This on-vehicle system is to be mounted in a vehicle and is provided with an electronic control device and an externality recognition sensor. The externality recognition sensor is equipped with a sensing unit for acquiring pre-processing externality information through sensing operations. The on-vehicle system is further equipped with: a condition calculation unit that, on the basis of a vehicle position, a vehicle traveling direction, and map information, calculates a processing condition in which information specifying an area on a map is associated with processing priority of the pre-processing externality information acquired by the externality recognition sensor; and a processing object determination unit that, on the basis of the pre-processing externality information and the processing condition, creates externality information having a smaller amount of information compared with the pre-processing externality information.

Techniques for determining an object contour are discussed. Depth data associated with an object may be received. The depth data, such as lidar data, can be projected onto a two-dimensional plane. A first convex hull may be determined based on the projected lidar data. The first convex hull may include a plurality of boundary edges. A longest boundary edge, having a first endpoint and a second endpoint, can be determined. An angle can be determined based on the first endpoint, the second endpoint, and an interior point in the interior of the first convex hull. The longest boundary edge may be replaced with a first segment based on the first endpoint and the interior point, and a second segment based on the interior point and the second endpoint. An updated convex hull can be determined based on the first segment and the second segment.

A vehicular camera includes a lens barrel accommodating a lens, a lens holder having a passageway therethrough, and an imager printed circuit board (imager PCB) having an imager. The imager PCB is attached at the lens holder. An attaching portion of the lens barrel is positioned at least partially in the passageway of the lens holder with a gap between the attaching portion and the lens holder that at least partially circumscribes the attaching portion. With the attaching portion positioned at least partially in the passageway, a filler material is disposed at least partially within the gap. With the lens aligned relative to the imager, the filler material is heated via non-contact brazing to melt and flow into the gap. The melted filler material hardens upon cooling to secure the lens barrel relative to the lens holder and the imager PCB.

A system of controlling operation of a vehicle includes one or more sensors operatively connected to the vehicle. The sensors are configured to obtain respective data of a scene and include a radar unit. A command unit is adapted to receive the respective data and includes a processor and tangible, non-transitory memory on which instructions are recorded. The command unit is configured to determine an orientation angle of a pedestrian in the scene, a Doppler frequency of the pedestrian and a distance of the pedestrian from a border of a road, based in part on the respective data. The orientation angle is based on a heading of the pedestrian relative to a direction of a road. The command unit is configured to designate a status of the pedestrian as either crossing or not crossing based on the distance, the orientation angle and the Doppler frequency of the pedestrian.

An automated driving system includes an object detection system. A neural network image encoder generates image embeddings associated with an image including an object. A neural network text encoder generates concept embeddings associated with each of a plurality of concepts. Each of the plurality of concepts is associated with one of at least two object classes. A confidence score module generates a confidence score for each of the plurality of concepts based on the image embeddings and the concept embeddings associated with the concept. An object class prediction module generates a predicted object class of the object based on an association between a set of concepts of the plurality of concepts having at least two of the highest values of the generated confidence scores and the one of the at least two object classes associated with a majority of the set of concepts.

A vehicle driving assistance apparatus includes a sensing unit configured to sense an object outside a vehicle, and a processor. The processor is configured to obtain surrounding situation information, determine whether the object approaches the vehicle from a traveling lane or a lateral lane based on a location of the object outside the vehicle, and generate a control signal to control at least one of a drive apparatus of the vehicle, a steering apparatus of the vehicle, or a brake apparatus of the vehicle. The vehicle driving assistance apparatus enables the vehicle (i) to avoid collision between the vehicle and the object or (ii) to perform an action that reduces an impulse on the vehicle from the collision, and provide the control signal to a vehicle control system of the vehicle.

A vehicle control system includes: a reception section configured to receive a selection operation for one or more driving modes by an occupant of a vehicle from out of plural driving modes having different control characteristics related to acceleration/deceleration or cornering; and an automated driving controller configured to perform automated driving in which at least one of speed control and steering control of the vehicle is controlled automatically based on the driving mode received as the selection operation by the reception section.

Methods and systems for assessing, detecting, and responding to malfunctions involving components of autonomous vehicles and/or smart homes are described herein. Autonomous operation features and related components can be assessed using direct or indirect data regarding operation. Vehicle collision and/or smart home incident monitoring, damage detection, and responses are also described, with particular focus on the particular challenges associated with incident response for unoccupied vehicles and/or smart homes. Operating data associated with the autonomous vehicle and/or smart home may be received. Within the operating, an unusual condition indicative of a likelihood of incident may be detected. Based on the unusual condition, it may be determined that the incident occurred. Accordingly, a response to the incident may be determined. The response may be implemented by the autonomous vehicle and/or smart home.

A passive infra-red pedestrian detection and avoidance system and method for augmenting the operation of a vehicle on a roadway, especially for identifying potential pedestrian/vehicular collision danger for the vehicle in operation and adjusting the position and operation of the vehicle accordingly, includes at least one passive infra-red sensor array mounted on the vehicle in operative communication with an image processor tied into the operational system of the vehicle. The system detects, using thermal imaging and processing, the presence of people that may be in or laterally crossing into the travel lane of the vehicle. The image processor analyzes the detection of a human thermal signature and determines if the detected human thermal signature is moving, in what direction and at what speed, to assess any potential threat to the pedestrian or biker, and further whether any responsive action needs to be triggered in the vehicle's operation to avoid a collision.

A navigation system for a host vehicle is provided. The system may comprise at least one processing device programmed to receive, from a camera, a plurality of images representative of an environment of the host vehicle; analyze the plurality of images to identify at least one vehicle-induced occlusion zone in an environment of the host vehicle; and cause a navigational change for the host vehicle based, at least in part, on a size of a target vehicle that induces the identified occlusion zone.