A vehicular control system includes a camera disposed at a windshield of a vehicle and viewing forward through the windshield. With the equipped vehicle moving in a forward direction, a control, via image processing at an image processor of image data captured by the camera, determines presence of a pedestrian ahead of the vehicle and in the field of view of the camera. The control, via image processing at the image processor of image data captured by the camera, determines if the pedestrian present ahead of the equipped vehicle is moving across a path of travel of the equipped vehicle. The control, at least in part responsive to determination that the pedestrian is moving across the path of travel of the equipped vehicle, reduces forward speed of the equipped vehicle to allow the pedestrian to move out of the path of travel of the forward moving equipped vehicle.

This application discloses a computing system to implement sensor event detection and fusion system in an assisted or automated driving system of a vehicle. The computing system can monitor an environmental model to identify spatial locations in the environmental model populated with temporally-aligned measurement data. The computing system can analyze, on a per-sensor basis, the temporally-aligned measurement data at the spatial locations in the environmental model to detect one or more sensor measurement events. The computing system can utilize the sensor measurement events to identify at least one detection event indicative of an object proximate to the vehicle. The computing system can combine the detection event with at least one of another detection event, a sensor measurement event, or other measurement data to generate a fused detection event. A control system for the vehicle can control operation of the vehicle based, at least in part, on the detection event.

The present invention relates to a vehicle driving assistance method comprising the steps of: selecting a driver type; detecting the driver's condition; and controlling, in phases, at least one vehicle driving assistance function or selectively controlling a plurality of vehicle driving assistance functions, according to the selected driver type and the detected driver's condition.

Methods and systems for assessing, detecting, and responding to malfunctions involving components of autonomous vehicles and/or smart homes are described herein. Autonomous operation features and related components can be assessed using direct or indirect data regarding operation. Such assessment may be performed to determine the robustness of autonomous systems, including the use of virtual assessment of software components within a simulated environment. To this end, a server may retrieve one or more routines associated with autonomous operation. The server may also generate a set of test data associated with test conditions. The server may also execute an emulator that virtually simulates autonomous environment. The test data may be presented to the routines executing in the emulator to generate output data. The server may then analyze the output data to determine a quality metric.

There is provided a vehicle control apparatus that controls automated driving of a vehicle. The apparatus includes an extraction unit configured to extract an object existing around the vehicle from a scene image representing a peripheral status of the vehicle, and a control unit configured to calculate a moving locus of the object and a moving locus of the vehicle for a predetermined time from time when the scene image is acquired, and generate a moving locus by correcting the moving locus of the object based on the moving locus of the vehicle.

Systems and methods are provided for navigating an autonomous vehicle using reinforcement learning techniques. In one implementation, a navigation system for a host vehicle may include at least one processing device programmed to: receive, from a camera, a plurality of images representative of an environment of the host vehicle; analyze the plurality of images to identify a navigational state associated with the host vehicle; provide the navigational state to a trained navigational system; receive, from the trained navigational system, a desired navigational action for execution by the host vehicle in response to the identified navigational state; analyze the desired navigational action relative to one or more predefined navigational constraints; determine an actual navigational action for the host vehicle, wherein the actual navigational action includes at least one modification of the desired navigational action determined based on the one or more predefined navigational constraints; and cause at least one adjustment of a navigational actuator of the host vehicle in response to the determined actual navigational action for the host vehicle.

A multi layer learning based control system and method for an autonomous vehicle or mobile robot. A mission planning layer, behavior planning layer and motion planning layer each having one or more neural neworks are used to develop an optimal route for the autonomous vehicle or mobile robot, provide a series of functional tasks associated with at least one or more of the neural networks to follow the planned optimal route and develop commands to implement the functional tasks.

Techniques are discussed for predicting locations of an object based on attributes of the object and/or attributes of other object(s) proximate to the object. The techniques can predict locations of a pedestrian proximate to a crosswalk as they traverse or prepare to traverse through the crosswalk. The techniques can predict locations of objects as the object traverses an environment. Attributes can comprise information about an object, such as a position, velocity, acceleration, classification, heading, relative distances to regions or other objects, bounding box, etc. Attributes can be determined for an object over time such that, when a series of attributes are input into a prediction component (e.g., a machine learned model), the prediction component can output, for example, predicted locations of the object at times in the future. A vehicle, such as an autonomous vehicle, can be controlled to traverse an environment based on the predicted locations.

Provided is a vehicle driving support system that achieves a balance between accurately evaluating the path cost of a candidate path and reducing the load of calculating the path cost. A vehicle driving support system includes a controller that sets a target path on a travel road based on travel road information. The controller sets, in the vicinity of an obstacle, a warning area with an outer shape according to the obstacle, and sampling points at first intervals along a part of the candidate path that is included in the warning area, and sets the sampling points at second intervals, longer than the first interval, along the other part of the candidate path.

A vehicle controller includes: a surrounding area recognition unit that detects a state surrounding a subject vehicle; a human detection unit that detects a specific target object in a specific area into which entry of the specific target object is restricted; an automated driving control part that provides control such that the subject vehicle follows a vehicle traveling ahead thereof, based on a result detected by the surrounding area recognition unit. The automated driving control part makes the subject vehicle operate at at least one of a first support status, and a second support status which has an automated degree higher than that of the first support status or has a task required to be done by a vehicle occupant of the subject vehicle less than that of the first support status. When the subject vehicle is traveling at the first support status and the human detection unit has detected a specific target object, the automated driving control part keeps the support status of the subject vehicle unchanged at the first support status. When the subject vehicle is traveling at the second support status and the detection unit has detected therein a specific target object, the automated driving control part shifts the support status of the subject vehicle from the second support status to the first support status.