Methods and systems for autonomous and semi-autonomous vehicle routing are disclosed. Roadway suitability for autonomous operation is scored to facilitate use in route determination. Maps of roadways suitable for various levels of autonomous operation may be generated. Such map data may be used by autonomous vehicles or other computer devices in determining routes based upon criteria for vehicle trips. Such routes may be automatically updated based upon changes in road conditions, vehicle conditions, operator conditions, or environmental conditions. Emergency routing using such map data is described, such as automatic routing and travel when a passenger is experiencing a medical emergency.

A device and method for triggering an automatic response of a motor vehicle to an imminent accident situation by using data in a processing unit to create a traffic situation model of the existing traffic situation, analyzing a traffic situation and ascertaining response options, determining sequences to be expected beyond the primary accident incorporating subsequent movements of the road users and objects involved leading to secondary accidents or further subsequent accidents or potentially arising further hazardous situations, computing a probability distribution and/or an extent of injuries to persons and/or damage to objects of the road users and objects involved as a function of the response options, selecting that response option that is expected to have the smallest total degree of probability or the smallest extent of injury to persons and/or damage to objects overall for all road users and objects, and outputting control signals to initiate the selected response option.

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 collision detection device detects a probability of whether a vehicle will collide with a detected moving object. An ECU determines a high-risk region measured by a predetermined distance in a forward moving direction of the vehicle from a farthest feature point obtained from a farthest-located obstacle object detected on a road's shoulder. When the moving object is detected in the high-risk region, the ECU reduces a collision detection time-period within which the vehicle must detect whether the vehicle would collide with the moving object. The reduced collision detection time-period is shorter than a collision detection time-period to be used when the moving object is detected outside of the high-risk region.

The present invention provides an in-vehicle device (10) that includes a monitoring unit (11) that determines a state of an object on the basis of an output of a sensor mounted in a vehicle, a generation unit (12) that generates process information for causing the vehicle to execute a process in accordance with the state, a control unit (13) that causes an output device to output cause information indicating at least one of the object or the state which is a cause of the process, and a reception unit (14) that receives an input for changing the process due to the cause indicated by cause information, in which, on the basis of a reception result of the reception unit (14), the generation unit (12) generates the process information in which the process is changed.

An autonomous driving system mounted on a vehicle, includes: an information acquisition device configured to acquire driver operation information indicating an operation by a driver of the vehicle; and an autonomous driving control device configured to perform autonomous driving control of the vehicle. The autonomous driving control device performs: default lane setting processing that sets a default lane in which the vehicle travels; and vehicle travel control processing that controls the vehicle to travel in the default lane when there is no departure reason for departing from the default lane and to travel away from the default lane until there is no departure reason when there is the departure reason. In the default lane setting processing, the autonomous driving control device refers to the driver operation information to set the default lane according to the driver's intention.

An approach is provided for operating a vehicle using vulnerable road user data. The approach, for example, involves determining a road link on which the vehicle is traveling or expects to travel. The approach also involves querying a geographic database for a vulnerable road user attribute of the road link. The approach further involves providing a notification to activate or deactivate an autonomous driving mode of the vehicle, a vehicle sensor of the vehicle, or a combination thereof while the vehicle travels on the road link based on determining that the vulnerable road user attribute meets a threshold criterion.

An inter-vehicle management apparatus acquires scene information about a driving scene of a host vehicle, estimates the driving scene based on the scene information acquired, acquires behavior information about a driving behavior of the host vehicle driven by the user, determines a driving risk of the host vehicle driven by the user, based on the driving scene estimated and on the behavior information acquired, and controls a presentation of assist information by an information presentation unit in order to prompt the user to address the driving risk. When the driving scene estimated represents a follow-up traveling state of the host vehicle following the preceding vehicle, the assist information to be presented by the information presentation unit is selected based on a magnitude of the driving risk determined before the operation by an emergency control unit.

In one embodiment, an autonomous driving vehicle (ADV) speed following system determines how much and when to apply a throttle or a brake control of an ADV to maneuver the ADV around, or to avoid, obstacles of a planned route. The speed following system calculates a first torque force to accelerate the ADV, a second torque force to counteract frictional forces and wind resistances to maintain a reference speed, and a third torque force to minimize an initial difference and external disturbances thereafter between predefined target speed and actual speed of the ADV over a planned route. The speed following system determines a throttle-brake torque force based on the first, second, and third torque forces and utilizes the throttle-brake torque force to control a subsequent speed of the ADV.

Disclosed herein are an autonomous vehicle for preventing collisions effectively, and an apparatus and a method for controlling the autonomous vehicle. According to the autonomous vehicle, and the apparatus and the method for controlling the autonomous vehicle of the present disclosure, an area to which the vehicle has to move to avoid a collision may be preset through simple data before the collision at the same time as driving information is generated for regular autonomous driving, and when the collision occurrence is predicted, the collision is prevented by allowing the vehicle to move to the preset collision-avoidance area.

An autonomous vehicle, to which the present disclosure is applied, may be a vehicle that can drive itself to a destination without operations of a user. In this case, the autonomous vehicle may be linked with any artificial intelligence (AI) modules, any drones, any unmanned aerial vehicles, any robots, any augmented reality (AR) modules, any virtual reality (VR) modules, any 5th generation (5G) mobile communication devices, and the like.