Honda Motor Co., Ltd.

Akansel Cosgun

Kikuo Fujimura

Alireza Nakhaei Sarvedani

David Francis Isele

Gholamreza Rahimi

Kaushik Subramanian

Priyam Parashar

Mustafa Mukadam

Systems and methods for generating instructions for a vehicle to navigate an unsignaled intersection are provided. The method may include: generating an expected return over a sequence of actions of the vehicle; determining an optimal policy by selecting an action with a maximum value for the vehicle; executing dynamic frame skipping to expedite learning a repeated action of the vehicle; prioritize an experience replay by utilizing an experience replay buffer to break correlations between sequential steps of the vehicle; generate a plurality of state-action representations based on at least one of the expected return, the optimal policy, the dynamic frame skipping, or the prioritized experience replay; generate the instructions for navigating the unsignaled intersection based on the plurality of state-action representations; and transmit the instructions for navigating the unsignaled intersection to the vehicle such that the vehicle executes the instructions to navigate the unsignaled intersection.

Systems and methods for generating instructions for navigating intersections with autonomous vehicles

According to one aspect, an autonomous vehicle policy generation system may include a state input generator generating a set of attributes associated with an autonomous vehicle undergoing training, a traffic simulator simulating a simulation environment including the autonomous vehicle, a roadway associated with a number of lanes, and another vehicle within the simulation environment, a Q-masker determining a mask to be applied to a subset of a set of possible actions for the autonomous vehicle for a time interval, and an action generator exploring a remaining set of actions from the set of possible actions and determining an autonomous vehicle policy for the time interval based on the remaining set of actions and the set of attributes associated with the autonomous vehicle.

Autonomous vehicle policy generation

According to one aspect, keyframe based autonomous vehicle operation may include collecting vehicle state information and collecting environment state information. A size of an object within the environment, a distance between the object and the autonomous vehicle, and a lane structure of the environment through which the autonomous vehicle is travelling may be determined. A matching keyframe model may be selected based on the size of the object, the distance from the object to the autonomous vehicle, the lane structure of the environment, and the vehicle state information. Suggested limits for a driving parameter associated with autonomous vehicle operation may be generated based on the selected keyframe model. The autonomous vehicle may be commanded to operate autonomously according to the suggested limits for the driving parameter.

Keyframe based autonomous vehicle operation

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Melbourne, Australia

Akansel Cosgun