Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Embodiments are directed to employing various methods and devices to access a vehicle and controlling a level of access granted based on the device and methods employed. Controlling access to a vehicle can comprise maintaining a plurality of security profiles, each security profile associated with a different access device or type of access device. When the presence of an access device in proximity to the vehicle or a request to access the vehicle is detected, a security profile associated with the detected access device or the request to access the vehicle can be retrieved and used to determine whether to grant access to the vehicle. In response to determining to grant access to the vehicle, access to the vehicle can be provided according to an access level for the detected access device or the request to access the vehicle identified in the retrieved security profile.

Abstract: Systems of an autonomous vehicle and the operations thereof are provided. The vehicle can predict a user's trip using his or her history of past trips, the relevant context, and the current environmental conditions. The navigation system of the vehicle can use historical GPS data, defined by the sequence of position measurements, of a given vehicle to determine frequently visited locations. Then, the navigation system may augment the learned knowledge of frequently visited locations and routes with the relevant context and environmental conditions to make recommendations on the locations the user is most likely to visit and the route most likely to drive on. More specifically, the navigation system can automatically cluster historical trips by similarity (e.g., trips with similar contexts and/or environmental conditions), while identifying, mitigating, and/or eliminating the potential for statistical outliers (trips taken by a driver that are infrequent and thus less useful for predicting future trips).

Abstract: Systems of an electrical vehicle and the operations thereof are provided. Electric vehicles may be routed from a start location to a destination through a network of charging stations explicitly considering time-varying uncertainty in both charging times, queueing times, and range. The routing objective may be a function of trip duration, electric vehicle state of charge at any time or location along the trip, uncertainty in the vehicle state of charge, the estimated trip duration, etc. Uncertainty in a distribution may be computed using an information-theoretic metric such as entropy. Waiting times may be estimated at an electric vehicle charging station given observed data for that station. An estimated waiting time at a charging station can be communicated directly to the owner of an electric vehicle owner or used to design a robust system for routing through charging stations.

Abstract: The systems and methods described herein can be applied to a vehicle equipped with a sensor suite that can observe information about a location, for example, a traffic light duration. The system can record information, e.g., the traffic light colors, duration of color changes, and location of the traffic lights and can upload this information to the cloud. Then, the system can augment or learn about the location, e.g., learning of traffic patterns, and store the augmented data as database-based information, where available. The learned information can help a requesting vehicle to better estimate an estimated time of arrival (ETA) for common routes taken, provide more accurate ETAs based on historical knowledge, and/or calculate or provide alternative route information.

Abstract: The systems and methods described herein can be applied to a vehicle equipped with a sensor suite that can observe information about a location, for example, a traffic light duration. The system can record information, e.g., the traffic light colors, duration of color changes, and location of the traffic lights and can upload this information to the cloud. Then, the system can augment or learn about the location, e.g., learning of traffic patterns, and store the augmented data as database-based information, where available. The learned information can help a requesting vehicle to better estimate an estimated time of arrival (ETA) for common routes taken, provide more accurate ETAs based on historical knowledge, and/or calculate or provide alternative route information.

Abstract: 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.

Abstract: 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.