Systems including one or more sensors, coupled to a vehicle, may detect sensor information and provide the sensor information to another computing device for processing. A system includes one or more sensors, coupled to a vehicle and configured to detect sensor information, and a computing device configured to communicate with one or more mobile sensors to receive the mobile sensor information, communicate with the one or more sensors to receive the sensor information, and analyze the sensor information and the mobile sensor information to identify one or more risk factors.

The vehicle control device sets a region including a stationary object on a road, calculates a passing position at which a host vehicle and an oncoming vehicle pass each other in accordance with a velocity of the host vehicle and a position and a velocity of the oncoming vehicle, calculates a first score that is a larger value as the velocity of the oncoming vehicle is greater, calculates a second score that is a larger value as an acceleration rate of the oncoming vehicle is greater, integrates the first score with the second score so as to calculate an integration score, and causes the host vehicle to decelerate when the integration score is greater than or equal to a predetermined value or causing the host vehicle to keep the velocity or accelerate when the integration score is smaller than the predetermined value.

A risk prediction determination device includes a predicted traveling route specifying unit, a risk location specifying unit, and a risk prediction determination unit. The predicted traveling route specifying unit specifies a predicted traveling route of a subject vehicle based on traveling lane information indicating a traveling lane of the subject vehicle, road type information indicating a type of a road around the subject vehicle, road link information related to a road link, and sensor information of the subject vehicle. The risk location specifying unit specifies a location with risk based on road shape information around the subject vehicle and location information of an obstacle. The risk prediction determination unit performs a risk prediction determination based on a specified result of the predicted traveling route specifying unit and a specified result of the risk location specifying unit.

According to one aspect, uncertainty prediction based deep learning may include receiving, using a memory, a trained neural network policy π trained based on a first dataset in a first environment, implementing, via a controller, the trained neural network policy π in a second environment by receiving an input and generating an output y, calculating an uncertainty array U[T] for a time window T, wherein the uncertainty array is indicative of a level of uncertainty associated with an output sample distribution of the output across the time window T based on a temporal divergence, an entropy H, a variational ratio VR, and a standard deviation SD of the output y, and executing, via the controller and one or more systems, an action based on the uncertainty array U[T], such as discontinuing use of the trained neural network policy π.

In a driving assistance apparatus, an area setter is configured to set a warning area on at least one of a left rear side and a right rear side of a subject vehicle. A notifier is configured to provide a notification in response to an object detector detecting an object in the warning area. A determiner is configured to determine initiation and completion of a turn at an intersection during turning of the subject vehicle at the intersection. An area changer is configured to, in response to the determiner determining the initiation of the turn, reduce the warning area in a direction approaching the subject vehicle, and in response to the determiner determining the completion of the turn, expand the reduced warning area according to a traveling state of the subject vehicle after the completion of the turn.

Embodiments for operational envelope detection (OED) with situational assessment are disclosed. Embodiments herein relate to an operational envelope detector that is configured to receive, as inputs, information related to sensors of the system and information related to operational design domain (ODD) requirements. The OED then compares the information related to sensors of the system to the information related to the ODD requirements, and identifies whether the system is operating within its ODD or whether a remedial action is appropriate to adjust the ODD requirements based on the current sensor information. Other embodiments are described and/or claimed.

A device may receive accelerometer data and video data for a vehicle and may identify bounding boxes and object classes for objects near the vehicle. The device may identify tracks for the objects and may filter out tracks that are not associated with vehicles or vulnerable road users to generate one or more tracks or an indication of no tracks. The device may generate a collision cone identifying a drivable area of the vehicle to identify objects more likely to be involved in a collision and may filter out tracks from the one or more tracks, based on the bounding boxes, and to generate a subset of tracks or another indication of no tracks. The device may determine scores for the subset of tracks and may identify a track of the subset of tracks with a highest score. The device may perform actions based on the identified track.

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.

An apparatus is obtained which provides, to a vehicle, information on a road-surface and that on an obstacle(s), and information on the degree of reliability, on the basis of information received from roadside units. The apparatus includes a communications circuitry to receive obstacle information from the roadside units where each unit includes a plurality of sensors to detect the obstacle(s) within a predetermined field of view. A reliability determinator determines the degree of reliability of information on an obstacle(s) received, and outputs a reliability value on the bases of the number of roadside units detecting the same obstacle, the number of sensors in one roadside unit detecting the same obstacle, and a difference value of detecting the same obstacle between different roadside units or that of detecting the same obstacle between different sensors; and a correction term is outputted on the basis of information on a road-surface condition.

The control device according to the present disclosure executes a process of acquiring an image captured by a camera provided in a vehicle and estimating an vehicle-to-vehicle distance and a relative speed with the preceding vehicle based on an image, a process of calculating a time to collision with respect to the preceding vehicle from the vehicle-to-vehicle distance and the relative speed, a deceleration process of decelerating the vehicle based on the vehicle-to-vehicle distance, the relative speed, and the time to collision, and a process of performing a warning to the driver of the vehicle upon receiving that the amplitude of the variation of any one physical quantity of the vehicle-to-vehicle distance, the relative speed, or the time to collision has become equal to or greater than a predetermined threshold value.