A vehicle control device generates a fusion target by fusion of a radar target of an object ahead of the own vehicle acquired as a reflected wave of a carrier wave and an image target of the object acquired by image processing of an acquired image of a region ahead of the own vehicle and performs vehicle control of the own vehicle for the object detected as the fusion target. The vehicle control device determines whether a state where the object is detected with the fusion target has transitioned to a state where the object is detected with only the radar target; determines whether a distance to the object is a predetermined short distance when the state is determined to have transitioned to the state where the object is detected with only the radar target; and performs the vehicle control for to the object when the distance to the object is determined to be the predetermined short distance.

This vehicle synchronization simulation device and means is provided with: a means for calculating positional information of the own vehicle; a means for transmitting the own vehicle positional information to a server means; a means for converting the own vehicle positional information into a specific data format and transmitting the same; a means for transferring the data via a network or a transmission bus inside of a specific device; a means for receiving the data and generating an image; a means for recognizing and detecting a specific object from the generated image; and a means for changing/correcting positional information of the own vehicle using the information resulting from recognition. The server means is provided with a means that synchronously controls three means, a own vehicle position calculating means, a transmitting/receiving means, and an image generating/recognizing means.

A system and a method for autonomous decisioning and operation by an autonomous agent includes: collecting decisioning data including: collecting a first stream of data includes observation data obtained by onboard sensors of the autonomous agent, wherein each of the onboard sensors is physically arranged on the autonomous agent; collecting a second stream of data includes observation data obtained by offboard infrastructure devices, the offboard infrastructure devices being arranged geographically remote from and in an operating environment of the autonomous agent; implementing a decisioning data buffer that includes the first stream of data from the onboard sensors and the second stream of data from the offboard sensors; generating current state data; generating/estimating intent data for each of one or more agents within the operating environment of the autonomous agent; identifying a plurality of candidate behavioral policies; and selecting and executing at least one of the plurality of candidate behavioral policies.

A vehicle and a control method thereof are provided to predict a behavior of a driver and a pedestrian and control the vehicle based on the predicted behavior of the driver and the pedestrian. The vehicle includes a capturer configured to capture an image around the vehicle; a behavior predictor configured to obtain joint image information corresponding to the joint motions of a pedestrian based on the captured image around the vehicle, predict behavior change of the pedestrian based on the joint image information, and determine the possibility of collision with the pedestrian based on the behavior change; and a vehicle controller configured to control at least one of stopping, decelerating and lane changing of the vehicle so as to avoid collision with the pedestrian when there is a possibility of collision with the pedestrian.

Human driving performance can be assessed using an autonomous vehicle. An autonomous vehicle can have a manual operational mode and one or more autonomous operational modes. While the vehicle is operating in the manual operational mode, driving data relating to a manual driving maneuver can be acquired. The acquired driving data can be evaluated relative to a driving scene model to determine whether the manual driving maneuver is acceptable or unacceptable based on the acquired driving data. Responsive to determining that the manual driving maneuver is unacceptable, feedback can be provided to a user. In some instances, the feedback can be active feedback or passive feedback. In some instance, the user can be the human driver of the vehicle, or some other person related to the driver in some manner.

Systems and methods are provided for navigating a vehicle using a crowdsourced sparse map. In one implementation, a method of autonomously navigating a vehicle along a road segment may include receiving a sparse map model, receiving at least one image representative of an environment of the vehicle, analyzing the sparse map model and the at least one image, and determining an autonomous navigational response for the vehicle based on the analysis of the sparse map model and the at least one image. The at least one image may be received from a camera, and the sparse map model may include at least one line representation of a road surface feature extending along the road segment, each line representation representing a path along the road segment substantially corresponding with the road surface feature.

A vehicle drive assistance system is provided, which includes a processor configured to execute a required driving ability estimating module to estimate a driver's driving ability required for driving a vehicle based on a traffic environment around the vehicle and drive assistance provided to the driver by the vehicle, a current driving ability estimating module to estimate a driver's current driving ability, and a changing module to reduce the required driving ability by performing reduction processing in which comprehension of the traffic environment by the driver is facilitated when the current driving ability is lower than the required driving ability.

This application discloses a computing system to implement map building in an assisted or automated driving system. The computing system can track movement of a vehicle based on sensor measurement data populated in an environmental model and vehicle movement measurements. The computing system can correlate the tracked movement of the vehicle to map data based on a previously detected location of the vehicle relative to the map data. The computing system can modify the map data to include the sensor measurement data utilized to track the movement of the vehicle based on the correlation of the tracked movement of the vehicle to map data. The computing system can modify the map data by building a map of the sensor measurement data and the tracked movement of the vehicle, and populating the map data with the built map.

A vehicle safety support apparatus includes: a driver monitoring sensor configured to monitor a driver; an external environment monitoring sensor configured to monitor an external environment of a vehicle; a driver input filtering unit configured to filter a vehicle control input from the driver; and at least one processor connected to the driver monitoring sensor, the external environment monitoring sensor, and the driver input filtering unit, the at least one processor configured to: determine criterion based on data acquired from the driver monitoring sensor and the external environment monitoring sensor; determine whether to take over a driving control of the vehicle in response to the data acquired from the driver monitoring sensor and the external environment monitoring sensor meeting the criterion; and perform autonomous driving to move the vehicle to a safe area in response to determining to take over the driving control from the driver.

Systems and methods implemented in algorithms, software, firmware, logic, or circuitry may be configured to process data and sensory input to determine whether an object external to an autonomous vehicle (e.g., another vehicle, a pedestrian, road debris, a bicyclist, etc.) may be a potential collision threat to the autonomous vehicle. The autonomous vehicle may be configured to implement active safety measures to avoid the potential collision and/or mitigate the impact of an actual collision to passengers in the autonomous vehicle and/or to the autonomous vehicle itself. Interior safety systems, exterior safety systems, a drive system or some combination of those systems may be activated to implement active safety measures in the autonomous vehicle.