![]() ![]() Instead of using geometric primitives (like lines, spirals and arcs), they use sequences of points to represent road elements. The roads are divided into lane sections, which contain the details of the borders of each lane with respect to the reference line.įinally, Baidu uses a modified version of the OpenDRIVE format for its Apollo self-driving software stack. An OpenDRIVE map consists of a list of roads, each with geometric primitives which describe the reference line of the road. Unlike Lanelet2 maps, OpenDRIVE maps don’t contain points, and instead use geometric primitives to describe their geometry. ![]() And the developers of the open source simulator, Carla, use RoadRunner to create maps. ![]() VectorZero’s RoadRunner, a software tool for designing road networks, exports to OpenDRIVE. It has been used in simulation for some time, but more recently, has been adopted by the autonomous driving industry. Lastly, the relationships between Lanelets is described, allowing routing along the map.Įxample map showing the internal structure of a Lanelet2 map Īnother common format currently in use is OpenDRIVE. Those points are grouped into Linestrings, which are then grouped to form Lanelets. As seen in the diagram below, a Lanelet2 map is comprised of a database of points. The lanelet format is proving useful in self-driving and is becoming more popular due to its simplicity, as well as powerful tools for handling them, such as the Lanelet2 framework. ![]() There are efforts to develop standards though, such as the Autoware Foundation’s Map Data and Formats working group, and ASAM’s project to develop a concept for OpenDRIVE 2.0. Widely used formatsĪlthough HD maps are not new, the recent rapid development of self-driving technology has led to the development of different formats to suit different needs. Our prediction module can make better predictions about the intentions of detected vehicles, pedestrians, and so on. Our perception module can segment sensor data based on the map to make better detections. For example, our motion planning module can plan relative to the lane center line further ahead than we could detect with sensor data, and around occlusions. Since we are developing an L4 system in an area that we can map in advance, we are able to take advantage of the benefits HD maps offer. Some self-driving systems are striving to work anywhere, and since they cannot map the whole world, they are faced with the challenges of inferring details about the road from sensor data in real time. This gives us the centimeter-level accuracy that many of our software modules demand. In contrast to geographical datasets such as OpenStreetMap’s, which often rely on satellite imagery, our HD maps are generated from high resolution LiDAR and camera data collected from the mapped area. It’s a database of all the lanes, the relationships between them, coordinates describing their boundaries and center-lines, as well as the locations of crosswalks, speed bumps, traffic signs, and traffic lights. Our integration between Carla and Apollo, including Carla towns converted to Apollo maps and the long-awaited Carla-Apollo bridge!Īs the name suggests, an HD map is a highly detailed description of a road network.Process of converting OpenDRIVE maps (a format used by many ADAS and AV companies, including Carla) to Apollo maps.HD maps (an essential component for most self-driving car stacks) and the current popular open source HD map formats.In part 1 of this series we introduced a bridge between the ubiquitous ROS framework, and the newer Apollo Cyber RT, which was created for the Apollo autonomous driving platform. In part 2, we are happy to open-source a bridge between the popular Carla simulation platform and Apollo. ![]()
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