An information processing apparatus includes a processor. The processor is configured to estimate whether a fellow passenger is included in occupants of a vehicle, when the fellow passenger is included in the occupants, acquire information relating to a driver of the vehicle and information relating to the fellow passenger, estimate a relationship between the driver and the fellow passenger based on the acquired information, and select a control relating to traveling of the vehicle according to the relationship.

Methods and systems are provided for preparing an energy receiving apparatus of a vehicle for receiving an increase in a level of energy storage prior to a vehicle reaching an energy replenishment station for receiving the increase. In one example, a method comprises via a controller, requesting a confirmation as to whether a vehicle operator intends to stop at a particular energy replenishment station, and when confirmation is received, commanding one or more actions to prepare the energy receiving apparatus for accepting the energy level increase. In this way, a time-frame for accepting the energy level increase may be reduced as compared to situations where the one or more actions are not undertaken.

A method includes: determining, by a computer device, conditions along a predefined driving route of a vehicle; determining, by the computer device and based on the conditions, risk factors of segments of the driving route; determining, by the computer device, an optimal one of the segments for switching the vehicle from autonomous driving mode to manual driving mode; and outputting, by the computer device, data defining the determined optimal one of the segments.

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, with the customer's permission, a vehicle operator in an autonomous or semi-autonomous vehicle may be determined by a sensor disposed within the autonomous or semi-autonomous vehicle or a mobile computing device associated with the vehicle operator. A determination may be made as to whether the vehicle operator is authorized to operate the vehicle based upon the determined identity of the vehicle operator; and if the vehicle operator is not authorized, an alert may be generated and/or the vehicle may be caused to not operate. Alternatively, autonomous operation features may be automatically engaged to automatically drive the autonomous or semi-autonomous vehicle to a safe location when the operator is not authorized. Insurance discounts may be provided based upon the anti-theft functionality.

An autonomous driving system mounted on a vehicle includes: a vehicle behavior control device configured to perform vehicle behavior control that controls behavior of the vehicle; and an awareness level calculation device configured to calculate an awareness level of a driver of the vehicle. A low awareness state is a state where the awareness level is lower than a normal state. The vehicle behavior control device performs the vehicle behavior control such that another vehicle around the vehicle predicts or recognizes the behavior of the vehicle more easily in a case of the low awareness state than in a case of the normal state.

A driver assist analysis system includes a processing system and a database that stores vehicle data, vehicle operational data, vehicle accident data, and environmental data related to the configuration and operation of a plurality of vehicles with driver assist systems or features. The driver assist analysis system also includes one or more analysis engines that execute on the processing system to determine one or more driving anomalies (e.g., accidents or poor driving operation) based on the vehicle operational data, and that correlate or determine a relationship between the driving anomalies and the operation of the driver assist systems or features. The driver assist analysis system then determines an effectiveness of operation of one or more of the driver assist systems or features as a statistical measure based on the determined relationship and the driver assist analysis system notifies a user or receiver, such as a manufacturer or an insurance company of the statistical measure.

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, a computer-implemented method for responding to damage to an autonomous or semi-autonomous vehicle relating to a collision or other loss may be provided. With the customer's permission, an indication of damage to the autonomous or semi-autonomous vehicle, and/or operating data regarding operation of the vehicle during (i) a time period without the damage to the autonomous or semi-autonomous vehicle, and (ii) a time period with the damage to the autonomous or semi-autonomous vehicle may be received. Repairs associated with the damage may be determined based upon the operating data, as well as an expected cost associated with the repairs. Insurance discounts may be provided to risk averse customers that timely repair damaged vehicles or features that impact vehicle safety.

A system includes an acquisition unit that acquires an operation amount or a duration count, and a switching unit that switches a driving state. The switching unit switches the driving state to the cooperative driving state when the operation amount is equal to or greater than an intervention threshold and less than a start threshold or the duration count is equal to or greater than a first threshold and less than a second threshold during the autonomous driving state, switches the driving state to the autonomous driving state when the operation amount is less than the intervention threshold or the duration count is less than the first threshold during the cooperative driving state, and switches the driving state to the manual driving state when the operation amount is equal to or greater than the start threshold or the duration count is equal to or greater than the second threshold.

Embodiments herein can determine an optimal route for an autonomous electric vehicle. The system may score viable routes between the start and end locations of a trip using a numeric or other scale that denotes how viable the route is for autonomy. The score is adjusted using a variety of factors where a learning process leverages both offline and online data. The scored routes are not based simply on the shortest distance between the start and end points but determine the best route based on the driving context for the vehicle and the user.

Various technologies described herein pertain to causing presentation on a user interface of an immediate portion of a navigation route of an autonomous vehicle. A computing system of the autonomous vehicle determines whether an object detected by sensor(s) of the autonomous vehicle proximate to the immediate portion of the navigation route are of a type and relative position defined as one of consequential and inconsequential for a human passenger. In response to determining that an object has both a type and relative position defined as consequential, the computing system causes presentation on the user interface a representation of the object relative to the immediate portion of the navigation route to provide a confidence engendering indication that the autonomous vehicle has detected the object. Otherwise if inconsequential, presentation on the user interface of any representation of the object is not caused by the computing system to avoid creating a confusing presentation.