Huawei Technologies Co., Limited

Wulong Liu

Bin Wang

Yuzheng Zhuang

Dong Li

Chen Chen

Jun Qian

Yingxue Zhang

Zhanguang Zhang

Mohammad Amini

The present disclosure relates to driving decision-making methods, apparatuses, and chips. One example method includes building a Monte Carlo tree based on a current driving environment state, where the Monte Carlo tree includes a root node and N−1 non-root nodes, each node represents one driving environment state, and a driving environment state represented by any non-root node is predicted by a stochastic model of driving environments. Based on at least one of an access count or a value function of each node in the Monte Carlo tree, a node sequence that starts from the root node and ends at a leaf node is determined, and a driving action sequence is determined based on a driving action corresponding to each node in the node sequence.

Driving decision-making method and apparatus and chip

An autonomous lane change method includes: calculating a local neighbor feature and a global statistical feature of an autonomous vehicle at a current moment based on travel information of the autonomous vehicle at the current moment and motion information of obstacles in lanes within a sensing range of the autonomous vehicle; obtaining a target action indication based on the local neighbor feature, the global statistical feature, and a current control policy; and executing the target action according to the target action indication. On the basis of the local neighbor feature, the global statistical feature is further introduced into the current control policy to obtain the target action indication. The target action obtained by combining local and global road obstacle information is a globally optimal decision action.

Autonomous lane change method and apparatus, and storage medium

Methods and systems for generating a floorplan for a circuit are disclosed. A netlist graph of the circuit and block features associated with blocks of the circuit are obtained. A reinforcement learning (RL) agent is used to generate a sequence of corner block list (CBL) actions. Each CBL action is generated by: generating a current state embedding representing a current state of the floorplan; and inputting the current state embedding to a policy network of the RL agent to generate a predicted output vector, which is used to generate the CBL action. After each CBL action is generated, the current CBL representation of the floorplan and the block features are updated to reflect the state of the floorplan after applying the CBL action. The CBL representation is outputted as a final floorplan after all blocks have been placed.

System and method for generating a floorplan for a digital circuit using reinforcement learning

In the method for optimizing decision-making regulation and control, a first traveling sequence is obtained, where the first traveling sequence includes a first trajectory sequence of the vehicle in information about a first environment and first target driving behavior output by a behavior decision-making layer of a decision-making and control system based on the information about the first environment. A second traveling sequence is obtained, where the second traveling sequence includes a second trajectory sequence output by a motion planning layer of the decision-making and control system based on preset second target driving behavior and the second target driving behavior. The behavior decision-making layer is optimized based on a difference between the first traveling sequence and a preset traveling sequence, and the motion planning layer is optimized based on a difference between the second traveling sequence and the preset traveling sequence.

Growing community of inventors

Montreal, Canada

Wulong Liu