Methods and systems for monitoring use, determining risk, and pricing insurance policies for a vehicle having one or more autonomous or semi-autonomous operation features are provided. According to certain aspects, the operating status and/or configuration of autonomous operation features of an autonomous or semi-autonomous vehicle may be determined, such as via an on-board computer system or mobile device, and/or then directly or indirectly wirelessly communicated via data transmission from the vehicle computer system or mobile device to a remote server. An adjustment to one or more risk levels associated with operation of the autonomous or semi-autonomous vehicle may also be determined, and an auto insurance policy, premium, or discount may be adjusted based upon the adjustment to the risk levels and presented to the customer for their review and approval. As a result, insurance cost savings may be passed onto risk averse customers that opt into to a rewards program.

An operation appropriateness determination system includes an authentication apparatus that identifies an operator on a basis of a feature of the operator and that outputs information for identifying the operator, a biometric sensing apparatus that obtains biological information regarding the operator and that outputs the biological information, an operation sensing apparatus that detects a load of an operation that is being performed by the operator and that outputs operation information indicating the load of the operation, a storage device, and a signal processing apparatus.

Aspects of the disclosure relate to determining and responding to an internal state of a self-driving vehicle. For instance, an image of an interior of the vehicle captured by a camera mounted in the vehicle is received. The image is processed in order to identify one or more visible markers at predetermined locations within the vehicle. The internal state of the vehicle is determined based on the identified one or more visible markers. A responsive action is identified action using the determined internal state, and the vehicle is controlled in order to perform the responsive action.

A backend for a hazard detection system comprising: a processor; and a memory in communication with the processor, the memory storing a set of instructions. The set of instructions, when accessed and executed by the processor, cause the processor to: receive vehicle and/or driver data from a vehicle, evaluate the received vehicle and/or driver data, use the evaluation as a basis for detecting a hazard in road traffic, and send the information about the detected hazard to at least one vehicle to warn said vehicle about the hazard.

A computer includes a processor and a memory storing instructions executable by the processor to receive an input by a first user setting a maximum speed for a vehicle; prevent a second user from operating the vehicle above the maximum speed; permit the second user to operate the vehicle above the maximum speed upon at least one condition being met; in response to permitting the second user to operate the vehicle above the maximum speed, transmit a first collection of vehicle data to the first user; and in response to the second user failing to stop the vehicle at a stop indicator, transmit a second collection of the vehicle data to the first user.

An autonomous vehicle having a user interface and a computing system that is in communication with the user interface. The computing system may have at least one processor and at least one memory that stores computer-executable instructions. When executed by the at least one processor, the instructions may cause the at least one processor to output information through the user interface to inform the passenger of an action that the passenger needs to enact prior to the autonomous vehicle beginning to move and determine, based upon an occurrence of the action that the passenger needs to enact, whether the autonomous vehicle is permitted to begin moving.

An apparatus including a capture device and a processor. The capture device may be configured to generate a plurality of video frames corresponding to users of a vehicle. The processor may be configured to perform operations to detect objects in the video frames, detect users of the vehicle based on the objects detected in the video frames, determine a limitation profile for the users, monitor for conditions provided by the limitation profile and generate a reaction if one or more of the conditions are met. The limitation profile may be determined in response to characteristics of the users. The characteristics of the users may be determined by performing the operations on each of the users.

A method for robust identification of a vehicle occupant. Simultaneously acquired data pairs from interior sensors and sensing elements are used as a training data set for global learning of a Deep Canonical Correlation Analysis architecture with two neural networks, wherein an authentication of the respective vehicle occupants from the group of vehicle users is carried out in a first step as an initialization. In a second step or a subsequent use, only one of the two sensing elements is then needed to identify the respective vehicle occupant. Also described is a system, via which the robust identification is provided by way of the method.

A method and apparatus for providing a human-machine interface (HMI) mode of a vehicle are provided. The method, performed by the device of the vehicle, for providing a human-machine interface (HMI) mode includes, analyzing a state of an occupant, calculating a confidence score for the vehicle based on the state of the occupant, determining an HMI mode corresponding to the confidence score among a plurality of predefined HMI modes; and providing first guidance information to the occupant based on the determined HMI mode.

In various embodiments, a driver sensing subsystem computes a characterization of a driver based on physiological attribute(s) of the driver that are measured as the driver operates a vehicle. Subsequently, a driver assessment application uses a confidence level model to estimate a confidence level of the driver based on the characterization of the driver. The driver assessment application then causes driver assistance application(s) to modify at least one functionality of the vehicle based on the confidence level. Advantageously, by enabling the driver assistance application(s) to take into account the confidence level of the driver, the driver assessment application can improve driving safety relative to conventional techniques for implementing driver assistance applications that disregard the confidence levels of drivers.