Honda Motor Co., Ltd.

Other

Rakesh Gupta

Deepak Ramachandran

Antoine Roland Raux

Ken Hennacy

Teruhisa Misu

Ming-Hsuan Yang

Ananth Ranganathan

Jongwoo Lim

Ian R Lane

Jason Meltzer

Quang Do

Bo Tang

Stefan Krawczyk

Robert Cofield

Neville Mehta

Lev Ratinov

Aasish Pappu

Vasco Calais Pedro

Matthew Hoffman

Salman Anwar Khokhar

Igor V Karpov

Samarth Swarup

Trupti Devdas Nayak

Chirag Shah

Lev-Arie Ratinov

Mykel J Kochenderfer

Yi Ma

Jie Gao

Adam C Vogel

A trajectory planning system for an autonomous vehicle fits a jerk profile including a plurality of phases within a set of acceptable parameters, a jerk value being constant within each phase of the jerk profile. The system parameterizes the jerk profile based on an initial velocity, an initial acceleration, a final velocity, and a final acceleration for the first segment. The system then integrates the jerk profile to determine a first trajectory function, the first trajectory function including a speed for the vehicle at a given time. The system guides the vehicle along the first segment of the path according to the first trajectory function. The system detects an unplanned obstacle along the first segment of the path. The system plans a second trajectory function for a second segment of the path between a current location of the vehicle and a location on the path before the unplanned obstacle.

System and method for trajectory planning for unexpected pedestrians

One or more embodiments of techniques or systems for generating turn predictions or predictions are provided herein. Environment layout information of an operating environment through which a first vehicle is travelling may be received. A current location of the first vehicle may be received. One or more other vehicles may be detected. Additional environment layout information from other vehicles may be received. A model including the operating environment, the first vehicle, and one or more of the other vehicles may be built. The model may be based on the environment layout information and the additional environment layout information and indicative of an intent of a driver of one of the other vehicles. Further, predictions may be generated based on the model, which may be based on a Hidden Markov Model (HMM), a Support Vector Machine (SVM), a Dynamic Bayesian Network (DBN), or a combination thereof.

Turn predictions

A method and system are disclosed for recognizing speech errors, such as in a spoken short messages, using an audio input device to receive an utterance of a short message, using an automated speech recognition module to generate a text sentence corresponding to the utterance, generating an N-best list of predicted error sequences for the text sentence using a linear-chain conditional random field (CRF) module, where each word of the text sentence is assigned a label in each of the predicted error sequences, and each label is assigned a probability score. The predicted error sequence labels are rescored using a metacost matrix module, the best rescored error sequence from the N-best list of predicted error sequences is selected using a Recognition Output Voting Error Reduction (ROVER) module, and a dialog action is executed by a dialog action module based on the best rescored error sequence and the dialog action policy.

Method and system for the correction-centric detection of critical speech recognition errors in spoken short messages

One or more embodiments of techniques or systems for modeling familiarity for a traveler are provided herein. Familiarity evidence can be received, indicative of how familiar a traveler is with an area or road segment, and based on a number of visits the traveler has made to that area. The familiarity evidence can be used to generate one or more familiarity models indicative of a predicted familiarity of locations around the area. Familiarity models can be based on kernels, graph distances, Markov random fields (MRFs), etc. When route directions are generated from an origin location to a destination location, one or more of the directions can be provided based on one or more of the familiarity models. For example, if a familiarity model indicates that a traveler is familiar with a route, driving directions of the route can be adapted to be more succinct.

Familiarity modeling

The present invention provides a method for identifying a turn, such as a sentence or phrase, for addition to a platform dialog comprising a plurality of turns. Lexical features of each of a set of candidate turns relative to one or more turns in the platform dialog are determined. Semantic features associated with each candidate turn and associated with the platform dialog are determined to identify one or more topics associated with each candidate turn and with the platform dialog. Lexical features of each candidate turn are compared to lexical features of the platform dialog and semantic features associated with each candidate turn are compared to semantic features of the platform dialog to rank the candidate turns based on similarity of lexical features and semantic features of each candidate turn to lexical features and semantic features of the platform dialog.

Dialog coherence using semantic features

A method and system for multimodal human-vehicle interaction including receiving input from an occupant in a vehicle via more than one mode and performing multimodal recognition of the input. The method also includes augmenting at least one recognition hypothesis based on at least one visual point of interest and determining a belief state of the occupant's intent based on the recognition hypothesis. The method further includes selecting an action to take based on the determined belief state.

System and method for multimodal human-vehicle interaction and belief tracking

An utterance is received from a user specifying a location attribute and a landmark. A set of candidate locations is identified based on the specified location attribute, and a confidence score can be determined for each candidate location. A set of landmarks is identified based on the specified landmark, and confidence scores can be determined for the landmarks. An associated kernel model is generated for each landmark. Each kernel model is centered at the location of the associated landmark on a map, and the amplitude of the kernel model can be based on landmark attributes, landmark confidence scores, characteristics of the user, and the like. The candidate locations are ranked based on the amplitudes of overlapping kernel models at the candidate locations, and can also be ranked based on confidence scores associated with the candidate locations. A candidate location is selected and presented to the user based on the candidate location ranking.

