公开(公告)号：US20230410659A1

公开(公告)日：2023-12-21

申请号：US18249715

申请日：2021-04-12

Applicant: SOUTHEAST UNIVERSITY

Inventor： Xu LI ,  Jinchao HU ,  Weiming HU

IPC: G08G1/16 ,  G06V20/70 ,  G06V10/82 ,  G06V10/26 ,  G06V40/20 ,  G06V10/77 ,  G06V10/774 ,  G06V20/52 ,  G08G1/052

CPC classification number: G08G1/166 ,  G06V20/70 ,  G06V10/82 ,  G06V10/26 ,  G06V40/25 ,  G06V10/7715 ,  G06V10/774 ,  G06V20/52 ,  G08G1/052

Abstract: A method for predicting a pedestrian crossing behavior for an intersection includes the following steps: step 1: designing an immediate reward function; step 2: establishing a fully convolutional neural network-long-short term memory network (FCN-LSTM) model to predict a motion reward function; step 3: training the fully convolutional neural network-long-short term memory network (FCN-LSTM) model based on reinforcement learning; and step 4: predicting the pedestrian crossing behavior and performing hazard early-warning. The technical solution does not require establishment of a complex pedestrian movement model or preparation of massive labeled data sets, achieves autonomous learning of pedestrian crossing behavior features at the intersection, predicts their walking, stopping, running and other behaviors, especially predicts the pedestrian crossing behavior when inducing hazards such as pedestrian-vehicle collision and scratch in real time, and performs hazard early-warning on crossing pedestrians and passing vehicles.

公开(公告)号：US20230182725A1

公开(公告)日：2023-06-15

申请号：US17766870

申请日：2021-04-12

Applicant: SOUTHEAST UNIVERSITY

Inventor： Xu LI ,  Weiming HU ,  Jinchao HU ,  Xuefen ZHU

IPC: B60W30/095 ,  G06N3/045 ,  G06N3/0464

CPC classification number: B60W30/0956 ,  G06N3/045 ,  G06N3/0464 ,  B60W2554/4041 ,  B60W2420/52 ,  B60W2554/80 ,  B60W2300/125

Abstract: The present invention discloses a backward anti-collision driving decision-making method for a heavy commercial vehicle. Firstly, a traffic environment model is established, and movement state information of a heavy commercial vehicle and a vehicle behind the heavy commercial vehicle is collected. Secondly, a backward collision risk assessment model based on backward distance collision time is established, and a backward collision risk is accurately quantified. Finally, a backward anti-collision driving decision-making problem is described as a Markov decision-making process under a certain reward function, a backward anti-collision driving decision-making model based on deep reinforcement learning is established, and an effective, reliable and adaptive backward anti-collision driving decision-making policy is obtained. The method provided by the present invention can overcome the defect of lack for research on the backward anti-collision driving decision-making policy for the heavy commercial vehicle in the existing method, can quantitatively output proper steering wheel angle and throttle opening control quantities, can provide effective and reliable backward anti-collision driving suggestions for a driver, and can reduce backward collision accidents.

公开(公告)号：US20240375682A1

公开(公告)日：2024-11-14

申请号：US18259378

申请日：2022-02-25

Applicant: Southeast University

Inventor： Xu LI ,  Weiming HU ,  Jinchao HU ,  Kun WEI ,  Qimin XU

IPC: B60W60/00 ,  B60W50/00

Abstract: A method of making a highly humanoid safe driving decision for an automated driving commercial vehicle, includes: collecting synchronously multi-source information on driving behaviors in typical traffic scenarios, constructing an expert trajectory data set representing driving behaviors of excellent drivers; simulating the driving behaviors of excellent drivers by utilizing a generative adversarial imitation learning (GAIL) algorithm, in a comprehensive consideration of influences of factors such as a forward collision, a backward collision, a transverse collision, a vehicle roll stability and a driving smoothness on a driving safety, constructing a generator and a discriminator by utilizing a proximal policy optimization algorithm and a deep neural network respectively, and establishing a safe driving decision-making model with highly humanoid level; and training the safe driving decision-making model to obtain safe driving policies under different driving conditions and to implement an output of an advanced decision-making for the automated driving commercial vehicle.