A system described herein may generate one or more models based on relationship data associated with multiple objects. The models may include one or more thresholds (e.g., which may indicate potential collisions between objects). The system may compare relationship data, associated with a particular vehicle and a particular object, with the one or more thresholds associated with the one or more models, and determine whether the relationship data associated with the particular vehicle and the particular object is rare data with respect to the one or more models. When the relationship data associated with the particular vehicle and the particular object is not rare data, the system may refraining from causing the particular vehicle to perform a collision prevention measure, and when the relationship data associated with the particular vehicle and the particular object is rare data, the system may cause the particular vehicle to perform the collision prevention measure.

A travel assistance method and a travel assistance device for a vehicle is capable of avoiding any risk that may arise. The method includes obtaining a risk potential of an object detected by the vehicle, associating the risk potential of the object with an encounter location at which the object is encountered, accumulating the risk potential at the encounter location, and using the accumulated risk potential to obtain a primary estimated risk potential of the object predicted to be encountered at the encounter location. The primary estimated risk potential is lower than the risk potential obtained when detecting the object. The method further includes obtaining a secondary estimated risk potential using a predicted travel movement of another vehicle that avoids a risk due to the primary estimated risk potential, and when traveling at the encounter location again, autonomously controlling travel of the vehicle using the secondary estimated risk potential.

Provided are systems, methods, and computer program products for controlling an autonomous vehicle (AV) to maneuver in a roadway, comprising acquiring, data associated with an actor detected on a route of the AV in the roadway for sensing a trajectory of the actor, predicting that the trajectory of the actor includes at least one characteristic that is associated with invoking a conditionally disallowed action in the AV, automatically restricting the conditionally disallowed action from a motion plan of the AV to prevent the AV from executing the conditionally disallowed action in response to detecting that one or more conditions are present in the roadway, issuing a command to control the AV on a candidate trajectory generated to prevent an option for the conditionally disallowed action.

Determining yaw parameter(s) (e.g., at least one yaw rate) of an additional vehicle that is in addition to a vehicle being autonomously controlled, and adapting autonomous control of the vehicle based on the determined yaw parameter(s) of the additional vehicle. For example, autonomous steering, acceleration, and/or deceleration of the vehicle can be adapted based on a determined yaw rate of the additional vehicle. In many implementations, the yaw parameter(s) of the additional vehicle are determined based on data from a phase coherent Light Detection and Ranging (LIDAR) component of the vehicle, such as a phase coherent LIDAR monopulse component and/or a frequency-modulated continuous wave (FMCW) LIDAR component.

A method of path planning for a host vehicle includes: receiving host vehicle, environmental and obstacle information; calculating one or more projected host vehicle locations; computing a projected obstacle location for each obstacle; and determining a collision potential between each projected host vehicle location and each projected obstacle location. Until a maximum number of steps is reached, and while at least one projected host vehicle location has an associated collision potential below a collision threshold, the method further includes repeating the calculating, computing and determining steps.

Enclosed are embodiments for navigation with drivable area detection. In an embodiment, a method comprises: receiving a point cloud from a depth sensor, receiving image data from a camera; predicting at least one label indicating a drivable area by applying machine learning to the image data; labeling the point cloud using the at least one label; obtaining odometry information; generating a drivable area by registering the labeled point cloud and odometry information to a reference coordinate system; and controlling the vehicle to drive within the drivable area.

A driving support system executes a risk avoidance control for reducing a risk of collision with an object in front of a vehicle. A risk potential field represents a risk value as a function of position. An obstacle potential field is a risk potential field in which the risk value is maximum at a position of the object and decreases as a distance from the object increases. A vehicle center potential field is the risk potential field in which a valley of the risk value extends in a lane longitudinal direction from a position of the vehicle. A first risk potential field is the sum of the vehicle center potential field and the obstacle potential field. The driving support system executes a steering control such that the vehicle follows the first valley of the risk value represented by the first risk potential field.

An object-tracking method includes using the velocity of a track for tracking an object, generated from a point cloud related to the object, in a current recognition period to obtain a current velocity-based heading angle of the track. The method includes accumulating scores associated with information about the current velocity-based heading angle and incorporating the accumulated scores into a heading history having regions sectioned for respective heading angles of the track. The method includes obtaining a history-based heading angle of the track using the heading history, correcting the history-based heading angle using information about the shape of the track, and determining the result of correction to be a final heading angle in the current recognition period.

A vehicle driving assistance system includes a travel route generation system that acquires travel road information and obstacle information acquired by sensors and the like and generates the target travel route, on which a host vehicle travels, on a travel road. In the case where the host vehicle changes lanes, the system acquires information on the two peripheral vehicles, which exist near the host vehicle, on the change destination lane from the obstacle information, sets a target space, to which the host vehicle 1 should move, between the two peripheral vehicles on the change destination lane on the basis of this information on the two peripheral vehicles, predicts a future position of the target space on the basis of a moving speed of the target space, and generates the target travel route, on which the host vehicle travels during the lane change, on the basis of this predicted future position.

A host vehicle includes a rear image capturing device and a vehicle control device. The rear image capturing device captures an image of an area behind the host vehicle. The vehicle control device detects a lateral position of a following vehicle in the same lane as that in which the host vehicle travels, the following vehicle being reflected in the image captured by the rear image capturing device and traveling in the lane. The vehicle control device determines a lateral position of the host vehicle within a lane depending on the lateral position of the following vehicle within the same lane.