE chassis: huge advances
4.9.Voltage trends
4.10.Typical pure electric bus technology
4.11.Electric motors
4.11.1.Overview
4.11.2.Synchronous or asynchronous
4.11.3.Operating principles for most EV uses
4.11.4.Electric motor choices for robot shuttles and their current EV uses
4.11.5.Electric motors for pure electric cars, vans: lessons for shuttle buses
4.11.6.Company experience and designer preferences
4.11.7.Motor material cost trends spell trouble
4.12.In-wheel motors
4.13.Sideways steerable wheels
4.14.360 degree wheels with in-wheel motor: Protean and Productiv
4.15.Energy storage for pure electric buses
4.15.1.Conventional buses see batteries shrink
4.15.2.Robot shuttles stay battery hungry
4.15.3.Even better batteries and supercapacitors a real prospect: future W/kg vs Wh/kg
4.15.4.Location and protection of batteries
4.15.5.Bus battery type, performance, future for 31 manufacturers
4.16.Charger standardisation: bus/truck commonality
4.17.Energy Independent Electric Vehicles EIEV
4.18.Stella Vie showing the way to an energy positive robot shuttle?
5.ENABLING TECHNOLOGIES: LIDARS, RADARS, CAMERAS, AI SOFTWARE AND COMPUTING PLATFORM, HD MAP, TELEOPERATION, CYBERSECURITY, 5G AND V2X
5.1.Chess pieces: autonomous driving tasks
5.2.Typical toolkit for autonomous cars
5.3.Anatomy of an autonomous car
5.4.Evolution of sensor suite from Level 1 to Level 5
5.5.What is sensor fusion?
5.6.Sensor fusion: past and future
6.LIDARS
6.1.3D Lidar: market segments & applications
6.2.3D Lidar: four important technology choices
6.3.Comparison of Lidar, Radar, Camera & Ultrasonic sensors
6.4.Automotive Lidar: SWOT analysis
6.5.Automotive Lidar: operating process & requirements
6.6.Emerging technology trends
6.7.Comparison of TOF & FMCW Lidar
6.8.Laser technology choices
6.9.Comparison of common Laser type & wavelength options
6.10.Beam steering technology choices
6.11.Comparison of common beam steering options
6.12.Photodetector technology choices
6.13.Comparison of common photodetectors & materials
6.14.106 Lidar players by geography
6.15.Lidar hardware supply chain for L3+ vehicles
6.16.Beam steering technology
6.17.Mechanical Lidar players, rotating & non-rotating
6.18.Micromechanical Lidar players, MEMS & other
6.19.Pure solid-state Lidar players, OPA & liquid crystal
6.20.Pure solid-state Lidar players, 3D flash
6.21.Players by technology & funding secured
6.22.Lidars per vehicle by technology & common configurations
6.23.Lidar configuration diagrams: L3, L4 & L5 vehicles
6.24.Average Lidar cost per vehicle by technology
6.25.L3 private vehicle market share by Lidar technology
6.26.L4 & L5 private vehicle market share by Lidar technology
6.27.L4 & L5 shared mobility market share by Lidar technology
6.28.Global Lidar unit sales by L3+ vehicle type
6.29.Global Lidar market size by L3+ vehicle type
6.30.Global Lidar unit sales by technology
6.31.Global Lidar market size by technology
7.RADARS
7.1.Towards ADAS and autonomous driving: increasing sensor content
7.2.Towards ADAS and autonomous driving: increasing radar use
7.3.SRR, MRR and LRR: different functions
7.4.The evolving role of the automotive radar towards full 360 degree imaging
7.5.Automotive radars: role of legislation in driving the market
7.6.Automotive radars: frequency trends
7.7.Radar: which parameters limit the achievable KPIs
7.8.Impact of frequency and bandwidth on angular resolution
7.9.Why are radars essential to ADAS and autonomy?
7.10.Towards autonomy: Increasing semiconductor use
7.11.Performance levels of existing automotive radars
7.12.Radar players and market share
7.13.Radar market forecasts 2020-2040 in all levels of autonomy/ADAS in vehicles and trucks (unit numbers)
7.14.Radar market forecasts 2020-2040 in all levels of autonomy/ADAS in vehicles and trucks (market value)
7.15.Radar market forecasts 2020-2040 in all levels of autonomy/ ADAS in vehicles and trucks (market value) - moderate
7.16.Radar market forecasts 2020-2040 in all levels of autonomy/ ADAS in vehicles and trucks (market value) - aggressive
7.17.Radar semiconductor market share forecast (GaAs, SiGe, Si)
7.18.Ten year (unit number) market forecasts for automotive radars
7.19.Benchmarking of semiconductor technologies for mmwave radars
7.20.The choice of the semiconductor technology
7.21.SiGe: current and emerging performance levels
7.22.SiGe: overview and comparison of manufacturers
7.23.SiGe BiCMOS: Infineon Technology
7.24.SiGe BiCMOS: NXP
7.25.SiGe BiCMOS: ST Microelectronics
7.26.A closer look at SiGe vs Si CMOS
7.27.Emerging all Si CMOS radar IC packages: NXP
7.28.Emerging all Si CMOS radar IC packages: ADI
7.29.Emerging all Si CMOS radar IC packages: TI
7.30.Many chip makers are on-board
7.31.Packaging trends: from discreet bare die (COB) to wafer-level packaging and beyond?
