机器人穿梭车和自动驾驶巴士 2020-2040: IDTechEx

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机器人穿梭车和自动驾驶巴士 2020-2040


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IDTechEx 已对此发布了第一份名为《机器人穿梭车和自动驾驶巴士 2020-2040》的深度报告。机器人穿梭车是一种重要的、可重新配置的全新货物和人员运输方式,甚至可以用作移动办公室、餐厅等。报告的主题核心是直立的、四四方方的、可容纳 8-20 人的交通工具,它们是对称的,所以不掉头。此类车型环境影响小,视野广,门大,安静,零排放,可以进入室内,穿过广场和道路,可以在一天内完成许多不同的任务。它们主要用于密集型城市用途,其速度绝不会超过确定的速度,通常在 50-60 公里每小时的范围内。作为全新的交通方式,它得到了丰田、百度等大公司和初创公司的支持,有一家还为此项目筹集了 10 亿美元的资金。他们的试验探索了许多可能的应用,包括增强贫困者和残疾人的能力,有效填补交通网络的缺口,以及替换校车和私家车等利用率极低的交通工具,以减少拥堵和成本。
  • Robot shuttles and precursors: manufacturer profiles 36
  • Robot shuttle manufacturer countries 15
  • Countries with deployments analysed 20
  • New infograms and forecasts/ graphs 64
  • Allied markets forecasted such as all buses, autonomous buses 7
  • Pages 380
IDTechEx has issued the first in-depth report on this called "Robot Shuttles and Autonomous Buses 2020-2040". Robot shuttles are an important new, reconfigurable form of transportation for goods and people that may even function as mobile offices, restaurants and more. It finds that the heart of the subject is upright, boxy, 8-20 person vehicles that are symmetrical so they never do a U turn. Small footprint, all-round vision, large doors, quiet, zero emission, they can go indoors and over piazzas and roads and are able perform many different tasks even in one day. Primarily intended for intensive urban use, they are gated to never exceed a determined speed, typically in the range 50-60 kph. It all adds up to a new form of transport backed by both huge companies like Toyota and Baidu and startups, one having raised one billion dollars for the task. Their trials explore many possible applications, from empowering the poor and disabled, to viably filing in gaps in the transportation network and replacing very underutilised vehicles such as school buses and private cars, reducing congestion and cost.
City road congestion will be eliminated by banning little used and weakly filled vehicles such as private cars and some school buses and introducing robot shuttles intensively used because they reconfigure in use, even go indoors, move sideways and arrive at your door.
The Executive Summary presents the dreams, 10 primary conclusions, the 36 models and their 20 projects, the number deployed by the leaders, the big differences from robotaxis covered in a sister IDTechEx report. It picks winners on IDTechEx criteria and forecasts 20 years ahead with prices tumbling for identified reasons and a tipping point of sales when full Level 5 autonomy can be widespread. The overall bus and the autonomous large bus market is forecasted. See graphs of the robot shuttle hardware business of tens of billions of dollars emerging and additionally the market for associated services.
The Introduction talks through the needs, issues, originality and impediments. Chapter 3 critically appraises the 36 robot shuttle companies/models in 15 countries in detail with partners named, stated objectives and dreams of participants with a profusion of photographs and drawings and SOFT reports of each.
Chapter 4 explains the huge advances in vehicle technology beyond autonomy that are ahead for robot shuttles and key to major success, from solar, supercapacitor, color changing and self-healing bodywork to smart glass, 360 degree wheels and much more. Autonomy makes the vehicle work but these new structures provide a profusion of income streams and business cases, long life and near zero maintenance. The analysis benchmarks other industries where they are ahead with these vehicle technologies.
Chapter 5 is a deep dive into the autonomy technologies including their integration, cost and power reduction ahead plus challenges. That includes detail about lidar, radar, cameras, sensor fusion and more. Chapter 6 is a detailed analysis of cost and income streams, with the most detailed forecasts and background data for buses, autonomous buses, robot shuttles and earning streams 2020-2040. It all makes the IDTechEx report, "Robot Shuttles and Autonomous Buses 2020-2040" exceptionally thorough, insightful and up-to-date, using new information and interviews by PhD level globetrotting analysts from IDTechEx interviewing in local languages.
