A receding horizon state estimator estimates state of a vehicle such as to reduce total communication cost of acquiring external measurements over a prediction horizon, in which state estimation accuracy for a time step is a function of state estimation accuracy for a previous time step. For each time step of the prediction horizon, estimator selects a subset of external sensors with external measurements sufficient to estimate the state with accuracy satisfying the constraint on state estimation accuracy for the corresponding time step while reducing a total communication cost of acquiring the external measurements over the prediction horizon. The estimator requests the external measurements from the subset of external sensors determined for a current time step and estimates the state of the vehicle using the internal and the requested external measurements.

A method of controlling engine on of a hybrid vehicle, may include determining, by a controller, a shift pattern of the vehicle between multiple regions; deriving, by the controller, a shifting possibility of the vehicle from each of the regions; and deriving an engine-on strategy of the vehicle on the basis of the derived shifting possibility, and controlling an engine of the vehicle to be on or off in accordance with the derived engine-on strategy.

The present disclosure is generally related to a data processing system to validate vehicular functions in a voice activated computer network environment. The data processing system can improve the efficiency of the network by discarding action data structures and requests that invalid prior to their transmission across the network. The system can invalidate requests by comparing attributes of a vehicular state to attributes of a request state.

Systems and methods for autonomous vehicle control are disclosed herein. According to some implementations, a method includes a scenario-specific operation control evaluation module (SSOCEM) based on a route of the vehicle. The SSOCEM includes a preferred model and one or more fallback models that respectively determine candidate vehicle control actions. The method includes instantiating a SSOCEM instance based on the SSOCEM. The SSOCEM determines a candidate vehicle control action by determining an approximate amount of time needed to determine a solution to the preferred model and determining an approximate amount of time until the upcoming scenario is reached. When the approximate amount of time needed to determine the solution is less than the approximate amount of time to reach the upcoming scenario, the candidate vehicle control action is determined based on the preferred model; otherwise, the candidate vehicle control action is determined based on a fallback model.

The embodiments described herein aim to improve environmental sensing by providing a computationally efficient and accurate means for fusing sensor data and using this fused data to control sensors to focus on areas that would most reduce the uncertainty in the sensing system. In this way, the system can direct sensors to focus on the most important areas and features within the environment in order to provide the most effective sensor data (e.g. for use by a control system). The methods described herein make use of multi-agent sensor-action fusion. The methods are multi-agent in that a set of machine learning agents are trained in order to control the sensors to focus on the most important features and regions. The embodiments implement sensor-action fusion in that sensor fusion is performed in order to obtain a combined view of the environment and this combined view is utilised to determine the most appropriate actions.

A vehicle control device includes a crossing vehicle detection sensor configured to detect a crossing vehicle approaching an own vehicle while traveling in an intersecting lane, the intersecting lane being a lane that intersects an own vehicle lane at an intersection at a time the own vehicle approaches the intersection, the crossing vehicle being a vehicle travelling in the intersecting lane, and a controller configured to set a plurality of candidate paths extending from the own vehicle, and assist traveling of the own vehicle based on the plurality of candidate paths. The controller is further configured to set, between the own vehicle and the crossing vehicle, a virtual area that moves with the crossing vehicle, and that extends in an advancing direction of the crossing vehicle, and set the plurality of candidate paths such that the plurality of candidate paths are prevented from passing through the virtual area.

An information processing apparatus according to an aspect of the present technology includes an estimation unit, a generation unit, and a frequency control unit. The estimation unit estimates at least one of a location or a posture of a mobile object. The generation unit generates a movement plan for moving the mobile object. The frequency control unit controls frequency of update of the movement plan to be performed by the generation unit, on the basis of load index information serving as an index of a load on the estimation unit.

Aspects of the disclosure relate to controlling a vehicle using a simplified model of an object. In one example, sensor data including a plurality of data points corresponding to surfaces of the object in the vehicle's environment may be received from one or more sensors of the vehicle. A first model may be determined using a subset of the plurality of data points. A set of secondary data points may be identified from the plurality of data points using a point on the vehicle. The set of secondary data points may be filtered from the subset of the plurality data points to determine a second model, wherein the second model is a simplified version of the first model. The vehicle may be controlled in an autonomous driving mode based on the second model.

Provided is a vehicle driving support system that achieves a balance between accurately evaluating the path cost of a candidate path and reducing the load of calculating the path cost. A vehicle driving support system includes a controller that sets a target path and a target stop position on a travel road based on travel road information. The controller sets sampling points at first intervals along a part of a candidate path that is in the vicinity of the target stop position, and sets the sampling points at second intervals, longer than the first interval, along the other part of the candidate path.

Provided is a vehicle driving support system that achieves a balance between accurately evaluating the path cost of a candidate path and reducing the load of calculating the path cost. A vehicle driving support system includes a controller that sets a target path on a travel road based on travel road information. The controller sets, in the vicinity of an obstacle, a warning area with an outer shape according to the obstacle, and sampling points at first intervals along a part of the candidate path that is included in the warning area, and sets the sampling points at second intervals, longer than the first interval, along the other part of the candidate path.