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HD MAP Principles:
\n1.Mapping as pre-computation : Perception and localization of static objects in the world such as roads, intersections, street signs, etc. can be solved offline and in a highly accurate manner .This are some of the things that could be precomputed before the AV starts driving.
2.Mapping to improve safety: Level 5 HD maps are designed not only to contain speed limit information for each lane segment, but also speed profiles derived from actual human drivers on the Lyft network that meet our high bar for safety.
\n3.Map as a unique sensor: Viewing the map as yet another sensor allows us to design efficient map access patterns and integrate map data more naturally into the autonomy stack (e.g. sensor-fusion components).
\n4.Map as global shared state: The map then becomes a shared data structure that lives both in the cloud and also docked in each of the AVs. AVs use the map to both read and write to this social memory.
Four noteworthy HD layers are:
\nThe geometric map : The geometric map layer contains 3D information of the world. This information is organized in very high detail to support precise calculations. The ground map is key for aligning the subsequent layers of the map, such as the semantic map.
The semantic map : Semantic objects include various traffic 2D and 3D objects such as lane boundaries, intersections, crosswalks, parking spots, stop signs, traffic lights, etc. that are used for driving safely. These objects contain rich metadata associated with them such as speed limits and turn restrictions for lanes.
\nMap priors :The map priors layer contains derived information about dynamic elements and also human driving behavior. Information here can pertain to both semantic and geometric parts of the map
\nReal-time knowledge:The real-time layer is the top most layer in the map and is designed to be read/write capable. This is the only layer in the map designed to be updated while the map is in use by the AV serving a ride
\n", "messages": [ { "id": 986249, "postDate": "2020-08-26T10:10:09.350Z", "content": "HD MAP Principles:
\n1.Mapping as pre-computation : Perception and localization of static objects in the world such as roads, intersections, street signs, etc. can be solved offline and in a highly accurate manner .This are some of the things that could be precomputed before the AV starts driving.
2.Mapping to improve safety: Level 5 HD maps are designed not only to contain speed limit information for each lane segment, but also speed profiles derived from actual human drivers on the Lyft network that meet our high bar for safety.
\n3.Map as a unique sensor: Viewing the map as yet another sensor allows us to design efficient map access patterns and integrate map data more naturally into the autonomy stack (e.g. sensor-fusion components).
\n4.Map as global shared state: The map then becomes a shared data structure that lives both in the cloud and also docked in each of the AVs. AVs use the map to both read and write to this social memory.
Four noteworthy HD layers are:
\nThe geometric map : The geometric map layer contains 3D information of the world. This information is organized in very high detail to support precise calculations. The ground map is key for aligning the subsequent layers of the map, such as the semantic map.
The semantic map : Semantic objects include various traffic 2D and 3D objects such as lane boundaries, intersections, crosswalks, parking spots, stop signs, traffic lights, etc. that are used for driving safely. These objects contain rich metadata associated with them such as speed limits and turn restrictions for lanes.
\nMap priors :The map priors layer contains derived information about dynamic elements and also human driving behavior. Information here can pertain to both semantic and geometric parts of the map
\nReal-time knowledge:The real-time layer is the top most layer in the map and is designed to be read/write capable. This is the only layer in the map designed to be updated while the map is in use by the AV serving a ride
\n", "rawMarkdown": "HD MAP Principles:\n1.Mapping as pre-computation : Perception and localization of static objects in the world such as roads, intersections, street signs, etc. can be solved offline and in a highly accurate manner .This are some of the things that could be precomputed before the AV starts driving.\n\n2.Mapping to improve safety: Level 5 HD maps are designed not only to contain speed limit information for each lane segment, but also speed profiles derived from actual human drivers on the Lyft network that meet our high bar for safety.\n\n3.Map as a unique sensor: Viewing the map as yet another sensor allows us to design efficient map access patterns and integrate map data more naturally into the autonomy stack (e.g. sensor-fusion components).\n\n4.Map as global shared state: The map then becomes a shared data structure that lives both in the cloud and also docked in each of the AVs. AVs use the map to both read and write to this social memory.\n\nFour noteworthy HD layers are:\nThe geometric map : The geometric map layer contains 3D information of the world. This information is organized in very high detail to support precise calculations. The ground map is key for aligning the subsequent layers of the map, such as the semantic map.\n\nThe semantic map : Semantic objects include various traffic 2D and 3D objects such as lane boundaries, intersections, crosswalks, parking spots, stop signs, traffic lights, etc. that are used for driving safely. These objects contain rich metadata associated with them such as speed limits and turn restrictions for lanes.\n\nMap priors :The map priors layer contains derived information about dynamic elements and also human driving behavior. Information here can pertain to both semantic and geometric parts of the map\n\nReal-time knowledge:The real-time layer is the top most layer in the map and is designed to be read/write capable. This is the only layer in the map designed to be updated while the map is in use by the AV serving a ride\n\nSource : https://medium.com/lyftlevel5/https-medium-com-lyftlevel5-rethinking-maps-for-self-driving-a147c24758d6\n", "votes": 3 } ], "comments": [], "raw_markdown_by_id": { "986249": "HD MAP Principles:\n1.Mapping as pre-computation : Perception and localization of static objects in the world such as roads, intersections, street signs, etc. can be solved offline and in a highly accurate manner .This are some of the things that could be precomputed before the AV starts driving.\n\n2.Mapping to improve safety: Level 5 HD maps are designed not only to contain speed limit information for each lane segment, but also speed profiles derived from actual human drivers on the Lyft network that meet our high bar for safety.\n\n3.Map as a unique sensor: Viewing the map as yet another sensor allows us to design efficient map access patterns and integrate map data more naturally into the autonomy stack (e.g. sensor-fusion components).\n\n4.Map as global shared state: The map then becomes a shared data structure that lives both in the cloud and also docked in each of the AVs. AVs use the map to both read and write to this social memory.\n\nFour noteworthy HD layers are:\nThe geometric map : The geometric map layer contains 3D information of the world. This information is organized in very high detail to support precise calculations. The ground map is key for aligning the subsequent layers of the map, such as the semantic map.\n\nThe semantic map : Semantic objects include various traffic 2D and 3D objects such as lane boundaries, intersections, crosswalks, parking spots, stop signs, traffic lights, etc. that are used for driving safely. These objects contain rich metadata associated with them such as speed limits and turn restrictions for lanes.\n\nMap priors :The map priors layer contains derived information about dynamic elements and also human driving behavior. Information here can pertain to both semantic and geometric parts of the map\n\nReal-time knowledge:The real-time layer is the top most layer in the map and is designed to be read/write capable. This is the only layer in the map designed to be updated while the map is in use by the AV serving a ride\n\nSource : https://medium.com/lyftlevel5/https-medium-com-lyftlevel5-rethinking-maps-for-self-driving-a147c24758d6\n" } }