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In order for autonomous vehicles to navigate in complex and unpredictable environments, it is crucial to address three important key components: Precise High-Definition Mapping, Accurate Real-Time Localisation and the Infrastructure in order to support this mapping challenge. In this report, Strategy Analytics examines the different approaches that companies are taking in order to enable HD Mapping into the Autonomous Driving ecosystem and the challenges that they are facing.

1. Executive Summary
2. Data Collection Procedures/ Creating the Map
   • 2.1.Geospatial Technology in Autonomous Vehicles
   • 2.2.Connected and Autonomous Vehicles as Data Collectors
   • 2.3.Abbreviations Used in This Report
3. Advanced Multiple Sensors
   • 3.1.Remote Sensing
     • 3.1.1. Photogrammetry
     • 3.1.2. LiDAR and RADAR
   • 3.2.arbe Robotics
   • 3.3.Benewake
     • 3.3.1. Benewake SLAM Techniques
   • 3.4.Blickfeld
     • 3.4.1. Blickfeld’s LiDAR System
   • 3.5.Cyclomedia
     • 3.5.1. System Overview
     • 3.5.2. Cyclomedia and AND (Autonomous Driving Ecosystem)
   • 3.6.DeepMap
     • 3.6.1. DeepMap Partnerships and Investments
   • 3.7.Dibotics
     • 3.7.1. Data Acquisition
     • 3.7.2. Augmented LiDAR
     • 3.7.3. Dibotics Partnerships
   • 3.8.HERE
     • 3.8.1. HERE’s HD Live Map
     • 3.8.2. Partnerships and Collaborations
   • 3.9.Hesai Photonics Technology
   • 3.10.KAARTA
   • 3.11.METAWAVE
     • 3.11.1.Technical Specification
   • 3.12.NVIDIA
     • 3.12.1.NVIDIA MapWorks
   • 3.13.Robosense
     • 3.13.1.The RS-LiDAR-Algorithms
   • 3.14.Sanborn
     • 3.14.1.Sanborn HD Maps for Autonomous Driving
   • 3.15.Steradian Semiconductor
   • 3.16.TomTom Automotive
     • 3.16.1.TomTom HD Maps
     • 3.16.2.TomTom RoadDNA
     • 3.16.3.Map Making Before AVs
     • 3.16.4.Partnerships, Collaborations and Funding
   • 3.17.Quanergy
     • 3.17.1.Quanergy’s Qortex Software Platform
   • 3.18 Ushr
     • 3.18.1 Ushr’s Funding, Partnerships and Future Goals
   • 3.19.Velodyne LiDAR
     • 3.19.1.Velodyne’s VeloView from ParaView
   • 3.20.XenomatiX
     • 3.20.1.XenoWare
4. Building Maps based on Vehicular Crowdsourced Data
   • 4.1.Vehicle as a Sensor
   • 4.2.CARMERA
     • 4.2.1. CARMERA Autonomous Map
     • 4.2.2. CARMERA partnership with Voyage
   • 4.3.Civil Maps
   • 4.4.Comma AI
   • 4.5.lvl5
   • 4.6.Mapbox
     • 4.6.1. Mapbox Automotive
     • 4.6.2. Mapbox Partnerships and Future Goals
   • 4.7.Mapillary
     • 4.7.1. Partners and Integrations with Mapillary Technologies
   • 4.8.Mapper
   • 4.9.Mobileye
     • 4.9.1. Aftermarket Collision Avoidance System for Crowdsourcing Data
     • 4.9.2. New Partnerships for Building HD Maps
5. High Powered Cameras
   • 5.1.Companies who are Using Cameras for Mapping and Navigation
   • 5.2.Artisense
   • 5.3.Continental | Ygomi
   • 5.4.Machines with Vision
   • 5.5.Foresight
     • 5.5.1. QuadSight Windshield Camera
     • 5.5.2. Eyes-On Windshield Camera
6. GNSS and GPS Navigation Sensors
   • 6.1.Companies that are using GPS Sensors for Mapping
   • 6.2.Mitsubishi Electric
   • 6.3.Naventik
     • 6.3.1. Naventik’s PATHFINDER Navigation Engine
   • 6.4.NovAtel
     • 6.4.1. Novatel GNSS Solutions
     • 6.4.2. Multi-Frequency, Multi-Constellation
     • 6.4.3. GNSS Corrections
     • 6.4.4. RTK (Real Time Kinematic)
     • 6.4.5. PPP (Precise Point Positioning)
     • 6.4.6. Sensor Integration
   • 6.5.Point One Navigation
     • 6.5.1. Precise Location as a Service (PLaaS)
   • 6.6.Swift Navigation
     • 6.6.1. Piksi Multi GNSS Module
     • 6.6.2. Duro Ruggedized Receiver
   • 6.7.Qualcomm
     • 6.7.1. Qualcomm’s Snapdragon 820A Automotive Platform
     • 6.7.2. Partnerships with Qualcomm’s Automotive Solutions
   • 6.8.Trimble
     • 6.8.1. Trimble’s Automotive GNSS Solutions
     • 6.8.2. MB100
     • 6.8.3. BD910
     • 6.8.4. BD920
     • 6.8.5. BD982
     • 6.8.6. MB-ONE
     • 6.8.7. Applanix – A Trimble Company
   • 6.9.U-blox
     • 6.9.1. Sapcorda
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