Nio Usa, Inc.

Netflix Inc.

Nio Technology (anhui) Co., Ltd.

Christopher Francis Pouliot

Abhishek Singhal

Edward H Baik

Fei Xiao

Gautam Muralidhar

Jonathan A Cox

Guan Wang

Anthony Tao Liang

Carlos Alberto Gomez Uribe

Christopher Li

Nilesh Vijay Kulkarni

Lisa E Falkson

Satish Kumar Krishnamurthy

Zhou Xing

Marzieh Parandehgheibi

Eric Lucas

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 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.

Vehicle autonomous collision prediction and escaping system (ACE)

A network crawler crawls one or more media sites to extract a plurality of titles for information contained in the one or more media sites. For example, the network crawler may extract the titles on the one or more media sites by identifying different computer formats and converting the titles in the different computer formats into a common computer format for comparison. User profiles which, includes user's interest are stored in a user profile interest database. A novel personalized news recommendation engine recommends news similar to the interest specified in a user profile, where maximum weighted matching is applied to score the similarity of news against user interest in the word2vec space.

Personalized news recommendation engine

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.

Method and system for data optimization

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

One embodiment of the invention sets forth a mechanism for identifying similar items within a plurality of items available to a group of users. The mechanism includes identifying a first count that indicates a number of times any user has interacted with a first item included in the plurality of items and subsequently with any other item, identifying a second count that indicates a number of times any user within the has interacted with a second item included in the plurality of items after interacting with the first item, computing a probability that indicates the likelihood of a particular user interacting with the second item after interacting with the first item based on the first count and the second count, and computing a similarity score that indicates the similarity between the first item and the second item based on the probability and a popularity score associated with the second item.

Identifying similar items based on interaction history

An automated system for controlling a vehicle gathers a set of sensor information for a series of driving actions that comprises a driving path of an individual driver. A context is determined that is related to the set of sensor information for the series of driving actions that comprise the driving path of the individual driver. For example, the context may be a weather condition or when the driver merges into traffic. A personalization score is determined for the series of driving actions for the individual driver based on the context. A personalization score is determined for one or more other driving paths and a driving path with the highest personalization score is identified. The driving path with the highest personalization score is then chosen for automated driving.

Growing community of inventors

San Mateo, CA, United States of America

Christopher Francis Pouliot