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Table of Contents
1.1.Purpose of this report
1.2.SAE levels of automation in land vehicles
1.3.Ten 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
1.4.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
1.6.Robot shuttles: the bad things
1.7.Analysis of 36 robot shuttles and their dreams
1.8.Geographical, size, deployment distribution of 36 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 deployed
1.9.Timelines and forecasts
1.9.1.Technology and launch roadmap 2020-2030
1.9.2.Predicting when the robot shuttle has lower up-front price than a legal diesel midibus 2020-2040
1.9.3.Hype 2018-2040
1.9.4.Robot shuttles total market size in unit numbers thousand
1.9.5.Robot shuttles total market size in US$ million
1.9.6.Bus and shuttle global market number projection by size 2020-2040
1.9.7.Bus and shuttle global market number projection by size by % 2020-2040: growth of shuttle and smaller buses
1.9.8.Market share Level 4 and Level 5 autonomy in buses projection by size 2020-2040
1.9.9.Global bus market by level of autonomy and projection by bus/ robot shuttle size 2018-2040
1.9.10.Bus and robot shuttle total market projection by level of autonomy 2020-2030
1.9.11.Cost projection of pure electric bus and shuttle (minus autonomy) 2020-2040
1.9.12.Cost of autonomy 2020-2040
1.9.13.Total 20-year market forecast for all bus/shuttle sizes and levels of autonomy
1.9.14.Total 20-year market forecast (purpose-built shuttles and small-sized buses)
1.9.15.Total 20-year market forecast (medium and large sized buses)
1.9.16.Accumulated fleet size projected number 2020-2040
1.9.17.Service revenue forecast $ billion 2020-2040
1.9.18.Total revenue forecast $ billion 2020-2030
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
3.1.2getthere 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.DeLijn Belgium
3.13.e-BiGO Dubai
3.14.eGo Mover Germany
3.15.E-Palette Toyota
3.16.EZ10 EasyMile France
3.17.GACHA Sensible4 Finland
3.18.Heathrow pod ULTraFairwood UK
3.19.Hino Poncho SB Drive Japan
3.20.IAV HEAT Germany
3.21.iCristal Torc Robotics USA
3.22.KAMAZ shuttles Russia
3.23.KTI Hyundai Korea
3.24.LG Korea
3.25.Myla: May Mobility USA
3.26.Navya France
3.27.NEVS Sweden
3.28.Ohmio Automation New Zealand
3.29.Olli: Local Motors USA
3.30.Optimus Ride USA
3.31.Ridecell Auro USA
3.32.Scania NXT - a second generation robot shuttle Sweden
3.33.Sedric Germany
3.34.ST Engineering Land Systems Singapore
3.35.Tony: Perrone Robotics USA
3.36.Volkswagen ID Buzz Germany
3.37.Yutong Xiaoyu China
3.38.Zoox USA
4.2.Challenges being addressed
4.3.How eight key enabling technologies for robot shuttles are improving to serve 10 primary needs
4.4.How to reduce diesel shuttle parts by 90% with advanced electrics
4.5.Big change in relative importance of parts
4.6.Future electric vehicle powertrains - relevance to robot shuttles
4.7.Platform evolution
4.7.2.Toyota REE chassis: huge advances
4.8.Voltage trends
4.9.Typical pure electric bus technology
4.10.Electric motors
4.10.2.Synchronous or asynchronous
4.10.3.Operating principles for most EV uses
4.10.4.Electric motor choices for robot shuttles and their current EV uses
4.10.5.Electric motors for pure electric cars, vans: lessons for shuttle buses
4.10.6.Company experience and designer preferences
4.10.7.Motor material cost trends spell trouble
4.11.In-wheel motors
4.12.Sideways steerable wheels
4.13.360 degree wheels with in-wheel motor: Protean and Productiv
4.14.Energy storage for pure electric buses
4.14.1.Conventional buses see batteries shrink
4.14.2.Robot shuttles stay battery hungry
4.14.3.Even better batteries and supercapacitors a real prospect: future W/kg vs Wh/kg
4.14.4.Location and protection of batteries
4.14.5.Bus battery type, performance, future for 31 manufacturers
4.14.6.Best of both worlds?
4.15.Charger standardisation: bus/truck commonality
4.16.Energy Independent Electric Vehicles EIEV
4.17.Stella Vie showing the way to an energy positive robot shuttle?