7.32.Packaging trends: AiP goes commercial?
7.33.Packaging trends: from discreet bare die (COB) to wafer-level packaging and beyond?
7.34.Comparison of die vs packaged options
7.35.eWLP vs flip chip and BGA in terms of insertion loss
7.36.Radar packaging: Material opportunities
7.37.Glass and panel level packaging of radars?
7.38.Function integration trend: from discreet to full chip-level function integration
7.39.Function integration trends: towards true radar-in-a-chip
7.40.Evolution of radar chips towards all-in-one designs
7.41.Evolution of radar chips: all-in-one designs
7.42.Board trends: from separate RF board to hybrid to full package integration?
7.43.Hybrid board is the norm
7.44.Hybrid board: what is it
8.CAMERAS
8.1.Camera technology: an overview of the market
8.2.Camera technology: CMOS is the bright spot in semiconductor sales landscape
8.3.How many camera needed in various levels of autonomy
8.4.CMOS image sensors vs CCD cameras
8.5.Key components in a CMOS image sensor (CIS)
8.6.Front vs backside illumination
8.7.Process flow for back-side-illuminated CMOS image sensors
8.8.Global vs Rolling Shutter
8.9.Global shutter: pixel size limitation and read-out mechanism
8.10.TPSCo: leading foundry for global shutter FSI CMOS on 65nm node
8.11.TPSCo: its best-in-class performance and partners
8.12.Sony: pixel architecture and performance of FSI global-shutter CMOS
8.13.Sony: back-side-illuminated stacked global shutter CMOS (breakthrough?)
8.14.Sony: BSI global shutter CMOS with stacked ADC
8.15.Omnivision: 2.2um GS CIS for automotive
8.16.Hybrid organic-Si global shutter CIS with high-res and low-noise
8.17.Hybrid QD-Si GS CIS at IR and NIR: achieving small pixels by physical separation of charge conversion and storage
8.18.Why one needs NIR sensing in machine vision
8.19.NIR sensing: limitation of Si CMOS
8.20.OmniVision: making silicon CMOS sensitive to NIR
8.21.Deep trench isolation: innovation to reduce cross-talk
8.22.What is SWIR or short-wave-infra-red?
8.23.Why SWIR in autonomous mobility
8.24.Other SWIR benefits: better animal or on-road hazard detection
8.25.SWIR sensitivity of different materials (PbS QDs, Si, polymers, InGaAs, HgCdTe, etc)
8.26.SWIR: incumbent and emerging technology options
8.27.The challenge of high resolution, low cost IR sensors
8.28.Silicon based SWIR sensors: innovation
8.29.Why colloidal quantum dots?
8.30.Quantum dots: choice of the material system
8.31.Advantage of solution processing: ease of integration with ROIC CMOS?
8.32.How is the QD layer applied?
8.33.Other ongoing challenges
8.34.Emberion: QD-graphene SWIR sensor
8.35.Emberion: QD-Graphene-Si broadrange SWIR sensor
8.36.SWIR Vision Sensors: first QD-Si cameras and/or an alternative to InVisage?
8.37.SWIR Vision Sensors: first QD-Si cameras and/or an alternative to InVisage?
8.38.QD-ROIC Si-CMOS integration examples (IMEC)
8.39.QD-ROIC Si-CMOS integration examples (ICFO)
9.AI SOFTWARE AND COMPUTING PLATFORM
9.1.Terminologies explained: AI, machine learning, artificial neural networks, deep neural networks
9.2.Artificial intelligence: waves of development
9.3.Classical method: feature descriptors
9.4.Typical image detection deep neutral network
9.5.Algorithm training process in a single layer
9.6.Towards deep learning by deepening the neutral network
9.7.The main varieties of deep learning approaches explained
9.8.There is no single AI solution to autonomous driving
9.9.Application of AI to autonomous driving
9.10.End-to-end deep learning vs classical approach
9.11.Imitation learning for trajectory prediction: Valeo (1)
9.12.Imitation learning for trajectory prediction: Valeo (2)
9.13.Hybrid AI for Level 4/5 automation
9.14.Hybrid AI for object tracking
9.15.Hybrid AI for sensor fusion
9.16.Hybrid AI for motion planning
9.17.Autonomous driving requires different validation system
9.18.Validation of deep learning system?
