Local planning for autonomous vehicles using multiple cameras
AssigneesConstructor Technology AG, Constructor Education and Research Genossenschaft
InventorsAleksandr Buyval, Ruslan Mustafin, Maksim Liubimov, Ilya Shimchik, Serg Bell, Stanislav Protasov, Nikolay Dobrovolskiy, Laurent Dedenis
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Abstract
Systems and methods for autonomous-vehicle navigation integrating path planning with a perception network. A Bird's Eye View costmap is generated at runtime using only onboard sensors. No external localization providers are used.
Claims20
A method for navigating a path by an autonomous vehicle in motion without using an external localization device, the method comprising:
- collecting image data along the path with an onboard camera operably coupled to the autonomous vehicle in motion;
- passing a slice of collected image data encoded with a first neural network feature extractor to generate encoded image data;
- passing the encoded image data to a Bird's Eye View (BEV) generation module;
- wherein the BEV generation module is a second neural network that cross-correlates input features with spatial positions around the autonomous vehicle;
- transforming the encoded image data into a BEV costmap using the BEV generation module;
- passing the outputted BEV costmap to a path-planning module operably coupled to the autonomous vehicle, wherein the path-planning module is configured to calculate a plurality of possible paths using a cost model;
- and
- selecting, with the path-planning module, the lowest cost path from among the possible calculated paths.
A system for navigating a path by an autonomous vehicle in motion without using an external localization device, the system comprising:
- an autonomous vehicle coupled with a plurality of onboard sensors for collecting image data;
- a microprocessor coupled with a nontransitory storage medium communicatively coupled with the plurality of onboard sensors;
- a first neural network comprising a feature extractor, under program control of the microprocessor, configured for encoding collected image data from the plurality of onboard sensors;
- a Bird's Eye View (BEV) generation module, under program control of the microprocessor, wherein the BEV generation module is a second neural network configured to cross-correlate input features with spatial positions around the autonomous vehicle and wherein the BEV module is configured to transform the encoded collected image data into a BEV costmap;
- a path-planning module, under program control of the microprocessor, configured to calculate a plurality of possible paths from the BEV costmap using a cost model, wherein the path-planning module is configured to select the lowest cost path from among the possible calculated paths.
A method for navigating a path by an autonomous vehicle in motion without using an external localization device, the method comprising:
- accessing image data collected on the path by an onboard sensor operably coupled to the autonomous vehicle;
- passing a slice of collected image data encoded with a first neural network feature extractor to generate encoded image data;
- passing the encoded image data to a Bird's Eye View (BEV) generation module;
- wherein the BEV generation module is a second neural network that cross-correlates input features with spatial positions around the autonomous vehicle;
- transforming the encoded image data into a BEV costmap using the BEV generation module;
- passing the outputted BEV costmap to a path-planning module operably coupled to the autonomous vehicle, wherein the path-planning module is configured to calculate a plurality of possible paths using a cost model;
- and
- selecting, with the path-planning module, the lowest cost path from among the possible calculated paths.
Dependencies are inferred from the claim text.