An electronic control device mounted on a vehicle includes: an information acquisition unit that acquires information regarding an environmental element around the vehicle, the environmental element including at least a road surface obstacle that is passable by the vehicle on a road surface; a risk map generation unit that generates a risk map representing a degree of traveling risk of the vehicle at each position around the vehicle based on the information; and a traveling control planning unit that determines a traveling track for traveling control for the vehicle based on the risk map, in which the traveling control planning unit determines the traveling track based on the degree of traveling risk due to the road surface obstacle on the risk map through which a wheel track of the vehicle in the traveling track passes.

A vehicle control device includes: a recognizer configured to recognize a surrounding situation of a vehicle including a predetermined object located near the vehicle; and a driving controller configure to control steering and a speed of the vehicle. The driving controller controls the speed of the vehicle such that the vehicle passes the predetermined object at a greater speed when the vehicle passes the predetermined object which is located ahead in a traveling direction of the vehicle and is moving in an opposite direction to the traveling direction of the vehicle than a speed when the vehicle passes the predetermined object which is located ahead in the traveling direction of the vehicle and is moving in the same direction as the traveling direction of the vehicle.

A method, system and device are disclosed for determining safety conflicts in redundant subsystems of autonomous vehicles. Each redundant subsystem calculates a world model or path plan, including locations, dimensions, and orientations of moving and stationary objects, as well as projected travel paths for moving objects in the future. The travel paths and projected future world models are subsequently compared using a geometric overlay operation. If at future time moments the projected world models match within predefined margins, the comparison results in a match. In case of a mismatch at a given future moment between projected world models, a determination is made as to whether the autonomous vehicle and all road users in this future moment are safe from collision or driving off the drivable space or road based on a geometric overlay operation.

Provided is a method of controlling driving of a vehicle using advance information, the method including acquiring preset local information including a road name, a road section, a road attribute, a location of a building, a lane, a traffic signal, and obstacle information for a predetermined region, acquiring a driving experience value resulting from a previous drive using the local information, and setting a target speed corresponding to the road attribute using the driving experience value, determining a driving state and a driving speed of the vehicle on the basis of the local information, and the target speed, of a current position of the vehicle, and generating a driving control command corresponding to the driving state and the driving speed.

In a vehicle control system (1, 101), a control unit (15) executes a stop process by which the vehicle is parked in a prescribed stop position when it is detected that the control unit or the driver has become incapable of properly maintaining a traveling state of the vehicle, and, in the stop process, if a sidewalk or a road sign is recognized ahead of the vehicle according to a signal from an external environment recognition device (6), the control unit determines the stop position according to the recognized sidewalk or road sign.

A vehicle control apparatus, a vehicle control method, and a program that can curb unnecessary driving control are provided. The vehicle control apparatus includes a pedestrian recognition unit configured to recognize a crossing pedestrian crossing a road on which a vehicle travels, a space recognition unit configured to recognize whether there is a space having a predetermined width or more between a lane on which the vehicle travels and an oncoming lane, and a driving control unit configured to execute avoidance support for avoiding contact between the vehicle and the crossing pedestrian recognized by the pedestrian recognition unit based on a behavior of the crossing pedestrian and a behavior of the vehicle, in which the driving control unit is configured to determine whether the crossing pedestrian recognized by the pedestrian recognition unit is moving from the oncoming lane side to a space recognized by the space recognition unit, and curb the avoidance support upon determination that the crossing pedestrian is moving to the space.

A stopping position control device according to an embodiment includes a sitting position specifying unit configured to specify a sitting position of a user who gets off a vehicle next, the vehicle being subjected to automatic driving control, and a stopping position determination unit configured to determine a stopping position of the vehicle, at which the user gets off the vehicle, corresponding to the sitting position of the user.

A temporal prediction model for semantic intent understanding is described. An agent (e.g., a moving object) in an environment can be detected in sensor data collected from sensors on a vehicle. Computing device(s) associated with the vehicle can determine, based partly on the sensor data, attribute(s) of the agent (e.g., classification, position, velocity, etc.), and can generate, based partly on the attribute(s) and a temporal prediction model, semantic intent(s) of the agent (e.g., crossing a road, staying straight, etc.), which can correspond to candidate trajectory(s) of the agent. The candidate trajectory(s) can be associated with weight(s) representing likelihood(s) that the agent will perform respective intent(s). The computing device(s) can use one (or more) of the candidate trajectory(s) to determine a vehicle trajectory along which a vehicle is to drive.

Provided are autonomous vehicles (AV), computer program products, and methods for maneuvering an AV in a roadway, including receiving forecast information associated with predicted trajectories of one or more actors in a roadway, determining a relevant trajectory of an actor based on correlating a forecast for predicted trajectories of the actor with the trajectory of the AV, regenerate a distance table for the relevant trajectory previously generated for processing constraints, generate a plurality of margins for the AV to evaluate, the margins based on a plurality of margin types for providing information about risks and effects on passenger comfort associated with a future proximity of the AV to the actor, classifying an interaction between the AV and the actor based on a plurality of margins, and generating continuous scores for each candidate trajectory that is also within the margin of the actor generated for the relevant trajectory.

Systems and methods are provided for navigating a host vehicle. A processing device may be programmed to receive an image representative of an environment of the host vehicle; determine a planned navigational action for the host vehicle; analyze the image to identify a target vehicle travelling toward the host vehicle; determine a next-state distance between the host vehicle and the target vehicle that would result if the planned navigational action was taken; determine a stopping distance for the host vehicle based on a braking profile, a maximum acceleration capability, and a current speed of the host vehicle; determine a stopping distance for the target vehicle based on a braking profile and a current speed of the target vehicle; and implement the planned navigational action if the determined next-state distance is greater than a sum of the stopping distances for the host vehicle and the target vehicle.