Aspects of the disclosure relate generally to detecting discrete actions by traveling vehicles. The features described improve the safety, use, driver experience, and performance of autonomously controlled vehicles by performing a behavior analysis on mobile objects in the vicinity of an autonomous vehicle. Specifically, an autonomous vehicle is capable of detecting and tracking nearby vehicles and is able to determine when these nearby vehicles have performed actions of interest by comparing their tracked movements with map data.

The present disclosure is directed to apparatuses, systems and methods for automatically classifying images of occupants inside a vehicle. More particularly, the present disclosure is directed to apparatuses, systems and methods for automatically classifying images of occupants inside a vehicle by comparing current image feature data to previously classified image features.

The present disclosure is directed to apparatuses, systems and methods for automatically classifying digital images of occupants inside a vehicle. More particularly, the present disclosure is directed to apparatuses, systems and methods for automatically classifying digital images of occupants inside a vehicle by comparing current image data to previously classified image data.

Methods and systems for determining fault for an accident involving a vehicle having one or more autonomous (and/or semi-autonomous) operation features and paying claims associated with such accidents are provided. According to certain aspects, operating data from sensors within or near the vehicle may be used to determine the occurrence of a vehicle accident, such as a collision. The operating data may further be used to determine an allocation of fault for the accident between a vehicle operator, the autonomous operation features, or a third party. The allocation of fault may be used to further determine and make claims payments related to the accident. In some embodiments, claims may be rejected based upon the operating data and determined allocation of fault.

This application relates to the field of self-driving car technologies. In a vehicle control method, first control behavior information is obtained, by processing circuitry of a vehicle, via a vehicle-mounted sensor system in a case that the vehicle is in an autonomous control mode. The first control behavior information is generated from a first user action performed on the vehicle. Whether the first control behavior information corresponds to a predetermined type of control behavior information is determined by the processing circuitry. The predetermined type of control behavior information corresponds to a user action type that is triggered by a reflex of a user. A switch, by the processing circuitry, is performed from the autonomous control mode to a manual control mode in a case that the first control behavior information is not determined as the predetermined type of control behavior information.

Among other things, a vehicle having autonomous driving capabilities is operated in a mixed driving mode.

In various embodiments, an apparatus includes a system-on-chip (SoC) to be disposed in a vehicle having a plurality of cores; a hypervisor arranged to partition the cores into at least two domains, an operational domain and a failover domain; a first operating system (OS) arranged to manage execution of at least a first application in the operational domain to provide a first plurality of functions for the vehicle; a second OS arranged to manage execution of at least a second application in the failover domain to provide a second plurality of functions for the vehicle, on occurrence of a failure of the first application. The second functions comprise a subset of the first functions or less embellished versions of some of the first functions, and the second OS has less capabilities than the first OS. Other embodiments, including storage media and methods, are also described and claimed.

A method for operating a vehicle. When an autonomous cruise control is in the activated state, a switch is made to an accelerator-pedal-controlled distance controller in response to an acceleration command indicated by an override of an accelerator pedal of the vehicle.

An automated driving control unit sets an automated driving state to a first automated driving state (step S3), in a case that an automated driving instruction unit has instructed the initiation of automated driving in a state in which a destination is set by a destination setting unit, sets the automated driving state to a second automated driving state (step S4), in a case that the automated driving instruction unit has instructed the initiation of automated driving in a state in which the destination is not set by the destination setting unit, and causes the automated driving state to transition from the first automated driving state to the second automated driving state (step S13), in a case that a current travel position lies outside of a travel route during the travel control in the first automated driving state.

A computer-implemented method, system, and/or computer program product controls a driving mode of a self-driving vehicle (SDV). One or more processors compare a control processor competence level of an on-board SDV control processor in controlling the SDV to a human driver competence level of a human driver in controlling the SDV while the SDV encounters a current roadway condition which is a result of current weather conditions of the roadway on which the SDV is currently traveling. One or more processors then selectively assign control of the SDV to the SDV control processor or to the human driver while the SDV encounters the current roadway condition based on which of the control processor competence level and the human driver competence level is relatively higher to one another.