Toyota Research Institute, Inc.

Noah J Epstein

Guy Rosman

Blake Warren Wulfe

Luke D Fletcher

Schuyler H Cohen

Alexander Glencairn Cunningham

Robert A E Zidek

Systems and methods of model or prediction algorithm selection are provided. An autonomous control system may include a perception component that, based on environmental inputs regarding an object(s), a vehicle's operating characteristics, etc., outputs a current state of the vehicle's surrounding environment. This in turn, is used as input to a prediction component comprising a plurality of prediction algorithms. The prediction component outputs a set of predictions regarding the trajectory of the object(s). Accordingly, for each object, a set of trajectories at specific timesteps may be generated by the different prediction algorithms which are input to a planner component. These trajectories may then be analyzed, compared, or otherwise processed to determine which trajectory regarding the object is most accurate. The prediction algorithm or model that produced the most accurate predicted trajectory may then be used for subsequent predictions/timesteps.

Systems and methods for multiple algorithm selection

System, methods, and embodiments described herein relate to predicting a future state of an object detected in a vicinity of a vehicle. In one embodiment, a method for predicting a state of an object includes detecting, at a plurality of discrete times [t, t&#x2212;1, t&#x2212;2, . . . ], a respective plurality of states of the object, obtaining, based at least in part on a present location of the vehicle, supplemental information, associated with an environment of the present location, that indicates at least a speed reduction factor, executing a prediction operation to determine a predicted state of the object at a time t+1 based at least in part on the detected plurality of states and the supplemental information, determining an actual state of the object at a time t+1 based on data from the one or more sensors, and modifying the prediction operation based at least in part on the actual state.

Object state tracking and prediction using supplemental information

Systems and methods described herein relate to vehicular navigation. One embodiment generates a polyline reference path for a vehicle; stores a representation of the polyline in a data structure; detects a plurality of obstacles ahead of the vehicle; identifies one or more obstacle gates among the plurality of obstacles using path coordinates relative to the reference path, each obstacle gate including at least one cluster of obstacles; identifies one or more gaps within each of the one or more obstacle gates; determines an obstacle-avoidance path for the vehicle that passes through a particular one of the one or more gaps in each of the one or more obstacle gates; and controls one or more aspects of operation of the vehicle based, at least in part, on the obstacle-avoidance path.

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Cambridge, MA, United States of America

Noah J Epstein