Honda Motor Co., Ltd.

Srikanth Malla

Chiho Choi

Behzad Dariush

Sangjae Bae

Akira Kanehara

Jiachen Li

Isht Dwivedi

Haiming Gang

Harshayu Girase

A system and method for future forecasting using action priors that include receiving image data associated with a surrounding environment of an ego vehicle and dynamic data associated with dynamic operation of the ego vehicle. The system and method also include analyzing the image data to classify dynamic objects as agents and to detect and annotate actions that are completed by the agents that are located within the surrounding environment of the ego vehicle and analyzing the dynamic data to process an ego motion history that is associated with the ego vehicle that includes vehicle dynamic parameters during a predetermined period of time. The system and method further include predicting future trajectories of the agents located within the surrounding environment of the ego vehicle and a future ego motion of the ego vehicle within the surrounding environment of the ego vehicle based on the annotated actions.

System and method for future forecasting using action priors

A system and method for providing long term and key intentions for trajectory prediction that include receiving image data and LiDAR data associated with RGB images and LiDAR point clouds that are associated with a surrounding environment of an ego agent and processing a long term and key intentions for trajectory prediction dataset (LOKI dataset) that is utilized to complete joint trajectory and intention prediction for heterogeneous traffic agents. The system and method also include encoding a past observation history of each of the heterogeneous traffic agents and sampling a respective goal. The system and method further include decoding and predicting future trajectories associated with each of the heterogeneous traffic agents based on data included within the LOKI dataset, the encoded past observation history, and the respective goal.

System and method for providing long term and key intentions for trajectory prediction

A system and method for completing trajectory prediction from agent-augmented environments that include receiving image data associated with surrounding environment of an ego agent and processing an agent-augmented static representation of the surrounding environment of the ego agent based on the image data. The system and method also include processing a set of spatial graphs that correspond to an observation time horizon based on the agent-augmented static representation. The system and method further include predicting future trajectories of agents that are located within the surrounding environment of the ego agent based on the spatial graphs.

System and method for completing trajectory prediction from agent-augmented environments

In one embodiment, a system includes one or more vehicle sensors for capturing host data and a processor having modules. The data receiving module identifies one or more proximate vehicles within the environment based on one or more of the host data and proximate data received from the one or more proximate vehicles. The motion prediction module generates a first joint uncertainty distribution based on an initial joint uncertainty model and a host model distribution. The motion prediction module also samples host kinematic predictions based on the first joint uncertainty distribution and the host data. The object localization module generates a second joint uncertainty distribution based on the initial joint uncertainty model and an object prediction model distribution. The object localization module also samples proximate kinematic predictions based on the second joint uncertainty distribution and the proximate data.

Systems and methods for providing future object localization

A system and method for completing joint risk localization and reasoning in driving scenarios that include receiving a plurality of images associated with a driving scene of an ego agent. The system and method also include inputting image data associated with the plurality of images to an encoder and inputting concatenated features to a decoder that identifies at least one of: an important traffic agent and an important traffic infrastructure that is located within the driving scene of the ego agent. The system and method further include controlling at least one system of the ego agent to provide a response to account for the at least one of: the important traffic agent and the important traffic infrastructure that is located within the driving scene of the ego agent.

System and method for completing joint risk localization and reasoning in driving scenarios

A system and method for providing social-stage spatio-temporal multi-modal future forecasting that include receiving environment data associated with a surrounding environment of an ego vehicle and implementing graph convolutions to obtain attention weights that are respectively associated with agents that are located within the surrounding environment. The system and method also include decoding multi modal trajectories and probabilities for each of the agents. The system and method further include controlling at least one vehicle system of the ego vehicle based on predicted trajectories associated with each of the agents and the rankings associated with probabilities that are associated with each of the predicted trajectories.

System and method for providing social-stage spatio-temporal multi-modal future forecasting

A system for method for future forecasting using action priors that include receiving image data associated with a surrounding environment of an ego vehicle and dynamic data associated with dynamic operation of the ego vehicle. The system and method also include analyzing the image data and detecting actions associated with agents located within the surrounding environment of the ego vehicle and analyzing the dynamic data and processing an ego motion history of the ego vehicle. The system and method further include predicting future trajectories of the agents located within the surrounding environment of the ego vehicle and a future ego motion of the ego vehicle within the surrounding environment of the ego vehicle.

According to one aspect, composite field based single shot trajectory prediction may include receiving an image of an environment including a number of agents, extracting a set of features from the image, receiving the image of the environment, encoding a set of trajectories from the image, concatenating the set of features and the set of trajectories from the image to generate an interaction module input, receiving the interaction module input, encoding a set of interactions between the number of agents and between the number of agents and the environment, concatenating the set of interactions and a localization composite field map to generate a decoder input, receiving the decoder input, generating the localization composite field map and an association composite field map, and generating a set of trajectory predictions for the number of agents based on the localization composite field map and the association composite field map.

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Sunnyvale, CA, United States of America

Srikanth Malla