A method for operating a motor vehicle in the event of an unavoidable collision with a collision object, in particular another vehicle. Environment data relating to the collision object are determined by an environment sensor device including at least one environment sensor and are evaluated to determine at least one driving intervention information for reducing the consequences of a collision. The motor vehicle is automatically guided in accordance with the driving intervention information, and the evaluation of the environment data is carried out together with structural information of the own motor vehicle describing the vehicle structure, in particular including elements absorbing collision energy of the motor vehicle, in such a way that a changed collision point maximizing the deformation energy absorbed by the vehicle structure and to be produced by the driving intervention information is determined when the driving intervention information is determined.

An advanced driver assistance system is provided. The advanced driver assistance system of the vehicle comprises a communicator configured to communicate with an obstacle detector configured to detect an obstacle; and a processor configured to determine a riding intention of a user in response to reception of a door unlocking instruction, obtain location information of other vehicles based on obstacle information detected by the obstacle detector in response to determining that the riding intention exists, determine a collision possibility with other vehicles based on the location information of other vehicles, and control output of notification information for a collision in response to determining that the collision possibility exists.

A method and system for determining risk indicators for intersections. A method includes receiving, by a computing node from intersection proximate sensors at each intersection, intersection data. For each intersection, the method includes determining, by the computing node for each connected vehicle proximate to an intersection, a driver risk score based on driver distraction data from the intersection data for the intersection, determining, by the computing node, a near miss score based on the intersection data for the intersection, and assigning, by the computing node for the intersection, an intersection risk indicator level based on an exponential moving average of intersection risk indicator scores determined from driver risk scores and near miss scores. The method includes providing, by the computing node, intersection risk indicator levels to each connected vehicle to facilitate control decisions by each connected vehicle when approaching intersections.

Techniques for accurately predicting and avoiding collisions with objects detected in an environment of a vehicle are discussed herein. A vehicle safety system can implement a model to output data indicating an intersection probability between the object and a portion of the vehicle in the future. The model may employ a rear collision filter, a distance filter, and a time to stop filter to determine whether a predicted collision may be a false positive, in which case the techniques may include refraining from reporting such predicted collision to other another vehicle computing device to control the vehicle.

A side collision risk estimation system for a vehicle comprises a speed sensor, a road line markers detector, a movement sensor, an object detector, and a controller. The controller is configured to estimate: the current speed of the vehicle, a heading of the adjacent road line ahead of the vehicle, a heading of the vehicle, a compensated heading of the vehicle, a predicted lateral change position of the vehicle, a heading of a target vehicle relative to the vehicle, the current speed of the target vehicle, the current lateral distance between the vehicles, the heading of the adjacent road line ahead of the target vehicle, a compensated relative heading of the target vehicle, a predicted lateral change position of the target vehicle, a predicted lateral distance over time between the vehicles, and a side collision risk over time from the predicted lateral distance between the vehicles.

A method of processing data for a driving automation system, the method comprising steps of: obtaining image data from a camera of an autonomous vehicle, AV; image processing the image data to obtain a vehicle registration mark, VRM, of another vehicle within the surrounding area of the AV; looking up the VRM in a vehicle information database to obtain information indicative of the make, the model and the date of manufacture of the other vehicle; looking up information indicative of the make, the model and the date of manufacture of the other vehicle in a vehicle dimensions database to obtain at least one dimension of the other vehicle; and updating a context of the autonomous vehicle based on said at least one dimension of the other vehicle.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for planning the future trajectory of an autonomous vehicle in an environment. In one aspect, a method comprises obtaining multiple types of scene data characterizing a scene in an environment that includes an autonomous vehicle and multiple agents; receiving route data specifying an intended route for the autonomous vehicle; for each data type, processing at least the data type using a respective encoder network to generate a respective encoding of the data type; processing a sequence of the encodings using an encoder network to generate a respective alternative representation for each data type; and processing the alternative representations using a decoder network to generate a trajectory planning output that comprises respective scores for candidate trajectories that represent predicted likelihoods that the candidate trajectory is closest to resulting in the autonomous vehicle successfully navigating the intended route.

Disclosed embodiments include technologies for improving safety mechanisms in computer assisted and/or automated driving (CA/AD) vehicles for protecting vulnerable road users (VRUs). Embodiments include Dynamic Contextual Road Occupancy Map (DCROM) for Perception aspects for VRU safety. Other embodiments are described and/or claimed.

An impairment analysis (“IA”) computer system for detecting an impairment is provided. The IA computer system is associated with a host vehicle, and includes at least one processor in communication with at least one memory device. The at least one processor is programmed to: (i) interrogate or otherwise scan a target vehicle by using a plurality of sensors included on a host vehicle to scan the target vehicle and a target driver; (ii) receive sensor data including target driver data and target vehicle condition data; (iii) analyze the sensor data by applying a baseline model to the sensor data; (iv) detect an impairment of the target driver or target vehicle based upon the analysis; and/or (v) output an alert signal to a host vehicle controller, or direct collision preventing actions (such as automatically engage vehicle safety systems), based upon the determination that the target driver or target vehicle is impaired.

A vehicle control device of an embodiment includes a recognizer configured to recognize a surrounding environment of a host vehicle and a driving controller configured to perform driving control by controlling one or both of a speed and steering of the host vehicle on the basis of a recognition result of the recognizer. In a case where a speed relationship between the host vehicle and a forward vehicle traveling in front of the host vehicle satisfies predetermined conditions, the driving controller is configured to perform passing control for passing the forward vehicle, and holds feature information of the forward vehicle under predetermined conditions.