As driving gradually shifts from humans to machines, digital maps need to go beyond the usual turn-by-turn navigation. They need to be built specifically for self-driving vehicles, and thus arises, the need of developing detailed HD maps which can provide highly accurate, up-to-date and realistic representations of the road. Autonomous driving is classified into Level 0 - 5. According to the level of autonomy in the vehicles, the mapping and data management requirements change.
Mapping for ADAS
Levels 1 and 2 are considered ‘driving support’ levels, where human drivers are supported by Advanced Driver Assistance Systems (ADAS) for improving safety, comfort and performance. To make accurate predictions about the road ahead, ADAS leverages map data, covering details like lanes, gradient, speed limits and curvature. Safety warnings, such as lane departure warnings, provided by the ADAS Maps enable the drivers to achieve higher Euro NCAP ratings, along with providing them a safer and more comfortable journey.
Mapping for full autonomy driving
As the automation level in autonomous driving reaches Level 3 and beyond, autonomous vehicles move toward full autonomy. In the absence of a human driver, the precision of the map data needs to be higher. This is where ADAS maps reach their limit and HD Maps, which can provide accuracy to centimeter level with a high degree of attribution need to take over. HD Maps enable autonomous vehicles to become location, path and environment aware, providing minute details such as localization objects, lane models, lane-level speed restrictions, traffic signs, street furniture, etc.
Mapping for autonomous parking
Another area where autonomous vehicles and HD Maps are gearing up to bring transformation is ‘parking of vehicles’. The world is fast moving towards automated valet parking and, HD Maps and localization technology are enabling vehicles to accurately identify their position and park successfully on their own. The process of creating maps that can successfully facilitate automated parking requires deep understanding of the built environment. Autonomous vehicles, to be able to park safely on their own would require detailed maps of the road. At all times, they need to be aware where to go on any road, where the parking spaces are located, where the drop and pick-up points are located, which parking spots are currently available and how to navigate to the right place effortlessly.
For accurate navigation, the vehicles need to be equipped with precise maps not only of roads but also private spaces where pick-up and drop-off could happen. Futuristic maps for self-driving cars which are expected to park on their own must include details of important private venues such as schools, buildings etc.
Data analytics for future navigation
To win the autonomous vehicles race, automakers have to move from developing autonomy to developing intelligent autonomy. Automakers who will be able accelerate this process will gain a winning edge. This means they have to focus on research and development and enhancing technical ability to collect, store and analyze massive amounts of sensor data.
Self-driving vehicles use different types of sensors to gather data about their surroundings. Additionally, autonomous vehicle development teams around the world run tests and collect large amount of test drive data. This massive amount of data must be analyzed intelligently so as to enable autonomous vehicles to make decisions faster in diverse conditions.
HD Mapping and Data Management Services for AV
The task of making HD Maps is quite challenging as it requires capture and storage of large amount of data. Sensors such as cameras, LiDAR, GPS, IMU, and radars, placed on autonomous vehicles constantly record data for map creation and map update purposes. These sensor data are aggregated in a centralized location and a rendering of the map is built offline, and Magnasoft’s human annotators annotate semantic structures on the map and review the final results. The annotated and curated maps are thereby presented to the vehicles on the road for smooth navigation.
Magnasoft can help you analyze the huge amounts of sensory data collected by autonomous vehicles thereby helping you to build control systems that can perceive information and safely navigate the roads without human intervention. Incorporating machine learning and artificial intelligence into building vehicle autonomy requires evolving human expertise and this is where Magnasoft’s services stand out.
Human operators are vital for quality checking of HD maps. At Magnasoft we bring in the human expertise to completely harness the power of AI to achieve high scalability in the HD Maps creation and update process. Automakers are investing billions in AI systems that can aid them in cleaning up the sensor data collected by autonomous vehicles. However, the precision level achieved by these systems is about 70%-80%. For achieving 100% precision in the data, human expertise is indispensable.
Along with our mapping expertise, our extensive experience in 3D modeling of structures provides us an added advantage of understanding such environments intricately, thereby helping you to generate high precision HD maps, which can help the autonomous vehicles to seamlessly traverse through concrete structures and park themselves at the right position.