An information processing apparatus includes: a point group data acquisition unit configured to acquire, based on information from a sensor configured to detect an object existing in surroundings of a vehicle, point group data related to a plurality of points representing the object; a movement amount estimation unit configured to estimate a movement amount of the vehicle; a storage unit configured to store, as a point group map recorded in association with position information including a latitude and a longitude, relative positions of the plurality of points relative to a first reference position that is a place on a travel path of the vehicle; and a position estimation unit configured to estimate a position of the vehicle based on the point group map, the point group data, and the movement amount.

Aspects of the disclosure relate to methods for controlling a vehicle having an autonomous driving mode. For instance, sensor data may be received from one or more sensors of the perception system of the vehicle, the sensor data identifying characteristics of an object perceived by the perception system. When it is determined that the object is no longer being perceived by the one or more sensors of the perception system, predicted characteristics for the object may be generated based on one or more of the identified characteristics. The predicted characteristics of the object may be used to control the vehicle in the autonomous driving mode such that the vehicle is able to respond to the object when it is determined that the object is no longer being perceived by the one or more sensors of the perception system.

Systems, apparatuses and methods may provide for technology that generates, via a first neural network such as a grid network, a first vector representing a prediction of future behavior of an autonomous vehicle based on a current vehicle position and a vehicle velocity. The technology may also generate, via a second neural network such as an obstacle network, a second vector representing a prediction of future behavior of an external obstacle based on a current obstacle position and an obstacle velocity, and determine, via a third neural network such as a place network, a future trajectory for the vehicle based on the first vector and the second vector, the future trajectory representing a sequence of planned future behaviors for the vehicle. The technology may also issue actuation commands to navigate the autonomous vehicle based on the future trajectory for the vehicle.

A method predicts the trajectory of an ego vehicle travelling in a main lane. A lane change by the ego vehicle from the main lane to an adjacent lane is determined according to an estimate of the dynamic behavior of a group of vehicles travelling in the adjacent lane. The group of vehicles includes at least one main vehicle which is located near the ego vehicle and a secondary vehicle which is located behind the ego vehicle.

A display control device comprising: a depression acquisition part acquiring a current amount of depression of an accelerator pedal; a suitable range calculation part calculating, as a suitable depression range, a range of an amount of accelerator depression required for a following distance of a preceding vehicle and an ego vehicle to become a predetermined target following distance, based on the following distance; and a display control part displaying the current amount of depression and the suitable depression range at a display device able to be viewed by a driver.

A vehicle control method of an autonomous vehicle for a right and left turn at a crossroad includes: determining whether a second vehicle intends to change a lane while passing a front or a rear of a first vehicle in order to move to a target lane for the right and left turn at the crossroad; controlling the first vehicle to decelerate when it is determined that the second vehicle intends to change the lane while passing the front of the first vehicle; determining whether the second vehicle is entering the first lane toward the front or the rear of the first vehicle; calculating a steering amount of the second vehicle when it is determined that the second vehicle is entering the first lane toward the front of the first vehicle; and controlling the first vehicle to decelerate according to the steering amount.

A vehicle type database includes a plurality of vehicle type profiles. Each of the vehicle type profiles is associated with a vehicle type having a vehicle type specific aerodynamic profile and includes optimal following ranges associated with the vehicle type. Each of the optimal following distance ranges is based on the vehicle type specific aerodynamic profile of the vehicle type and a vehicle speed of the vehicle type wherein a trailing vehicle disposed in the optimal following range is configured to operate at an optimal fuel efficiency. A first optimal following distance range is identified based on a first vehicle type and a first vehicle speed using a first vehicle type profile associated with the first vehicle type. A command is issued to the AR vehicle HUD display system to display a graphical representation of the first optimal following distance to overlay an actual view of the road.

The travel assistance apparatus includes: a first Operating System (OS) that controls execution of at least one of a first application and/or a second application, the first application being for specifying a first travel control amount of a vehicle based on first movement information on a position and a speed of an object around the vehicle, the second application being for specifying a second travel control amount of the vehicle based on second movement information on a position and a speed of the object; a second OS that controls execution of a third application for performing travel control of the vehicle based on at least one of the first travel control amount and/or the second travel control amount; and a hypervisor that is executed on a processor and controls execution of the first OS and the second OS.

An autonomous vehicle (AV) includes features that allows the AV to comply with applicable regulations and statues for performing safe driving operation. Example embodiments disclosed herein provide enhanced high-precision operation of an AV in low-speed environments, such as a toll booth facility or heavy traffic. One example method disclosed herein includes a control computer identifying a starting point of the toll booth facility on the roadway and a plurality of toll lanes associated with the toll booth facility; selecting a particular toll lane; determining a trajectory for the AV that extends through the particular toll lane; and in response to the autonomous vehicle arriving at the starting point for the toll booth facility, transmitting, over a subsystem interface to one or more drive subsystems of the AV, instructions configured to cause the drive subsystems to operate together to cause the AV to travel according to the trajectory.

An apparatus for controlling a distance from a front vehicle includes a receiver to receive information on a host vehicle and information on the front vehicle, an acceleration generator to generate one of first acceleration for the host vehicle or second acceleration for the host vehicle, based on the received information on the host vehicle and the received information on the front vehicle and an output device to output the generated first acceleration or the generated second acceleration.