Abstract: A system and method for path and/or motion planning and for training such a system are described. In one aspect, the method comprises generating a sequence of predicted occupancy grid maps (OGMs) for T?T1 time steps based on a sequence of OGMs for 0?T1 time steps, a reference map of an environment in which an autonomous vehicle is operating, and a trajectory. A cost volume is generated for the sequence of predicted OGMs. The cost volume comprises a plurality of cost maps for T?T1 time steps. Each cost map corresponds to a predicted OGM in the sequence of predicted OGMs and has the same dimensions as the corresponding predicted OGM. Each cost map comprises a plurality of cells. Each cell in the cost map represents a cost of the cell in corresponding predicted OGM being occupied in accordance with a policy defined by a policy function.

Abstract: A method for generation of an augmented point cloud with point features from aggregated 3D coordinate data and related device. The method comprises receiving a current point cloud in the form of 3D coordinate data in ego coordinates from one or more detection and ranging (DAR) devices of a vehicle. Features are extracted from the current point cloud. A previous point cloud is transformed into ego coordinates using a current location of the vehicle. Each point in the previous point cloud is transformed to align with a corresponding point in the current point cloud to generate a transformed point cloud. The current point cloud is aggregated with the transformed point cloud to generate an aggregated point cloud. The current point features are aggregated with the point features of the transformed point cloud to generate aggregated point features.

Abstract: A method for generation of an augmented point cloud with point features from aggregated 3D coordinate data and related device. The method comprises receiving a current point cloud in the form of 3D coordinate data in ego coordinates from one or more detection and ranging (DAR) devices of a vehicle. Features are extracted from the current point cloud. A previous point cloud is transformed into ego coordinates using a current location of the vehicle. Each point in the previous point cloud is transformed to align with a corresponding point in the current point cloud to generate a transformed point cloud. The current point cloud is aggregated with the transformed point cloud to generate an aggregated point cloud. The current point features are aggregated with the point features of the transformed point cloud to generate aggregated point features.

Abstract: A system and method for path and/or motion planning and for training such a system are described. In one aspect, the method comprises generating a sequence of predicted occupancy grid maps (OGMs) for T-T1 time steps based on a sequence of OGMs for 0-T1 time steps, a reference map of an environment in which an autonomous vehicle is operating, and a trajectory. A cost volume is generated for the sequence of predicted OGMs. The cost volume comprises a plurality of cost maps for T-T1 time steps. Each cost map corresponds to a predicted OGM in the sequence of predicted OGMs and has the same dimensions as the corresponding predicted OGM. Each cost map comprises a plurality of cells. Each cell in the cost map represents a cost of the cell in corresponding predicted OGM being occupied in accordance with a policy defined by a policy function.

Abstract: A method includes implementing a modularized front-end of the sensing platform on a substrate, utilizing real estate on the substrate for a microfluidic and/or a nanofluidic chamber, providing a mixing enclosure of a sample on the substrate, providing an electrochemical cell and one or more other sensor(s) on the substrate. The method also includes controlling, through a microcontroller communicatively coupled to a memory, operational parameters of the microfluidic and/or the nanofluidic chamber, the electrochemical cell and the one or more other sensor(s), data acquisition therefrom and post-processing of the acquired data to enable configuration and monitoring thereof and visualization of the post-processed data, and performing electrochemical and/or bioassay sensing based on the control, the data acquisition and the post-processing of the acquired data.