Skip to main content

Map handling framework for automated driving.

Project description

Lanelet2 is a C++-based framework for handling map data in the context of automated driving. It is designed to utilize high-definition map data in order to efficiently handle the challenges posed to a vehicle in complex traffic scenarios. Flexibility and extensibility are some of the core principles to handle the upcoming challenges of future maps.

Features:

  • 2D and 3D support
  • Consistent modification: if one point is modified, all owning objects see the change
  • Supports lane changes, routing through areas, etc.
  • Separated routing for pedestrians, vehicles, bikes, etc.
  • Many customization points to add new traffic rules, routing costs, parsers, etc.
  • Simple convenience functions for common tasks when handling maps
  • Accurate Projection between the lat/lon geographic world and local metric coordinates
  • IO Interface for reading and writing e.g. osm data formats (this does not mean it can deal with osm maps)
  • Python bindings for the whole C++ interface
  • Boost Geometry support for all thinkable kinds of geometry calculations on map primitives
  • Released under the BSD 3-Clause license

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

lanelet2-1.2.2-cp312-cp312-manylinux_2_31_x86_64.whl (4.3 MB view details)

Uploaded CPython 3.12 manylinux: glibc 2.31+ x86-64

lanelet2-1.2.2-cp311-cp311-manylinux_2_31_x86_64.whl (4.3 MB view details)

Uploaded CPython 3.11 manylinux: glibc 2.31+ x86-64

lanelet2-1.2.2-cp310-cp310-manylinux_2_31_x86_64.whl (4.3 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.31+ x86-64

lanelet2-1.2.2-cp39-cp39-manylinux_2_27_x86_64.whl (4.2 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.27+ x86-64

lanelet2-1.2.2-cp38-cp38-manylinux_2_27_x86_64.whl (4.2 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.27+ x86-64

File details

Details for the file lanelet2-1.2.2-cp312-cp312-manylinux_2_31_x86_64.whl.

File metadata

File hashes

Hashes for lanelet2-1.2.2-cp312-cp312-manylinux_2_31_x86_64.whl
Algorithm Hash digest
SHA256 c5a9c33e21f7b1d897f0c2f442c6c839edef0b11ace8422103551f3b2e88b527
MD5 f34b082fd3e9c5f1f2b5a9630f8bd66e
BLAKE2b-256 283179aa1fd8b50f5fb67a0e5537483005131ee347400797749eb50770d83010

See more details on using hashes here.

File details

Details for the file lanelet2-1.2.2-cp311-cp311-manylinux_2_31_x86_64.whl.

File metadata

File hashes

Hashes for lanelet2-1.2.2-cp311-cp311-manylinux_2_31_x86_64.whl
Algorithm Hash digest
SHA256 cc8e04519df162bfbe5bd6bc53a727b677afef6241455ed0c0d11190345acd06
MD5 a98beaf2931dd11f45fab4b9d2d2c7ec
BLAKE2b-256 13d091296f31ca60eea107077aba00bf417090eabf134cd7627271200ea54c4a

See more details on using hashes here.

File details

Details for the file lanelet2-1.2.2-cp310-cp310-manylinux_2_31_x86_64.whl.

File metadata

File hashes

Hashes for lanelet2-1.2.2-cp310-cp310-manylinux_2_31_x86_64.whl
Algorithm Hash digest
SHA256 adf6aef92288c8af33a9480b30a34102c1e168c53cb0e9f41d126caffeaf8b35
MD5 f5c221b00aaa7f5d0c575ed13d6a3182
BLAKE2b-256 5ad8398b0c5d9c934b7e0725371f2aea61cc7673a598db9e10a0ff5f1875dd48

See more details on using hashes here.

File details

Details for the file lanelet2-1.2.2-cp39-cp39-manylinux_2_27_x86_64.whl.

File metadata

File hashes

Hashes for lanelet2-1.2.2-cp39-cp39-manylinux_2_27_x86_64.whl
Algorithm Hash digest
SHA256 cfcf690ed4a18fff8bfeb25ad27f8b128d4c5f0559d7ecca72cc77942fc871a9
MD5 6cd8d9f74c0c617b1590b32295fb3208
BLAKE2b-256 0ac1eb8cbf885a47ee42b9262dccbcc01aaa2e2d8fd7f7d2d4007884666c4cea

See more details on using hashes here.

File details

Details for the file lanelet2-1.2.2-cp38-cp38-manylinux_2_27_x86_64.whl.

File metadata

File hashes

Hashes for lanelet2-1.2.2-cp38-cp38-manylinux_2_27_x86_64.whl
Algorithm Hash digest
SHA256 33d56e5a70119c4102ec2c49e89b73935aaf3c8bbfa36372d68e9544e6d34343
MD5 7efddbf7cc6c0d6d39189a38640f3194
BLAKE2b-256 2c8dc4e104f39892fdb10bb61d4e4b152662b77e568a77edd3651cd77cff4fef

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page