The market for autonomous vehicles is evolving rapidly, with numerous companies developing cutting-edge technology to enable self-driving technology to navigate roads safely and efficiently. Unlike human drivers, autonomous cars need to learn how to drive, read maps, and arrive at their destination using real-time data and through interaction with the surroundings.
For building a safe and secure autonomous mobility technology, you will need high-quality HD maps for precise navigation that are updated regularly. High-Definition (HD) Maps are essential for developing autonomous vehicles as they provide detailed and precise information about the surrounding environment.
This blog delves into HD Mapping and Localization concepts and explains some of our annotation use cases.
HD Maps for Autonomous Driving
HD maps are an essential component of autonomous driving technology. These maps are highly accurate, containing detailed information not typically found on traditional maps, and can be precise at the centimeter level. Captured using sensors, such as LiDARs, radars, digital cameras, and GPS, HD maps provide autonomous vehicles with critical information about the roadway signs, intersections, and other features in and around the roadway. These are some of the characteristics of HD Maps:
- Extremely high accuracy and precision at a centimeter-level for accurate navigation.
- Created using an AI-assisted processing pipeline that detects and classifies objects in the environment.
- Stored in industry-standard formats such as OpenDRIVE or ADASIS for easy integration into autonomous driving systems.
- Require regular updates with a high level of detail, including lane markings, traffic signs, and other features.
- Provide real-time mapping data, allowing autonomous vehicles to adapt to changing road conditions.
An HD Map has many layers – a base map (standard definition map), a geometric map, a semantic map, map priors, and real-time knowledge (Issue Triaging).
Basemap: Contains geographic information and provides context for additional layers on top of the base map with features including physical infrastructures such as buildings, roads, highways, bridges, tunnels, water bodies, parks, forests, etc.
Geometric Map: Comprises raw sensor data collected from LiDAR, various cameras, GPS, and IMUs, with the output as a dense 3D point cloud
Semantic Map: Built upon the geometric map layer by adding semantic objects (2D or 3D) with features including road boundaries, lane markings, crosswalks, traffic lights and stop signs, speed limits and speed zones, etc.
Map Priors: Contains dynamic information and human behavior data, including traffic lights change, the average wait times on a typical day at the lights, etc.
Real-Time Map: The topmost layer in the map, which is dynamically updated and contains real-time information, including traffic conditions, such as accidents, road closures, congestion, roadworks, weather updates, etc.QC Audit Services for Autonomous Vehicles by iMerit
Localization in HD Maps
Street mapping and localization are critical capabilities for autonomous vehicles, allowing them to pinpoint their exact location within a map with centimeter-level accuracy. This high level of precision is essential for self-driving technology to understand their environment and surroundings for establishing a sense of the road and lane structures around them.
The localization module provides crucial information to the car about its position in 3D space and the surrounding environment. For example, the system can inform the vehicle that it is 150 centimeters from the stop line and that a crosswalk is a certain width ahead. This information is crucial for enabling safe and efficient navigation by the autonomous vehicle.
HD maps enable self-driving vehicles to precisely determine their position through localization by eliminating noise and merging data from different sensors. HD mapping and street localization work very tightly together, constantly comparing cars on the go. It should be able to tell which object was supposed to be at which place and what the difference is.
iMerit’s HD Mapping and Localization Services
Top autonomous vehicle companies work with iMerit for the experts-in-the-loop model and scalability that our teams offer. Our custom workflows and rigorous quality assurance process ensure quality, scale, and flexibility. From static data sets to dynamic data services, our HD mapping solution feeds autonomous vehicle localization and perception systems. We annotate map features from signs, curves, and lane markings and derive attributes such as type, material, classification, and dimensions.
Data Services for HD Mapping
Semantic Mapping
Semantic mapping involves creating maps that contain not only geometric data but also semantic information about the environment. It includes labeling and annotating environmental features with metadata to provide context to the autonomous vehicles about what they are observing. On top of the geometric map, HD maps contain rich semantic information such as road boundaries, lane markings, crosswalks, traffic lights, speed zones, signage, etc. iMerit’s data labeling experts in the autonomous vehicle domain annotate and apply ground truth semantic labels for such features on dense point cloud maps.
Issue Resolution
Maps are constantly changing. It can be a new road sign or a temporarily blocked road due to repair or construction work. iMerit’s expert data annotation team regularly updates base maps, semantic maps, and live maps to resolve issues like a new crosswalk or road sign.
For edge cases (unseen situations), we work with the client on the best way to handle them and similar ones going forward. With our product, iMerit Edge Case, clients can gain visibility into edge case resolution, view edge case insights and analytics, and access a repository of edge cases for future projects.
Road Rules
States may have different rules regarding speed limits, U-turns, and other traffic laws. We help our AV clients update and maintain an ever-evolving database of road rules based on various state traffic laws. Autonomous vehicles can accurately identify and follow local laws and regulations by annotating and labeling these rules on the HD map.
Road Features and Conditions
Road features and conditions annotation involves annotating and labeling semantic features like traffic lights, road signs, and various road conditions such as construction sites, potholes, and speed bumps.
Route Creation
Route creation is a critical aspect of HD mapping that involves training autonomous vehicles to analyze multiple routes and select the most efficient path from point A to point B. By annotating the maps with information about road conditions, traffic flow, and other relevant factors, iMerit’s expert annotators create the most optimum route training for the autonomous vehicles.
Conclusion
As the data training process is highly iterative and constantly evolving, we at iMerit are committed to remaining as agile, versatile, and flexible as our clients need us to be. Our dedicated full-time data experts provide annotation support across 2D lane marking, 3D localization, multi-sensor fusion, face tracking for in-cockpit behavior, and others for autonomous vehicle technology.
