In today’s world, while robots have the capability to do some things more efficiently than humans, humans are still much wiser when it comes to real-time decision-making capability. One such application that comes to light is driving and navigation. For example, decisions, such as stopping the vehicle at the right place, watching for a traffic signal at the intersection, or avoiding a split at the last minute, which humans take for granted, are still much harder for robots to make.
In the near future, when cars start driving themselves, they will have to ‘see’ what is around them to maneuver leaving no room for errors. To achieve this, vehicles will not only rely on sensors but will also require machine-readable maps of the world, containing accurate and precise road information. Autonomous vehicles will use sensors to make driving decisions on the fly, but vehicle sensors cannot observe everything all the time. Vehicle sensors can be blinded by corners, other vehicles, or bad weather conditions. Even though the sensors may notice an obstacle, they may not do so early enough to make decisions. In addition, lanes and signs may be missing on the road or knocked over or hidden by bushes, and therefore, can go undetected by sensors. Such accidents will be averted when sensor data will be combined with map data.
This research service provides an overview of the mapping market along with the differentiation between the types of maps and levels of autonomy that they will support. In addition to the overview, the study covers the components and attributes of ADAS and HD maps, which will help an autonomous vehicle to operate safely.
In conjunction with different processes of mapping, major HD map developers have been segmented on the basis of the mapping process they follow and compared on the basis of various assessment criteria, such as localization accuracy, the technology used for developing HD maps and needed by customers, current partnerships, and cost of acquiring the solution for a customer.
Key Issues Addressed
- How will ADAS and HD maps enable the safe operation of an autonomous vehicle?
- How are ADAS maps different than HD maps, and why will HD maps replace ADAS maps for L4 and L5 autonomy?
- What are the different processes that traditional map makers and start-ups follow to build HD maps?
- Which are the different solutions offered by map-making companies, and how do they fare against each other?
- Which are the OEMs and other customers that these HD map developers have partnered with, for development and testing?
- Levels of Autonomy and Maps
- HD Mapping Segments
- Comparative Analysis - End-to-end Base Maps and Updates
- Comparative Analysis - Crowdsourced Data Collection for Updates
- Comparative Analysis - In-house Maps With Full Stack AV Software
- Key Conclusions
- Research Scope
- Research Aims and Objectives
- Key Questions this Study will Answer
- Research Methodology
- SAE Definitions
- Parameters to Compare Profiles
- SD, ADAS, and HD Maps
- Autonomous Vehicles and ADAS and HD Maps
- Elements of HD Maps
- HD Mapping Process
- HD Mapping Tools
- Importance of an HD Base Map
- Segmentation of Mapping Companies
- End-to-end Mapping Companies
- Crowdsourced Mapping Companies
- In-house Mapping Companies
- HERE Overview
- TomTom Overview
- Civil Maps Overview
- CARMERA Overview
- Sanborn Map Company Overview
- Voxelmaps Overview
- Mobileye Overview
- DeepMap Overview
- Mapper.ai Overview
- Mapbox Overview
- Waymo Overview
- Oxbotica Overview
- Drive.ai Overview
- Growth Opportunity - Investments and Partnerships from OEMs/TSPs
- Strategic Imperatives for Success and Growth
- Key Conclusions
- The Last Word - 3 Big Predictions
- Legal Disclaimer
- Market Engineering Methodology
- Abbreviations and Acronyms Used
- List of Exhibits
A selection of companies mentioned in this report includes:
- Civil Maps
- Sanborn Map Company