A vehicle control device includes a crossing vehicle detection sensor configured to detect a crossing vehicle approaching an own vehicle while the own vehicle is traveling in an intersecting lane, the intersecting lane being a lane that intersects an own vehicle lane at an intersection at a time the own vehicle approaches the intersection, the crossing vehicle being a vehicle travelling in the intersecting lane; and a controller configured to automatically brake the own vehicle to avoid a collision between the own vehicle and the crossing vehicle under a condition that the own vehicle enters the intersecting lane. The controller is configured to set, between the own vehicle and the crossing vehicle, a virtual area that moves with the crossing vehicle and that extends in an advancing direction of the crossing vehicle, and automatically brake the own vehicle to prevent the own vehicle from contacting the virtual area.

A prediction apparatus, comprising an acquisition unit configured to acquire peripheral information of an own vehicle, a determination unit configured to determine, based on the peripheral information, whether a behavior prediction target on a road visually recognizes a predetermined object, and a prediction unit configured to, if it is determined by the determination unit that the behavior prediction target visually recognizes the predetermined object, predict that the behavior prediction target moves across the road in a crossing direction.

Methods and systems for assessing, detecting, and responding to malfunctions involving components of autonomous vehicles and/or smart homes are described herein. Malfunctions may be detected by receiving sensor data from a plurality of sensors. One of these sensors may be selected for assessment. An electronic device may obtain from the selected sensor a set of signals. When the set of signals includes signals that are outside of a determined range of signals associated with proper functioning for the selected sensor, it may be determined that the selected sensor is malfunctioning. In response, an action may be performed to resolve the malfunction and/or mitigate consequences of the malfunction.

Various technologies described herein pertain to labeling sensor data generated by autonomous vehicles. A computing device identifies candidate path plans for an object in a driving environment of an autonomous vehicle based upon sensor data generated by sensor systems of the autonomous vehicle. The sensor data is indicative of positions of the object in the driving environment at sequential timesteps in a time period. Each candidate path plan is indicative of a possible maneuver being executed by the object during the time period. The computing device generates a weighted directed graph based upon the candidate path plans. The computing device determines a shortest path through the weighted directed graph. The computing device assigns a maneuver label to the sensor data based upon the shortest path, wherein the maneuver label is indicative of a maneuver that the object executes during the time period.

Various techniques related to a sensor for use with autonomous driving and/or navigation of a vehicle. The techniques can include use of three members, one of the members housing a sensor for use with autonomous driving and/or navigation of the vehicle. The members can define cavities housing others of the three members.

Methods, devices and apparatuses pertaining to U-turn assistance. The method may include obtaining, by a computing device, geographic obtaining of a location designated for an operation of a U-turn. The computing device may further obtain vehicle information of a vehicle performing the U-turn, and collect user information of an operator of the vehicle. Based on the geographic information, the vehicle information and the user information, the computing device may determine a level of difficulty of the U-turn and assist the operator with the operation of the U-turn based on the level of difficulty.

A vehicle control device includes a communication unit configured to receive location information of a vehicle, a sensing unit, a display unit, and at least one processor that is configured to determine, based on the location information received by the communication unit, that the vehicle has entered an area within a predetermined distance from an intersection at which the vehicle changes a travel direction according to a preset route information. The at least one processor is further configured to detect an object located around the intersection through the sensing unit, and to control the display unit to output a route to an entrance of the intersection based on information related to the object located around the intersection.

Apparatuses, systems, and methods are provided for the utilization of vehicle control systems to cause a vehicle to take preventative action responsive to the detection of a near short term adverse driving scenario. A vehicle control system may receive information corresponding to vehicle operation data and ancillary data. Based on the received vehicle operation data and the received ancillary data, a multi-dimension risk score module may calculate risk scores associated with the received vehicle operation data and the received ancillary data. Subsequently, the vehicle control systems may cause the vehicle to perform at least one of a close call detection action and a close call detection alert to lessen the risk associated with the received vehicle operation data and the received ancillary data.

A vehicle control device includes: an automatic driving control unit performing automatic driving control of automatically controlling one or both of acceleration/deceleration and steering of a subject vehicle such that the subject vehicle runs along a route to a destination; an avoidance target object detecting unit detecting an avoidance target object that is a target object to be avoided in the vicinity of the subject vehicle; and an avoidance control unit performing an avoidance operation by executing avoidance control of automatically controlling one or both of the acceleration/deceleration and the steering of the subject vehicle with a priority with respect to the automatic driving control in a case in which approach of the subject vehicle to the detected avoidance target object within a predetermined range is detected, and the automatic driving control unit ends the automatic driving control in a case in which the avoidance operation is executed.

A set of driving scenarios are determined for different types of vehicles. Each driving scenario corresponds to a specific movement of a particular type of autonomous vehicles. For each of the driving scenarios of each type of autonomous vehicles, a set of driving statistics is obtained, including driving parameters used to control and drive the vehicle, a driving condition at the point in time, and a sideslip caused by the driving parameters and the driving condition under the driving scenario. A driving scenario/sideslip mapping table or database is constructed. The scenario/sideslip mapping table includes a number of mapping entries. Each mapping entry maps a particular driving scenario to a sideslip that is calculated based on the driving statistics. The scenario/sideslip mapping table is utilized subsequently to predict the sideslip under the similar driving environment, such that the driving planning and control can be compensated.