Here Global B.v.

Deekshant Saxena

Senjuti Sen

Leon Stenneth

Ram Marappan

Danny Savla

Pranil Pandit

System and methods enable vehicles to make ethical/empathetic driving decisions by using deep learning aided location intelligence. The systems and methods identify moral islands/complex driving scenarios where a complex ethical decision is required. A Generative Adversarial Network (GAN) is used to generate synthetic training data to capture varied ethically complex driving situations. Embodiments train a deep learning model (ETHNET) that is configured to output one or more driving decisions to be taken when a vehicle comes across an ethically complex driving situations in the real world.

Location intelligence for building empathetic driving behavior to enable L5 cars

Embodiments including a method and apparatus for correction of a global navigation satellite system (GNSS) are described. In one example, the apparatus includes a communication interface and a processor. The communication interface is configured to a plurality of GNSS signals. The GNSS signals may include at least one almanac value and at least one ephemeris value. The processor is configured to generate a spatio-temporal graph model based on the at least one almanac value, the at least one ephemeris value, and a predetermined offset value for a base location. The spatio-temporal graph model analyzes subsequent GNSS signals to determined a predicted offset or a corrected GNSS position.

GNSS error resolution

System and methods for creating multi-return map data using single return Lidar data. The systems and methods use a long short-term memory (LSTM) model in combination with a Generative Adversarial Network (GAN) model. The systems and method use a single (1st) return of Lidar at a time stamp and create multiple unseen samples of 2nd and 3rd returns. The LSTM model is used to create a sequential calibration based on incidence angle to choose the optimized 2nd and 3rd return at the same instance of the time stamp. This creates a localized model of three returns from a single return of Lidar and thus provides additional data to generate an HD map.

Creating multi-return map data from single return lidar data

Embodiments provide systems and methods for three-dimensional building generation from machine learning and topological models. The method uses topology models that are converted into vertices and edges. A BGAN (Building generative adversarial network) is used to create fake vertices/edges. The BGAN is then used to generate random samples from seen sample of different structures of building based on relationship of vertices and edges. The embeddings are then fed into a machine trained network to create a digital structure from the image.

3D building generation using topology

A system, a method, and a computer program product for generating updated map data are provided. The method comprises obtaining first sensor data associated with a plurality of first road signs within a geographic region, obtaining second sensor data associated with the geographic region, and determining a sign matching efficiency for the geographic region, based on the first sensor data and the second sensor data. The method further comprises generating the updated map data, based on the determined sign matching efficiency.

System and methods for generating updated map data

A micro point address is detected in response to an audio trigger. Audio data is detected at a mobile device and compared to at least one predetermined audio sample. In response to the comparison, location data is collected or a location stamp including the location data is made. A grid location identifier is stored in association with the location data in response to the comparison.

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Mumbai, India

Deekshant Saxena