5.1.1.The automation levels in detail
5.1.2.Functions of autonomous driving at different levels
5.1.3.Future mobility scenarios: autonomous and shared
5.1.4.Chess pieces: autonomous driving tasks
5.1.5.Typical toolkit for autonomous cars
5.1.6.Perception technologies and AI
5.1.7.Anatomy of an autonomous vehicle
5.1.8.Evolution of sensor suite from Level 1 to Level 5
5.1.9.What is sensor fusion?
5.1.10.Sensor fusion: past and future
5.2.Lidars Lidar: market segments & applications 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.Emerging technology trends
5.2.6.Comparison of TOF & FMCW Lidar
5.2.7.Laser technology choices
5.2.8.Comparison of common laser type & wavelength options
5.2.9.Beam steering technology choices
5.2.10.Comparison of common beam steering options
5.2.11.Photodetector technology choices
5.2.12.Comparison of common photodetectors & materials
5.2.13.Mechanical Lidar players, rotating & non-rotating
5.2.14.Micromechanical Lidar players, MEMS & other
5.2.15.Pure solid-state Lidar players, OPA & liquid crystal
5.2.16.Pure solid-state Lidar players, 3D flash
5.2.17.Players by technology & funding secured
5.2.18.Average Lidar cost per vehicle by technology
5.3.1.Why are radars essential to ADAS and autonomy?
5.3.2.Towards ADAS and autonomous driving: increasing radar use
5.3.3.SRR, MRR and LRR: different functions
5.3.4.Radar: which parameters limit the achievable KPIs
5.3.5.Towards the radar of the future
5.3.6.Evolution of semiconductor technology in automotive radar
5.3.7.Benchmarking of semiconductor technologies for mmwave radars
5.3.8.Many chip makers are on-board
5.3.9.Function integration trends: towards true radar-in-a-chip
5.3.10.Evolution of radar chips towards all-in-one designs
5.3.11.Board trends: from separate RF board to hybrid to full package integration?
5.3.12.The evolving role of the automotive radar towards full 360degree imaging
5.3.13.AI trend: moving beyond just presence detection
5.3.14.Other trends: increasing range, angular and elevation resolution
5.3.15.Radar data: challenges of spare point cloud
5.3.16.Data fusion challenge: mismatch in point cloud densities
5.3.17.Training neutral networks on radar data: the labelling challenge
5.3.18.Automatic data labelling: early fusion of camera, lidar and radar data
5.4.AI software and computing platform
5.4.1.Terminologies explained: AI, machine learning, artificial neural networks, deep neural networks
5.4.2.Artificial intelligence: waves of development
5.4.3.Classical method: feature descriptors
5.4.4.Typical image detection deep neutral network
5.4.5.Algorithm training process in a single layer
5.4.6.Towards deep learning by deepening the neutral network
5.4.7.The main varieties of deep learning approaches explained
5.4.8.There is no single AI solution to autonomous driving
5.4.9.Application of AI to autonomous driving
5.4.10.End-to-end deep learning vs classical approach
5.4.11.Imitation learning for trajectory prediction: Valeo (1)
5.4.12.Imitation learning for trajectory prediction: Valeo (2)
5.4.13.Hybrid AI for Level 4/5 automation
5.4.14.Hybrid AI for sensor fusion
5.4.15.Hybrid AI for motion planning
5.4.16.Autonomous driving requires different validation system
5.4.17.Validation of deep learning system?
5.4.18.The vulnerable road user challenge in city traffic
5.4.19.Multi-layered security needed for vehicle system
5.5.High-definition (HD) map
5.5.1.Lane models: uses and shortcomings
5.5.2.Localization: absolute vs relative
5.5.3.HD mapping assets: from ADAS map to full maps for level-5 autonomy
5.5.4.Many layers of an HD map for autonomous driving
5.5.5.HD map as a service
5.5.6.Who are the players?
5.5.7.Why Vehicle-to-everything (V2X) is important for future autonomous vehicles
5.5.8.Use cases of 5G NR C-V2X for autonomous driving
6.1.Robot shuttle cost overview 2018-2040
6.1.1.Cost and price overview
6.1.2.Cost per passenger km vs eVTOL taxis
6.1.3.The example of Japan
6.1.4.Example of Germany: Robot shuttles will help eliminate subsidies

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