Systems and methods are provided for predicting blind spot incursions for a host vehicle. In one implementation, a navigation system for a host vehicle may comprise a processor. The processor may be programmed to receive, from an image capture device located on a rear of the host vehicle, at least one image representative of an environment of the host vehicle. The processor may be programmed to analyze the at least one image to identify an object in the environment of the host vehicle and to determine kinematic information associated with the object. The processor may further be programmed to predict, based on the kinematic information, that the object will travel in a region outside of a field of view of the image capture device and perform a control action based on the prediction.

A system for an autonomous vehicle that predicts a location-based maneuver of a remote vehicle located in a surrounding environment includes one or more vehicle sensors collecting sensory data indicative of one or more vehicles located in the surrounding environment. The system also includes one or more automated driving controllers in electronic communication with the one or more vehicle sensors. The one or more automated driving controllers execute instructions to compare a lane of travel of the remote vehicle with a current lane of travel of the autonomous vehicle. In response to determining the lane of travel of the remote vehicle is a different lane than the current lane of the autonomous vehicle, the one or more automated driving controllers predict the location-based maneuver of the remote vehicle based on aggregated vehicle metrics that are based on historical data collected at the specific geographical location.

Disclosed herein is a driver assistance apparatus including a camera which is installed in a vehicle, has a field of view around the vehicle, and is configure to acquire image data, and a controller including a processor configured to process the image data. The controller is configured to identify a gesture corresponding to a predesignated reference gesture based on the image data, and change a gear state of the vehicle and control a driving device of the vehicle to move the vehicle, based on identifying the gesture.

A vehicle driving assist device includes an oncoming moving body recognizer configured to recognize an oncoming moving body; a lateral position distribution characteristics acquisition unit configured to acquire distribution characteristics of a lateral position of the oncoming moving body; a risk determination region setting unit configured to set, based on the distribution characteristics, a risk determination region for calculating a risk degree; a risk degree calculator configured to calculate the risk degree for the oncoming moving body; and a preliminary collision avoidance controller configured to recognize the oncoming moving body as the obstacle in accordance with the risk degree, and perform preliminary collision avoidance control in response to the oncoming moving body prior to the emergency collision avoidance control. The risk determination region setting unit is further configured to variably set the risk determination region so that the risk degree relatively increases as the distribution characteristics tend to disperse.

A vehicle driving assist device includes a processor that functions as a receiver, an emergency collision avoidance controller, and a preliminary collision avoidance controller. The receiver receives, in relation to an oncoming moving body moving in an oncoming lane adjacent to a traveling lane of a vehicle and having a velocity component in a direction opposite to a traveling direction of the vehicle, a risk degree calculated based on a history of a distance from a lane marker defining the oncoming lane to the oncoming moving body, by communication with a device outside the vehicle. Upon determination that the vehicle is highly likely to collide with an obstacle, the emergency collision avoidance controller performs emergency collision avoidance control. The preliminary collision avoidance controller recognizes the oncoming moving body as the obstacle in accordance with the risk degree, and performs preliminary control prior to the emergency collision avoidance control.

Methods for reversing determination for a vehicle asset are provided. The methods include capturing by a telematics device coupled to the vehicle acceleration data from a three-axis accelerometer, determining by a reversing-determination machine learning mode, a machine-learning-determined reversing indication for the vehicle asset. The reversing-determination machine-learning model being trained by a vehicle reversing indication comprising a vehicle speed and a reverse gear indication.

This application relates to the automated driving field, and discloses a method for identifying abnormal driving behavior, a system, and a vehicle including the system. The method for identifying abnormal driving behavior includes: obtaining vehicle driving behavior data, and determining, based on the vehicle driving behavior data, whether a vehicle is in a state of suspicious abnormal driving behavior; obtaining current vehicle driving scenario data if the vehicle is in the state of suspicious abnormal driving behavior; and determining, based on the vehicle driving behavior data and the current vehicle driving scenario data, whether the suspicious abnormal driving behavior is abnormal driving behavior. In the technical solutions of this application, current driving scenario information is introduced to an identification process of abnormal driving behavior of the vehicle, so that accuracy of identifying the abnormal driving behavior is improved.

An information processing apparatus includes: a point group data acquisition unit configured to acquire, based on information from a sensor configured to detect an object existing in surroundings of a vehicle, point group data related to a plurality of points representing the object; a movement amount estimation unit configured to estimate a movement amount of the vehicle; a storage unit configured to store, as a point group map recorded in association with position information including a latitude and a longitude, relative positions of the plurality of points relative to a first reference position that is a place on a travel path of the vehicle; and a position estimation unit configured to estimate a position of the vehicle based on the point group map, the point group data, and the movement amount.

Aspects of the disclosure relate to methods for controlling a vehicle having an autonomous driving mode. For instance, sensor data may be received from one or more sensors of the perception system of the vehicle, the sensor data identifying characteristics of an object perceived by the perception system. When it is determined that the object is no longer being perceived by the one or more sensors of the perception system, predicted characteristics for the object may be generated based on one or more of the identified characteristics. The predicted characteristics of the object may be used to control the vehicle in the autonomous driving mode such that the vehicle is able to respond to the object when it is determined that the object is no longer being perceived by the one or more sensors of the perception system.

In one embodiment, a plurality of obstacles is sensed in an environment of an automated driving vehicle (ADV). One or more representations are formed to represent corresponding groupings of the plurality of obstacles. A vehicle route is determined in view of the one or more representations, rather than each and every one of the obstacles individually.