Abstract: The localization of a vehicle is determined using less expensive and computationally robust equipment compared to conventional methods. Localization is determined by estimating the position of a vehicle relative to a map of the environment, and the process thereof includes using a map of the surrounding environment of the vehicle, a model of the motion of the frame of reference of the vehicle (e.g., ego-motion), sensor data from the surrounding environment, and a process to match sensory data to the map. Localization also includes a process to estimate the position based on the sensor data, the motion of the frame of reference of the vehicle, and/or the map. Such methods and systems enable the use of less expensive components while achieving useful results for a variety of applications, such as autonomous vehicles.

Abstract: A system for use in a simulation is disclosed. The system may comprise a receiver to receive states of the simulation. A memory may store sets of hyperparameters for a neural network encoder. The memory may also store A-matrices. The neural network encoder, implemented using a processor, may use hyperparameters to implement an encoding function. The hyperparameters and an A-matrix may be selected from the memory responsive to the states of the simulation. The A-matrix may be used to determine a next predicted state for the simulation.

Abstract: A system for use in an autonomous vehicle simulation is disclosed. The system may comprise a processor to execute at least two models using an input state. The system may further comprise a state mixer to mix the output states of the two models to produce a mixed output state.

Abstract: The localization of a vehicle is determined using less expensive and computationally robust equipment compared to conventional methods. Localization is determined by estimating the position of a vehicle relative to a map of the environment, and the process thereof includes using a map of the surrounding environment of the vehicle, a model of the motion of the frame of reference of the vehicle (e.g., ego motion), sensor data from the surrounding environment, and a process to match sensory data to the map. Localization also includes a process to estimate the position based on the sensor data, the motion of the frame of reference of the vehicle, and/or the map. Such methods and systems enable the use of less expensive components while achieving useful results for a variety of applications, such as autonomous vehicles.