Baidu USA LLC

Jing Zhai

Quan Wang

Weide Zhang

Weicheng Zhu

Wei Fan

Nan Du

Yusheng Xie

Richard Chun Ching Wang

Dawen Zhou

Richard Chun-Ching Wang

Location of an autonomous driving vehicle (ADV) is determined with respect to a high definition map. On-boards sensors of the ADV obtain a 3D point cloud of objects surrounding the ADV. The 3D point cloud is organized into an ADV feature space of cells. Each cell has a median intensity value and a variance in elevation. To determine the ADV location, a coarse search of a subset of cells in the ADV feature space performed with respect to the high definition map, using a similarity metric that is based on the median intensity and variance in elevation of the candidate cell. When similarity of the first candidate cell is determined, a lookup table of similarity scores is generated and used for determining the similarity score for subsequent candidate cells. Then a fine search is performed on a small subset of candidate cells surrounding the highest similarity score cell.

Algorithm and infrastructure for robust and efficient vehicle localization

Described is a system that matches a query with a set of sentences. When a query is received, the system may extract features from the query including one or more words as tokens and retrieve a set of candidate sentences as potential results for the query. For example, the query may be in the form of a question, and the candidate sentences may be alternative phrasings of the query that potentially match the user's search intent. The relevant set of candidate sentences may be determined based on using multiple relevancy scores and the system may rank the candidates according to an overall or aggregate score. Accordingly, the set of results to a query may be provided by recognizing the sentence as a whole and, for example when the query is a question, the interrogative intent of the query.

Matching a query to a set of sentences using a multidimensional relevancy determination

Systems and methods are disclosed for question generation to infer the most probable cause from the observable outcome and known Noisy-OR causal relations. In embodiments, the outcomes are sorted by indices according to an order including but not limited to the outcomes' natural frequency order, expert-labeled order, machine-learning derived order, etc. According to their assigned indices, in embodiments, observed outcomes with lower indices are assigned for exact inference while observed outcomes with higher indices are assigned for variational inference. In embodiments, results of exact inference and variational inference are combined to predict the most probable cause. The unique combination of exact inference and variational inference according to outcome indices makes the probable cause inferring process faster.

Systems and methods of determining sufficient causes from multiple outcomes

Systems and methods are disclosed for extending and reinforcing a knowledge graph using wiki-like web pages as a source of information. A web crawler parse a wiki-like source and obtain a topic entity from the source. Relationships between the topic entity and sub-topics within the source are identified and a graph is built with the topic and relationships to sub-topics. A candidate topic in the knowledge graph is identified, and a sub-graph of the knowledge graph is either identified or generated. The knowledge sub-graph contains the candidate topic and relationships to sub-topics. A similarity is computed between the source graph and the knowledge sub-graph. If the two graphs are sufficiently similar, then the source topic graph is merged with the knowledge graph.

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Sunnyvale, CA, United States of America

Jing Zhai