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 rate, a maximum acceleration capability, and a current speed of the host vehicle; determine a stopping distance for the target vehicle based on a braking rate, a maximum acceleration capability, 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.

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.

A collision warning system notifies an occupant of a vehicle about a predicted collision between the vehicle and an object moving relative to the vehicle. The system includes an object input device, a vehicle input device, and a road detection module for generating a road signal associated with a road geometry that is visible through the windshield and located within a field of view of the occupant. The system further includes an ARHUD for generating multiple images upon the windshield and overlaid onto the road geometry. The system further includes a computer including a processor and a non-transitory computer readable storage medium storing instructions. The processor is programmed to determine a predicted collision between the object and the vehicle at a predicted time. The processor is further programmed to generate an actuation signal for actuating the ARHUD to generate the images upon the windshield and overlaid onto the road geometry.

The present disclosure is directed to autonomous vehicle having a vehicle control system. The vehicle control system includes a processing system that receives input values that indicate attributes of an object within a threshold distance of the autonomous vehicle and variance values indicating uncertainty associated with the input values. The processing system also provides a plurality of outcomes that are associated with combinations of split decisions. A given split decision indicates whether a particular input value is above or below a threshold value associated with the given split decision. The processing system further determines (i) a probability that the particular input value is above a threshold value and (ii) a probability that the particular input is below the threshold value for a given split decision. Additionally, the processing system determines one or more likelihoods associated with a given outcome. Further, the processing system provides instructions to control the autonomous vehicle.

An apparatus for controlling a host vehicle may include: a processor configured to calculate a cut-in possibility in which a nearby vehicle cuts into a lane on which the host vehicle travels ahead of the host vehicle, to determine a plurality of cut-in steps of the calculated cut-in possibility, and to control operation of the host vehicle so as to perform an inter-vehicle distance control operation or to provide a warning to a user of the host vehicle based on a state of the user in each of the plurality of cut-in steps; and storage configured to store the calculated cut-in possibility.

Techniques for determining unified futures of objects in an environment are discussed herein. Techniques may include determining a first feature associated with an object in an environment and a second feature associated with the environment and based on a position of the object in the environment, updating a graph neural network (GNN) to encode the first feature and second feature into a graph node representing the object and encode relative positions of additional objects in the environment into one or more edges attached to the node. The GNN may be decoded to determine a predicted position of the object at a subsequent timestep. Further, a predicted trajectory of the object may be determined using predicted positions of the object at various timesteps.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for selecting actions for an agent at a specific real-world location using historical data generated at the same real-world location. One of the methods includes determining a current geolocation of an agent within an environment; obtaining historical data for geolocations in a vicinity of the current geolocation of the agent from a database that maintains historical data for a plurality of geolocations within the environment, the historical data for each geolocation comprising observations generated at least in part from sensor readings of the geolocation captured by vehicles navigating through the environment; generating an embedding of the obtained historical data; and providing the embedding as an input to a policy decision-making system that selects actions to be performed by the agent.

Various technologies described herein pertain to generating an occupancy grid movie for utilization in motion planning for the autonomous vehicle. The occupancy grid movie can be generated for a given time and can include time-stepped occupancy grids for future times that are at predefined time intervals from the given time. The time-stepped occupancy grids include cells corresponding to regions in an environment surrounding the autonomous vehicle. Probabilities can be assigned to the cells specifying likelihoods that the regions corresponding to the cells are occupied at the future times. Moreover, cached query objects that respectively specify indices of cells of a grid occupied by a representation of an autonomous vehicle at corresponding orientations are described herein. An occupancy grid for the environment surrounding the autonomous vehicle can be queried to determine whether cells of the occupancy grid are occupied utilizing a cached query object from the cache query objects.

A method, apparatus and computer program product are provided for automatically identify wrong-way driven vehicles in real-time on a roadway. Methods may include: receiving an image from at least one image sensor captured at a location; processing the image using a computer vision model to detect features within the image; identifying, using the computer vision model, a wrong-way driven vehicle in the image; incrementing a confidence value for detection of the wrong-way driven vehicle; and generating an alert indicating the presence of the wrong-way driven vehicle at the location in response to the confidence value satisfying a predetermined value.

An apparatus for warning the collision of a vehicle includes an information acquirer configured to acquire information on a surrounding object and information on a vehicle, and a controller configured to generate collision predicting information for the surrounding object, based on the information on the surrounding object and the information on the vehicle, and generate control information to control braking of the vehicle and to provide, based on the collision predicting information, a buffer element to an outside of the vehicle while controlling the braking of the vehicle.