Abstract: A computing system can receive motion prediction data from a vehicle, where the motion prediction data is generated by a motion prediction model executing on the vehicle. Based on the motion prediction data, the system can determine predicted trajectories for a plurality of entities in a surrounding environment of the vehicle. The system can evaluate a prediction performance of the motion prediction model by (i) matching, for each respective entity of the plurality of entities, a predicted endpoint of each predicted trajectory of the set of predicted trajectories to one or more underlying lanes of the road segment, (ii) matching a ground truth future position of the entity to one or more underlying lanes of the underlying lane topology, and (iii) determining a distance along one or more lane segments between the lane(s) matched to the predicted endpoint and the lane(s) matched to the ground truth future position.

Abstract: A vehicle computing system can receive raw sensor data in both a traditional sensor data processing module and a learned sensor data processing module. Each module can reproject sensor data in BEV space, and can optionally perform sensor fusion when multiple sensor data types are processed.

Abstract: A vehicle computing system can receive sensor data from a sensor suite of the vehicle. The system can execute a autoencoder on the sensor data to compress the sensor data, and can stored the compressed sensor data in an on-board database of the vehicle.

Abstract: A backend computing system can receive compressed sensor data from a database of one or more vehicles that operate throughout a road network. The system can execute a set of learnable decoders on the compressed sensor data to decompress the compressed sensor data in accordance with a set of tasks of the set of learnable decoders.

Abstract: A system can perform a method that includes receiving sensor data from a subset of human-driven vehicles that have moved through an intersection in a region, where the sensor data indicates a respective set of trajectories of respective human-driven vehicles of the subset through the intersection. The method can further include processing the sensor data to classify driving behavior of each human-driven vehicle of the subset of human-driven vehicles through the intersection. The method can further include classifying the intersection to label an autonomy map to include pass-through information for at least one of autonomous vehicles or semi-autonomous vehicles driving through the intersection.

Abstract: A computing system can receive sensor data from a set of human-driven vehicles operating through a road segment. The system can process the sensor data to determine a set of right-of-way rules for autonomous vehicle driving through the road segment. In certain examples, the system can obtain an autonomous driving map utilized by autonomous vehicles for operating through the road segment, and modify the autonomous driving map to include the set of right-of-way rules for the road segment.

Abstract: In a method for the operation of an internal combustion engine either on all cylinders (as full engine) or with only some cylinders in operation (cylinder cut-off), the cylinders are divided, in the firing order, into two groups of alternating cylinders which are selectively operative with an angular ignition spacing twice that of the cylinders of the engine when operating as full engine, the two groups of cylinders being activated alternately during engine operation with cylinder cut-off.

Abstract: In a method for the operation of an internal combustion engine either on all cylinders (as full engine) or with only some cylinders in operation (cylinder cut-off), the cylinders are divided, in the firing order, into two groups of alternating cylinders which are selectively operative with an angular ignition spacing twice that of the cylinders of the engine when operating as full engine, the two groups of cylinders being activated alternately during engine operation with cylinder cut-off.