Honda Motor Co., Ltd.

Chengxi Li

Yi-Ting Chen

Yue Meng

A system and method for risk object identification via causal inference that includes receiving at least one image of a driving scene of an ego vehicle and analyzing the at least one image to detect and track dynamic objects within the driving scene of the ego vehicle. The system and method also include implementing a mask to remove each of the dynamic objects captured within the at least one image. The system and method further include analyzing a level of change associated with a driving behavior with respect to a removal of each of the dynamic objects. At least one dynamic object is identified as a risk object that has a highest level of influence with respect to the driving behavior.

System for risk object identification via causal inference and method thereof

A system and method for predicting driving actions based on intent-aware driving models that include receiving at least one image of a driving scene of an ego vehicle. The system and method also include analyzing the at least one image to detect and track dynamic objects located within the driving scene and to detect and identify driving scene characteristics associated with the driving scene and processing an ego-thing graph associated with the dynamic objects and an ego-stuff graph associated with the driving scene characteristics. The system and method further include predicting a driver stimulus action based on a fusion of representations of the ego-thing graph and the ego-stuff graph and a driver intention action based on an intention representation associated with driving intentions of a driver of the ego vehicle.

Driver-centric risk assessment: risk object identification via causal inference with intent-aware driving models

Systems and methods for driver behavior recognition is provided. In one embodiment a computer implemented method includes receiving image data associated with a general objects. The method also includes identifying a reactive object and an inert object from the general objects based on the image data. An ego reactive graph is generated for the reactive object based on a reactive feature of the reactive object and a reactive position vector. An ego inert graph is generated for the inert object based on an inert feature of the inert object and an inert distance. The method further includes performing interaction modeling based on the ego reactive graphs and the ego inert graphs to generate updated features. The method also includes performing temporal modeling on the updated features. The method further includes determining an egocentric representation of a tactical driver behavior based at least in part on the updated features.

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West Lafayette, IN, United States of America

Chengxi Li