Techniques are discussed for predicting locations of an object based on attributes of the object and/or attributes of other object(s) proximate to the object. The techniques can predict locations of a pedestrian proximate to a crosswalk as they traverse or prepare to traverse through the crosswalk. The techniques can predict locations of objects as the object traverses an environment. Attributes can comprise information about an object, such as a position, velocity, acceleration, classification, heading, relative distances to regions or other objects, bounding box, etc. Attributes can be determined for an object over time such that, when a series of attributes are input into a prediction component (e.g., a machine learned model), the prediction component can output, for example, predicted locations of the object at times in the future. A vehicle, such as an autonomous vehicle, can be controlled to traverse an environment based on the predicted locations.

A system for reconstructing a vehicular crash (i) receives sensor data of a vehicular crash from at least one mobile device associated with a user; (ii) generates a scenario model of the vehicular crash based upon the received sensor data; (iii) transmits the scenario model to a user computer device associated with the user; (iv) receives a confirmation of the scenario model from the user computer device; (v) stores the scenario model; and (vi) may generate at least one insurance claim form based upon the scenario model. As a result, the speed and accuracy of the claim processing is increased. The system may also utilize vehicle occupant positional data, and internal and external sensor data to detect potential imminent vehicle collisions, take corrective actions, automatically engage autonomous or semi-autonomous vehicle features, and/or generate virtual reconstructions of the vehicle collision.

A method for controlling a hybrid vehicle is disclosed. The method comprises calculating, for a low-emission area on the vehicle's current travel, a first energy to travel the low-emission area in a first driving mode, determining whether the hybrid vehicle is capable of traveling the low-emission area in the first driving mode using current energy of a vehicle battery, and when it is determined that the hybrid vehicle is incapable of traveling the low-emission area in the first driving mode using current energy of the vehicle batter, initiating a battery charging operation before the vehicle enters the emission-restricted area.

Embodiments are disclosed for reducing unauthorized modifications of object detection systems of motor vehicles. As an example, a method comprises: generating a cryptographic signature for an output of nodes of a fully connected layer of a neural network of an object detection system of a vehicle, the cryptographic signature based in part on a first private key stored in a reply-protected memory block (RPMB), the output at least partially describing a detected object; and, responsive to verifying the cryptographic signature, adjusting vehicle operation based on the detected object. In this way, outputs of the neural network may be verified, so that unauthorized modifications are detected, and system accuracy increases.

A vehicle control apparatus includes a host vehicle position recognizer, a moving object recognizer, a map database, an influence recognizer configured to recognize an influence of a moving object around the host vehicle traveling along a target path of the host vehicle, and a vehicle controller configured to decelerate the host vehicle to avoid collision with the moving object based on the influence. Map information includes area information regarding a plurality of areas on the map for specifying the moving object that is a target of recognizing the influence. When the host vehicle position satisfies predetermined host vehicle position conditions set in advance for each area, the influence recognizer recognizes the influence of the moving object present in the area where the host vehicle position satisfies the host vehicle position conditions, based on the host vehicle position, the area information, and the recognition result from the moving object recognizer.

This application relates to the field of intelligent vehicle technologies in the field of artificial intelligence technologies, and in particular, to a controller system. The controller system includes: an intelligent driving domain control unit; a human-machine interaction domain control unit; and a sensor interface unit that is connected to the intelligent driving domain control unit and the human-machine interaction domain control unit. The sensor interface unit is connected to a sensor, and transmit data of the sensor to the intelligent driving domain control unit and the human-machine interaction domain control unit. In this application, external cable connections can be simplified, occupation of data stream transfer resources can be reduced, and power consumption of a vehicle can be reduced.

Provided is a vehicle control device which, when traffic participants are waiting in the vicinity of a railroad crossing for a train to pass, appropriately controls driving of a vehicle about to pass through the railroad crossing. Entry of the vehicle into the railroad crossing is restrained until a waiting time elapses since when the railroad crossing transitioned from a passage blocking state to a passage allowing state, the waiting time being set in accordance with the kind or the number of the traffic participants present in the vicinity of the railroad crossing. When the waiting time has elapsed, the vehicle is caused to enter the railroad crossing and pass (through) the railroad crossing.

The present disclosure relates to a device for controlling a vehicle at an intersection. The device variably sets a region of interest of a vehicle sensor in accordance with a relationship between the progress paths of a subject vehicle and a target vehicle at an intersection, or controls the subject vehicle according to a collision avoidance control method in comparison with a predetermined scenario of a collision possibility of the subject vehicle and the target vehicle at the intersection according to a scenario, thereby preventing a collision at the intersection.

The present disclosure relates to a driving control apparatus, a driving control method, and a program that can resolve a conflict between a deliberate action and a reflex action during determination of a next action in autonomous driving. During autonomous driving, a reflex action is determined as a simplified action on the basis of detection results detected by a variety of sensors provided in a vehicle, and a deliberate action ranked higher than a reflex action is determined through elaborate processing. A plurality of resolution modes are made available to deal with a possible conflict between the reflex action and the deliberate action, and by which of the resolution modes the conflict is resolved is specified in advance so that the conflict is resolved by the specified resolution mode. The present disclosure is applicable to motor vehicles that drive autonomously.

Systems and methods are provided for navigating an autonomous vehicle using reinforcement learning techniques. In one implementation, a navigation system for a host vehicle may include 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 a navigational state associated with the host vehicle, provide the navigational state to a trained navigational system, receive, from the trained navigational system, a desired navigational action for execution by the host vehicle in response to the identified navigational state, analyze the desired navigational action relative to one or more predefined navigational constraints, determine an actual navigational action for the host vehicle, wherein the actual navigational action includes at least one modification of the desired navigational action determined based on the one or more predefined navigational constraints, and cause at least one adjustment of a navigational actuator of the host vehicle in response to the determined actual navigational action for the host vehicle.