Honda Motor Co., Ltd.

Hengbo Ma

Chiho Choi

Jiachen Li

Haiming Gang

Manh Trung Huynh

Systems and methods for uncertainty estimation in vehicle trajectory prediction are provided. In one embodiment, a method includes calculating predicted error scores for each static data point of a set of static data points for a new traffic scenario. A static data point corresponds to a location of the new traffic scenario. The method includes ranking the static data points of the set of static data points based on the predicted error scores. The method further includes selecting a predetermined percentage of the ranked static data points having the highest predicted error scores of the ranked static data points. The method includes identifying locations of the new traffic scenario corresponding to the selected ranked static data points. The method includes collecting additional data points from the identified locations of the new traffic scenario. The method includes training a decoder for a neural network based on the additional data points.

Systems and methods for uncertainty estimation in vehicle trajectory prediction

Object identification may be provided herein. A feature extractor may extract a first set of visual features, extract a second set of visual features, concatenate the first set of visual features, the second set of visual features, and a set of bounding box information, determine a number of object features and a global feature for a scene, and receive ego-vehicle feature information associated with an ego-vehicle. An object classifier may receive the number of object features, the global feature, and the ego-vehicle feature information, generate relational features with respect to relationships between each of the number of objects from the scene, and classify each of the number of objects from the scene based on the number of object features, the relational features, the global feature, the ego-vehicle feature information, and an intention of the ego-vehicle.

Autonomous vehicle object classifier method and system

A system and method for completing continual multi-agent trajectory forecasting with a graph-based conditional generative memory system that include receiving data associated with a surrounding location of an ego agent and inputting the data associated with the surrounding location of the ego agent to at least one episodic memory buffer and processing scene graphs associated with the surrounding location of the ego agent that are associated with the plurality of time steps. The system and method additionally include aggregating the data associated with the surrounding location of the ego agent associated with the plurality of time steps into mixed data and training a generative memory and a predictor with the mixed data. The system and method further include predicting future trajectories associated with traffic agents that are located within the surrounding location of the ego agent based on the training of the generative memory and the predictor.

System and method for completing continual multi-agent trajectory forecasting

Systems and methods for providing a framework for predicting future frames using diverse sampling are provided. In one embodiment, a method for predicting future frames includes receiving a video having a first frame from a first time and a second frame from a second time. The first frame and the second frame are represented in image space. The method also includes updating a prediction model based on the video. The method further includes determining whether a stopping condition is satisfied. In response to determining that the stopping condition has been satisfied, the method includes generating a plurality of future frames for a third time after the second time. The plurality of future frames is generated based on a normalized distance metric that preserves distance of samples in the latent space to the image space. The method yet further includes selecting a candidate frame from the plurality of future frames.

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Albany, CA, United States of America

Hengbo Ma