Nio Usa, Inc.

Nio Technology (anhui) Co., Ltd.

Gautam Muralidhar

Abhishek Singhal

Christopher Francis Pouliot

Edward H Baik

Jonathan A Cox

Anthony Tao Liang

Nadav Aharony

James S Higgins

Rachad Youssef Gamarra

Infantdani Abel Rayan

Joshua T Butler

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.

Autonomy first route optimization for autonomous vehicles

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.

Vehicle autonomy level selection based on user context

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.

Controlling access to a vehicle using wireless access devices

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.

Adaptive route and motion planning based on learned external and internal vehicle environment

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San Jose, CA, United States of America

Gautam Muralidhar