9.19.The vulnerable road user challenge in city traffic
9.20.Multi-layered security needed for vehicle system
9.21.Aurora: building the full-stack AD solution
9.22.Argo AI: fully integrated AD driving system for OEMs
9.23.Drive.ai: AD retrofitting kit
9.24.Momenta: the Chinese AD solution provider
9.25.Sensor fusion for Mpilot Highway and Parking
9.26.HoloMatic: the Xuanyuan platform
9.27.The coming flood of data in autonomous vehicles
9.28.Computing power needed for autonomous driving
9.29.Horizon Robotics: the Chinese embedded AI chip unicorn
9.30.The paradigm shift of AI computing
9.31.Horizon Robotics: software and hardware roadmap
9.32.By-wire and AV domain computer
9.33.Waymo open dataset
9.34.Pandaset by Hesai and Scale
9.35.Oxford radar Robotcar dataset
9.36.Astyx Dataset HiRes2019
9.37.Berkeley DeepDrive or BDD100K
9.38.Karlsruhe Institute of Technology and Toyota dataset
9.39.Cityscapes dataset presented in two 2015 and 2016 papers
9.40.Mapillary dataset presented in a 2017 paper
9.41.Apolloscape dataset by Baidu
9.42.Landmarks and Landmarks v2 by Google
9.43.Level 5 dataset by Lyft
9.44.nuScenes dataset by Aptiv
9.45.Datasets by University of Michigan and Stanford University
9.46.Sydney Urban Objects by the University of Sydney
10.HIGH-DEFINITION (HD) MAP
10.1.Lane models: uses and shortcomings
10.2.Localization: absolute vs relative
10.3.RTK systems: operation, performance and value chain
10.4.Sensors (GPS): price and market adoption (in unit numbers) evolution of GPS sensors
10.5.HD mapping assets: from ADAS map to full maps for level-5 autonomy
10.6.Many layers of an HD map for autonomous driving
10.7.HD map as a service
10.8.Who are the players?
10.9.Key business model differentiation between HD mapping players
10.10.Campines relying on vertical integration to build HD maps (TomTom. AutoNavi, Google, Here Technologies, etc.)
10.11.Campines relying on camera to build HD maps (IvI5, Atlatec, Carmera, Mapper)
10.12.Campines relying on camera to build HD maps (IvI5, Atlatec, Carmera)
10.13.Companies building a map for specific firms: DeepMap
10.14.Enabling edge-level calculations
10.15.Semi- or fully automating the data-to-map process
11.TELEOPERATION
11.1.Ottopia's advanced teleoperation for autonomous cars
11.2.Features of Ottopia's teleoperation platform
11.3.Business model of Ottopia
11.4.Phantom Auto's teleoperation as back-up for AVs
11.5.Phantom Auto gaining momentum in logistics
12.CYBERSECURITY
12.1.Cybersecurity risks for autonomous cars
12.2.Typical attack surfaces of a CAV
12.3.Vulnerable targets for hackers - connected ECUs
12.4.5StarS - consortium for cybersecurity assurance
12.5.Arilou's in-vehicle cybersecurity solutions
12.6.Argus's multi-layered cybersecurity solutions
12.7.TowerSec's intrusion detection and prevention solution
12.8.C2A Security's in-vehicle cybersecurity protection
12.9.Regulus's cyber defense for GNSS sensors
13.5G, 6G AND V2X FOR ROBOT SHUTTLES
13.1.Overview
13.2.Why Vehicle-to-everything (V2X) is important for future autonomous vehicles
13.3.Two type of V2X technology: Wi-Fi vs cellular
13.4.Regulatory: Wi-Fi based vs C-V2X
13.5.C-V2X assist the development of smart mobility
13.6.How C-V2X can support smart mobility
13.7.C-V2X includes two parts: via base station or direct communication
13.8.C-V2X via base station: vehicle to network (V2N)
13.9.5G technology enable direct communication for C-V2X
13.10.Architecture of C-V2X technology
13.11.Use cases and applications of C-V2X overview
13.12.C-V2X for automated driving use case
13.13.Use cases of 5G NR C-V2X for autonomous driving
13.14.Other use cases
13.15.Case study: 5G to provide comprehensive view for autonomous driving
13.16.Case study: 5G to support HD content and driver assistance system
13.17.Timeline for the deployment of C-V2X
13.18.Progress so far
13.19.Landscape of supply chain
13.20.5G for autonomous vehicle: 5GAA
13.21.Ford C-V2X from 2022
14.MICROLED DISPLAYS: INTERNAL/ EXTERNAL VIEWING IN ROBOT SHUTTLES
14.1.Existing large mini-/micro-led display announcements
14.2.Expectation of future displays
14.3.Characteristic comparison of different display technologies
14.4.Horizontal comparison
14.5.Core value propositions of µLED displays
14.6.Micro-LED display types
14.7.Micro-LED application roadmap
14.8.Emerging displays enabled by micro-LED technology
15.COST AND INCOME ANALYSIS
15.1.Robot shuttle cost overview 2018-2041
15.1.1.Cost and price overview
15.1.2.Cost per passenger km vs eVTOL taxis
15.1.3.The example of Japan
15.1.4.Example of Germany: Robot shuttles will help eliminate subsidies
 

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