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.

Embodiments can provide an intelligent vehicle that determines that the vehicle interior comprises multiple occupants; identifies the occupants; based on the driving behavior of the vehicle, creates a composite occupant profile associated with the group of plural occupants, the composite occupant profile comprising the identities of the plural occupants and group preferences for various vendor products or services; and, based on the composite occupant profile and received inputs from a user interface, an automatic vehicle location system, and a plurality of sensors in the vehicle, performs one or more actions, such as: (a) proposing one or more vendor products or services for the group of occupants; (b) publishing the vendor products or services selected by the group of occupants, via a social network, to associated or selected associates of the occupants in the group; and (c) presenting advertisement information from a vendor server associated with the proposed or selected vendor products or services to one or more of the occupants in the group.

Embodiments herein relate to an autonomous vehicle or self-driving vehicle. The system can determine a collision avoidance path by: 1) predicting the behavior/trajectory of other moving objects (and identifying stationary objects); 2) given the driving trajectory (issued by autonomous driving system) or predicted driving trajectory (human), establishing the probability for a collision that can be calculated between the vehicle and one or more objects; and 3) finding a path to minimize the collision probability.

Embodiments herein relate to an autonomous vehicle or self-driving vehicle with a vehicle control system. The vehicle control system can determine, prior to and/or during a collision, whether an escape path exits. If an escape path exists, the brakes are disengaged such that at least some of the energy and/or momentum from a colliding vehicle is transferred and a jolt or shock experienced by an occupant is reduced.

Systems of an electrical vehicle and the operations thereof are provided that use object profiles to select autonomous vehicle operations, including acceleration rate, deceleration rate, steering angle, and inter-vehicle spacing.

Embodiments herein relate to an autonomous vehicle or self-driving vehicle. The level of comfort with autonomous driving offered to the user and the parameters of operation of the autonomous vehicle might increase or decrease a driver's comfort (anxiety) with the vehicle. Sensors in the vehicle can detect a user's driving style, comfort level and may propose an autonomy level to the user and/or parameters of operation.

Systems of an electrical vehicle and the operations thereof are provided.

A vehicle drive system includes a slip acquisition unit that acquires that an excessive slip of front wheels or rear wheels has occurred, an addition slip point calculating unit that calculates addition slip points in a time-discrete manner, based on the slip acquisition unit having acquired that the excessive slip has occurred, a cumulative slip point calculating unit that accumulates the addition slip points and calculates a cumulative slip point over time, a drive state switching unit that, switches between 2WD and AWD based on cumulative slip points, and an increase forbidding determination unit that forbids addition or accumulation of the addition slip points, or increase of the cumulative slip points, in a case where a lateral acceleration correlation value that has correlation with lateral acceleration of the vehicle exceeds a lateral acceleration threshold value.

A method is provided for selecting a plurality of program functions for providing repeatedly implemented functions, e.g., in a vehicle, ship or in an aircraft. The method includes determining a first total performance value based on recorded first single performance values and recorded first dependencies, determining a first total performance value based on determined second single performance values and recorded second dependencies, determining a cluster performance from the first total performance value and from the second total performance value, and the cluster performance value or at least one value determined from the cluster performance value is used for selecting the program functions or of other program functions for providing the repeatedly implemented functions.

Methods and apparatus to extricate a vehicle from a stuck condition, apparatus, systems and articles of manufacture are disclosed. An example method includes receiving a command to place a vehicle in a stuck mode. The stuck mode corresponds to an autonomous control mode of the vehicle. The method further includes autonomously controlling the vehicle in the stuck mode to attempt to extricate the vehicle from a stuck condition.