A system for notifying emergency services of a vehicular crash may (i) receive sensor data of a vehicular crash from at least one mobile device associated with a user; (ii) generate a scenario model of the vehicular crash based upon the received sensor data; (iii) store the scenario model; and/or (iv) transmit a message to one or more emergency services based upon the scenario model. As a result, the speed and accuracy of deploying emergency services to the vehicular crash location is increased. The system may also utilize vehicle occupant positional data, and internal and external sensor data to detect potential imminent vehicle collisions, take corrective actions, automatically engage autonomous or semi-autonomous vehicle features, and/or generate virtual reconstructions of the vehicle collision.

A method for detecting fallen cargo, the method may include receiving by a computerized system, sensed information related to driving sessions of multiple vehicles; applying a machine learning process on the sensed information to detect fallen cargo and to classify the fallen cargo to fallen cargo classes; estimating, from the sensed information, an impact of at least some of the fallen cargo classes on a behavior of at least some of the multiple vehicles; and determining, based on the impact, at least one suggested vehicle behavior as a response to a detection of at least some of the fallen cargo classes.

Disclosed is an image processing apparatus that includes one or more processors; and a memory, the memory storing instructions. When executed by the one or more processors, cause the one or more processors to: generate distribution data indicating a frequency distribution of horizontal distance values of a range image based on the range image having pixel values according to distance of an object in a plurality of captured images; predict a predicted position of the object in the distribution data with respect to a range image of a current frame, based on the distribution data with respect to range images of a plurality of previous frames; and continue tracking of the object, based on a similarity between a region of the object in a previous frame and a region in the current frame with respect to the predicted position of the object.

A safety system for a vehicle may include a processor configured to determine whether a further vehicle is approaching the vehicle from a backside or a lateral side; determine that a collision of the further vehicle with the vehicle is likely; determine an evasive maneuver of the vehicle such that the evasive maneuver reduces the collision likelihood or impact between the vehicle and the further vehicle; and provide control instructions to control the vehicle to perform the evasive maneuver.

A driving support device comprises a passing determination unit which determines whether an own vehicle is going to pass by a stopped vehicle stopped in an adjacent lane adjacent to a traveling lane on which the own vehicle is traveling; and a notification control unit which issues warning notification when it is determined that the own vehicle is going to pass by the stopped vehicle.

Provided is an anti-collision control device of a vehicle, and a method therefore. Particularly, provided is a method and apparatus for avoiding a collision by previously sensing a risk of collision with a neighboring vehicle when a vehicle is located in a blind spot of the neighboring vehicle.

A crosswind effect estimation device is mounted in a vehicle and configured to estimate an effect on the vehicle due to a crosswind. The crosswind effect estimation device includes a processor configured to i) acquire information on the crosswind in a predetermined region forward of the vehicle in a traveling direction of the vehicle, ii) acquire information on a shielding object that is located on a windward side in a direction of the crosswind, and iii) estimate the effect on the vehicle due to the crosswind based on the acquired information on the crosswind and the acquired information on the shielding object.

An autonomous grain cart includes a width less than or equal to a distance from an end of the header of an agricultural vehicle to a lateral side of the agricultural vehicle, wherein the end and the lateral side are on a same longitudinal side of a lateral centerline of the agricultural vehicle, wherein the autonomous grain cart is configured to receive grain from the agricultural vehicle. The autonomous grain cart also includes a controller, comprising a processor and a memory. The autonomous grain cart further includes a drive system communicatively coupled to the controller, wherein the controller is configured to instruct the drive system to propel the autonomous grain cart. The autonomous grain cart also includes a steering system communicatively coupled to the controller, wherein the controller is configured to instruct the steering system to steer the autonomous grain cart.

A vehicle control method for controlling a vehicle using a vehicle control apparatus includes: a sensor configured to detect a state outside a subject vehicle; and a control device. The vehicle control method includes: executing control of recovering a travel trajectory of the subject vehicle to a target trajectory, as ordinary control, by giving a steering amount in a lateral direction with respect to a travel lane of the subject vehicle; using detection data of the sensor to determine whether or not another vehicle is traveling in an adjacent lane to the travel lane of the subject vehicle; and when determining that the other vehicle is traveling in the adjacent lane ahead of the subject vehicle, increasing a response of the steering amount to a higher response than that in the ordinary control, before the subject vehicle passes the other vehicle.

A system for reconstructing a vehicular crash (i) receives sensor data of a vehicular crash from at least one mobile device associated with a user; (ii) generates a scenario model of the vehicular crash based upon the received sensor data; (iii) transmits the scenario model to a user computer device associated with the user; (iv) receives a confirmation of the scenario model from the user computer device; (v) stores the scenario model; and (vi) may generate at least one insurance claim form based upon the scenario model. As a result, the speed and accuracy of the claim processing is increased. The system may also utilize vehicle occupant positional data, and internal and external sensor data to detect potential imminent vehicle collisions, take corrective actions, automatically engage autonomous or semi-autonomous vehicle features, and/or generate virtual reconstructions of the vehicle collision.