A traveling path setting method for a vehicle has a set traveling path of a host vehicle that includes a traveling path that turns across another lane at an intersection. The traveling path setting method is configured to set a traveling path for turning of the vehicle to an inner side of a turning direction when an adjacent vehicle exists within a predetermined distance outward of the traveling path of the host vehicle in the turning direction, compared to a case where no adjacent vehicle exists.

Embodiments of the present disclosure disclose a method for controlling an autonomous vehicle to pass through a curve, a device and a medium, and relate to the field of autonomous driving technologies. At least one implementation of the method for controlling an autonomous vehicle to pass through a curve includes: determining a curve boundary within a sensing area in a current driving direction of the autonomous vehicle based on a current position of the autonomous vehicle on the curve; determining a current safe stopping distance of the autonomous vehicle on the curve based on current driving parameters of the autonomous vehicle and the curve boundary; determining a speed threshold of the autonomous vehicle based on the current safe stopping distance, braking parameters of the autonomous vehicle and a curve curvature corresponding to the current position; and controlling a speed of the autonomous vehicle not to exceed the speed threshold.

Terrain characteristics of an off-road area are determined based on a map. The terrain characteristics include a terrain type, a terrain grade, and a presence or an absence of an obstacle. Vehicle characteristics are determined including a ground clearance and a breakover angle. Based on a user level, vehicle parameters for the off-road area are determined based on the terrain characteristics, the vehicle characteristics, and the user input. The vehicle parameters include a speed and a transmission gear. The vehicle parameters for the off-road area are output.

A method for the geometric representation of a vehicle area (1) of a vehicle for the purpose of collision detection, wherein the vehicle area (1) has a boundary (2), comprising the method steps of performing a medial axis transformation of the vehicle area (1) to generate a vehicle area skeleton (4) and performing a point classification of points of the vehicle area skeleton (4) to determine front corner region points (5, 6) and rear corner region points (7, 8), and a front wheelbase point (9) and a rear wheelbase point (10), and also performing a circle decomposition of the vehicle area (1), wherein each circle of the circle decomposition has a maximum area exceedance value (17).

A controlled intersection employs video analytics to identify incoming vehicles coupled with autonomous driving capabilities in the vehicle to selectively provide intervention for collision avoidance. A camera image of an approaching vehicle is used to identify a range and speed, and to compute whether intervention is appropriate based on a detected distance and speed from the intersection. A vehicle approaching a stop signal (e.g. “red light”) at an unsafe rate of speed triggers an invocation of on-board autonomous systems in the vehicle that provide appropriate warnings and ultimately, forced braking if warnings go unheeded. A registration system maintains a local grouping of vehicles in proximity to an intersection for minimizing latency in vehicle identification for commencing intervention. In this manner, on-board vehicle collision avoidance systems collaborate with complementary traffic control logic at a controlled intersection for preventing inadvertent or intentional disregard of a red signal.

Techniques for controlling a vehicle based on height data and/or classification data being determined utilizing multi-channel image data are discussed herein. The vehicle can capture lidar data as it traverses an environment. The lidar data can be associated with a voxel space as three-dimensional data. Semantic information can be determined and associated with the lidar data and/or the three-dimensional voxel space. A multi-channel input image can be determined based on the three-dimensional voxel space and input into a machine learned (ML) model. The ML model can output data to determine height data and/or classification data associated with a ground surface of the environment. The height data and/or classification data can be utilized to determine a mesh associated with the ground surface. The mesh can be used to control the vehicle and/or determine additional objects proximate the vehicle.

A method for providing an information signal (8) for at least partially automatic vehicle control involves an environment sensor system (3) of a motor vehicle (1) being used to generate measurement data for surroundings of the motor vehicle (1). A computing unit (2) of the motor vehicle (1) is used to identify an object (7) and a landmark (5) on the basis of the measurement data. The computing unit (4) is used to determine a first relative position of the object (7) in relation to the landmark (5) on the basis of the measurement data. A communication interface (4) of the motor vehicle (1) is used to generate the information signal (8) on the basis of the first relative position.

A problem of the present invention is to provide an object detection device etc. that can accurately detect an object regardless of a view angle position of and distance to the object. An object detection device of the present invention has: a stereo distance detection portion 105 that detects a distance to an object; a position detection portion 106 that detects a position of the object; a pose detection portion 111 that detects a pose of the object; a vehicle information input portion that inputs state information about a host vehicle and a different vehicle; a position prediction portion 109 that predicts a position of the different vehicle based on the state information about the host vehicle and the different vehicle; a pose prediction portion 110 that predicts a pose of the different vehicle based on the state information about the host vehicle and the different vehicle; and a determination portion 112 that determines a distance to, a position of, and a pose of the different vehicle in response to the information detected or predicted by the distance detection portion, the position detection portion, the pose detection portion, the position prediction portion, and the pose prediction portion.

Provided is a driving assistance system including a storage device having a program stored therein and a hardware processor, wherein the hardware processor executes the program stored in the storage device, to thereby recognize an object which is present outside of a vehicle on the basis of a detection result of at least one of a radar device and an imaging device which are mounted in the vehicle, perform driving assistance for the vehicle on the basis of a recognition result, and determine a degree of matching between a portion of a contour line of the object and a road partition line and suppress an operation of the driving assistance in a case where the degree of matching is equal to or greater than a threshold.

A driving support device includes a storage device storing a program and a hardware processor. The hardware processor executes the program stored in the storage device to perform driving support of a vehicle based on a detection result of at least one of a radar device and an imaging device mounted in the vehicle, determine whether the vehicle is traveling in an underpass which is a traffic route along which the vehicle is able to pass under an overlying structure, and suppress an operation of the driving support when the vehicle is determined to be traveling under the underpass.