Landmark-based location belief tracking for voice-controlled navigation system

A powertrain of a hybrid electric vehicle (HEV) is controlled. A first value &#x3b1;and a second value &#x3b1;are determined. &#x3b1;represents a proportion of an instantaneous power requirement (P) supplied by an engine of the HEV. &#x3b1;controls a recharging rate of a battery of the HEV. A determination is performed, based on &#x3b1;and &#x3b1;, regarding how much engine power to use (P) and how much battery power to use (P). Pand Pare sent to the powertrain.

Hybrid vehicle fuel efficiency using inverse reinforcement learning

The prevention of vehicle accidents is targeted. A road texture model is created based on a vehicle camera image. An initial vehicle location estimate is determined, and map imagery is obtained based on this location estimate. A refined vehicle location is determined using visual egomotion. In particular, 3D features of the vehicle image and the retrieved map imagery are identified and aligned. A map image is selected based on this alignment, and the location associated with the map image is modified by a displacement between the selected map image and the vehicle image to produce a refined vehicle location. A road boundary model is created based on the road texture model and the refined vehicle location, and a road departure model is created based on the road boundary model and vehicle odometry information. The operator of the vehicle is warned of a road departure based on the road departure model.

Road departure warning system

The present invention provides a method for extracting relationships between words in textual data. Initially, a classifier is trained to identify text data having a specific format, such as situation-response or cause-effect, using a training corpus. The classifier receives input identifying components of the text data having the specified format and then extracts features from the text data having the specified format, such as the part of speech for words in the text data, the semantic role of words within the text data and sentence structure. These extracted features are then applied to text data to identify components of the text data which have the specified format. Rules are then extracted from the text data having the specified format.

Scalable knowledge extraction

An action is performed in a spoken dialog system in response to a user's spoken utterance. A policy which maps belief states of user intent to actions is retrieved or created. A belief state is determined based on the spoken utterance, and an action is selected based on the determined belief state and the policy. The action is performed, and in one embodiment, involves requesting clarification of the spoken utterance from the user. Creating a policy may involve simulating user inputs and spoken dialog system interactions, and modifying policy parameters iteratively until a policy threshold is satisfied. In one embodiment, a belief state is determined by converting the spoken utterance into text, assigning the text to one or more dialog slots associated with nodes in a probabilistic ontology tree (POT), and determining a joint probability based on probability distribution tables in the POT and on the dialog slot assignments.

Belief tracking and action selection in spoken dialog systems

The present invention provides a system and method associating the freeform speech commands with one or more predefined commands from a set of predefined commands. The set of predefined commands are stored and alternate forms associated with each predefined command are retrieved from an external data source. The external data source receives the alternate forms associated with each predefined command from multiple sources so the alternate forms represent paraphrases of the predefined command. A representation including words from the predefined command and the alternate forms of the predefined command, such as a vector representation, is generated for each predefined command. A similarity value between received speech data and each representation of a predefined command is computed and the speech data is classified as the predefined command whose representation has the highest similarity value to the speech data.

Free-speech command classification for car navigation system

The present invention provides a method for learning a policy used by a computing system to perform a task, such delivery of one or more objects by the computing system. During a first time interval, the computing system determines a first state, a first action and a first reward value. As the computing system determines different states, actions and reward values during subsequent time intervals, a state description identifying the current sate, the current action, the current reward and a predicted action is stored. Responsive to a variance of a stored state description falling below a threshold value, the stored state description is used to modify one or more weights in the policy associated with the first state.

Smoothed sarsa: reinforcement learning for robot delivery tasks

The present invention provides a method for extracting relationships between words in textual data. Initially, training relationship data, such as word triplets describing a cause-effect relationship, is received and used to collect additional textual data including the training relationship data. Distributed data collection is used to receive the training data and collect the additional textual data, allowing a broad range of data to be acquired from multiple sources. Syntactic patterns are extracted from the additional textual data and a distributed data source is scanned to extract additional relationship data describing one or more causal relationships using the extracted syntactic patterns. The extracted additional relationship data is then stored, and can be validated by a supervised learning algorithm before storage and used to train a classifier for automatic validation of additional relationship data.

Learning syntactic patterns for automatic discovery of causal relations from text

The present invention provides a method for incorporating features from heterogeneous auxiliary datasets into input text data for use in classification. Heterogeneous auxiliary datasets, such as labeled datasets and unlabeled datasets, are accessed after receiving input text data. Features are extracted from each of the heterogeneous auxiliary datasets. The features are combined with the input text data to generate a set of features which may potentially be used to classify the input text data. Classification features are then extracted from the set of features and used to classify the input text data. In one embodiment, the classification features are extracted by calculating a mutual information value associated with each feature in the set of features and identifying features having a mutual information value exceeding a threshold value.

Growing community of inventors

Mountain View, CA, United States of America

Rakesh Gupta