A vehicle system includes an electronic control unit. The electronic control unit is configured to execute a first program, a second program, and a third program. The first program is configured to recognize an object present around a vehicle, the second program is configured to store information related to the recognized object as time-series map data, and the third program is configured to predict a future position of the object based on the stored time-series map data. The first program and the third program are configured to be (i) first, individually optimized based on first training data corresponding to output of the first program and second training data corresponding to output of the third program, and (ii) then, collectively optimized based on the second training data corresponding to the output of the third program.

A system for controlling platooning by a following vehicle includes a sensor located in or on the following vehicle configured to detect data corresponding to a shape of a leading vehicle. The system further includes an electronic control unit (ECU) located in or on the following vehicle, coupled to the sensor, and configured to determine an optimal distance from the following vehicle to the leading vehicle based on the shape of the leading vehicle, the optimal distance corresponding to a distance at which drag applied to the following vehicle is reduced based on a pressure wake from the leading vehicle.

A driver alert system for an automobile includes a global positioning system adapted to monitor a location of an automobile, and a processor adapted to receive data from the global positioning system, receive data of historical wildlife position and migration habits within a pre-determined range from the automobile, calculate a distance from the automobile to an area of wildlife activity as indicated by the data of historical wildlife position and migration habits within the pre-determined range from the automobile, and provide an alert to a driver of the automobile when the data of historical wildlife position and migration habits indicates wildlife movement within the pre-determined range of the automobile and the automobile is within a triggering distance of such wildlife movement.

Techniques for detecting and responding to an emergency vehicle are discussed. A vehicle computing system may determine that an emergency vehicle based on sensor data, such as audio and visual data. In some examples, the vehicle computing system may determine aggregate actions of objects (e.g., other vehicles yielding) proximate the vehicle based on the sensor data. In such examples, a determination that the emergency vehicle is operating may be based on the actions of the objects. The vehicle computing system may, in turn, identify a location to move out of a path of the emergency vehicle (e.g., yield) and may control the vehicle to the location. The vehicle computing system may determine that the emergency vehicle is no longer relevant to the vehicle and may control the vehicle along a route to a destination. Determining to yield and/or returning to a mission may be confirmed by a remote operator.

A surrounding situation information acquisition device acquires surrounding situation information of a vehicle. A steering angle sensor detects a steering angle and a steering direction of the vehicle. A steering assist controller executes traveling control involving steering assist control based on information output from the surrounding situation information acquisition device and information output from the steering angle sensor. The steering assist controller recognizes, based on the information output from the surrounding situation information acquisition device, an oncoming vehicle in an oncoming lane adjacent to a traveling lane of the vehicle and an avoidance target on a road shoulder side of the traveling lane of the vehicle, estimates an avoidance priority level of the oncoming vehicle and an avoidance priority level of the avoidance target, and sets a new target lane keeping traveling path of the vehicle based on the avoidance priority levels.

A method of processing data for a driving automation system, the method comprising steps of: obtaining image data from a camera of an autonomous vehicle, AV; image processing the image data to obtain a vehicle registration mark, VRM, of another vehicle within the surrounding area of the AV; looking up the VRM in a vehicle information database to obtain information indicative of the make, the model and the date of manufacture of the other vehicle; looking up information indicative of the make, the model and the date of manufacture of the other vehicle in a vehicle dimensions database to obtain at least one dimension of the other vehicle; and updating a context of the autonomous vehicle based on said at least one dimension of the other vehicle.

Path specific rolling resistance is determined in a method including: receiving, from a first source, first rolling resistance data of a first set of road surface portions of a corresponding first set of times; receiving, from a second source, second rolling resistance data of a second set of road surface portions of a corresponding second set of times; determining, using any of the first rolling resistance data and the second rolling resistance data, a path specific rolling resistance for a given path including one or more of the road surface portions selected from the first and second set of road surface portions; and providing at least one target vehicle with the path specific rolling resistance or a derivative of the path specific rolling resistance.

The disclosure relates to a vehicle velocity control method. The method includes: determining, by an onboard sensor, drivable distances in different directions in front of a current vehicle, and obtaining, at least based on types of targets in the different directions, an area of a drivable space in front of the current vehicle; determining, based on the area of the drivable space and a current vehicle velocity, a result of a safety degree in a current driving scenario; and controlling the vehicle velocity of the current vehicle based on the result of the safety degree. The disclosure further relates to a vehicle control device, a computer storage medium, and a vehicle.

An autonomous vehicle (AV) includes features that allows the AV to comply with applicable regulations and statues for performing safe driving operation. An example system for an AV includes obtaining, by a computer located in the AV, an image from a camera located on the AV, where the image characterizes an area towards which the AV is driven on a lane on a road or a highway; determining, from the image, that a pedestrian or a cyclist is located next to the lane on the road or the highway; and in response to the determining, performing driving operations on the AV such as steering from a center of the lane to a first side of the lane that is away from the center of the lane and away from a location of the pedestrian or the cyclist, and/or slowing down the AV in response to certain conditions.

The present disclosure relates to an information processing apparatus, an information processing method, and a program that allow for appropriately pulling over to a safe road shoulder when an emergency occurs during automated driving. On the basis of distance information from a depth sensor or the like, a travelable region available for a vehicle to travel is set like an occupancy map, and image attribute information is generated from an image by semantic segmentation. On the basis of the image attribute information, an evacuation space is set in the travelable region in accordance with the situation of the road surface of the travelable region, and thus an evacuation space map is created. The present disclosure can be applied to a mobile object.