A method for fusing a first occupancy map and a second occupancy map comprises: determining at least one fusion parameter representing a potential dissimilarity between the first occupancy map and the second occupancy map and determining a fused occupancy map representing free and occupied space around the vehicle. The fused occupancy map is determined based on the first occupancy map, the second occupancy map, and a fusion rule. The fusion rule is configured to control the influence of the first occupancy map and/or the second occupancy map on the fused occupancy map based on the at least one fusion parameter.

A vehicle driving assist device comprises: an oncoming moving body recognizer configured to recognize an oncoming moving body; a risk determination region setting unit configured to set a risk determination region for calculating a risk degree that decreases as a distance outward from a center of the oncoming moving body in a width direction of the oncoming moving body increases; a risk degree calculator configured to calculate the risk degree for the oncoming moving body in accordance with an overlap state between the target traveling path of the vehicle and the risk determination region; and a preliminary collision avoidance controller configured to recognize the oncoming moving body as the obstacle in accordance with the risk degree, and perform preliminary collision avoidance control in response to the oncoming moving body recognized as the obstacle prior to the emergency collision avoidance control.

A vehicle driving assist device comprises: an oncoming moving body recognizer configured to recognize an oncoming moving body; a risk determination region setting unit configured to set a risk determination region for calculating a risk degree that decreases as a distance outward from a center of the oncoming moving body in a width direction of the oncoming moving body increases; a risk degree calculator configured to calculate the risk degree for the oncoming moving body in accordance with an overlap state between the target traveling path of the vehicle and the risk determination region; and a preliminary collision avoidance controller configured to recognize the oncoming moving body as the obstacle in accordance with the risk degree, and perform preliminary collision avoidance control in response to the oncoming moving body recognized as the obstacle prior to the emergency collision avoidance control.

A mobile object control device according to an embodiment includes a recognizer that recognizes a surroundings situation of a mobile object, and an unavoidable contact determiner that determines whether or not contact between the mobile object and an object likely to come into contact with the mobile object is unavoidable on the basis of a recognition result of the recognizer when there is the object around the mobile object, and the unavoidable contact determiner limits a recognition range of the recognizer to a predetermined range including the object when the recognition result of the recognizer satisfies a predetermined condition, and determines whether or not the contact between the mobile object and the object is unavoidable.

A mobile object control device according to an embodiment includes a recognizer that recognizes a surroundings situation of a mobile object, and an unavoidable contact determiner that determines whether or not contact between the mobile object and an object likely to come into contact with the mobile object is unavoidable on the basis of a recognition result of the recognizer when there is the object around the mobile object, and the unavoidable contact determiner limits a recognition range of the recognizer to a predetermined range including the object when the recognition result of the recognizer satisfies a predetermined condition, and determines whether or not the contact between the mobile object and the object is unavoidable.

Example embodiments relate to techniques for using vehicle image radar to estimate rain rate and other weather conditions. A computing device may receive radar data from a radar unit coupled to a vehicle. The radar data can represent the vehicle's environment. The computing device may use the radar data to determine a radar representation that indicates backscatter power and estimate, using a rain rate model, a rain rate for the environment based on the radar representation. The computing device may then control the vehicle based on the rain rate. In some examples, the computing device may provide the rain rate estimation and an indication of its current location to other vehicles to enable the vehicles to adjust routes based on the rain rate estimation.

Example embodiments relate to techniques for using vehicle image radar to estimate rain rate and other weather conditions. A computing device may receive radar data from a radar unit coupled to a vehicle. The radar data can represent the vehicle's environment. The computing device may use the radar data to determine a radar representation that indicates backscatter power and estimate, using a rain rate model, a rain rate for the environment based on the radar representation. The computing device may then control the vehicle based on the rain rate. In some examples, the computing device may provide the rain rate estimation and an indication of its current location to other vehicles to enable the vehicles to adjust routes based on the rain rate estimation.

Aspects of the disclosure relate to controlling a vehicle having an autonomous driving mode. This may include receiving, by one or more processors of the vehicle, first information identifying a current relative humidity measurement within a sensor housing of a vehicle having an autonomous driving mode. The relative humidity measurement and pre-stored environmental map information may be used by the one or more processors to estimate a condition of a sensor within the sensor housing at a future time. This estimated condition may be used by the one or more processors to control the vehicle.

Aspects of the disclosure relate to controlling a vehicle having an autonomous driving mode. This may include receiving, by one or more processors of the vehicle, first information identifying a current relative humidity measurement within a sensor housing of a vehicle having an autonomous driving mode. The relative humidity measurement and pre-stored environmental map information may be used by the one or more processors to estimate a condition of a sensor within the sensor housing at a future time. This estimated condition may be used by the one or more processors to control the vehicle.

An environmental information acquiring unit (11) to acquire environmental information on an environment around a mobile object, an action information acquiring unit (12) to acquire action information on an action of a driver of the mobile object, a calculation unit (13) to obtain control information for performing automated driving control of the mobile object on the basis of the environmental information acquired by the environmental information acquiring unit (11) and a machine learning model (18) that uses the environmental information as an input and outputs the control information, a contribution information determining unit (14) to determine contribution information having a high degree of contribution to the control information on the basis of the environmental information and the control information, a cognitive information calculating unit (15) to calculate cognitive information indicating a cognitive region of the driver in the environment around the mobile object on the basis of the action information and the environmental information, a specification unit (16) to specify unrecognized contribution information estimated not to be recognized by the driver on the basis of the contribution information and the cognitive information, and an information output control unit (17) to output driving assistance information necessary for driving assistance on the basis of the unrecognized contribution information specified by the specification unit (16) are provided.