A Novel Graph-Based Trajectory Predictor With Pseudo-Oracle

Yang, Biao; Yan, Guocheng; Wang, Pin; Chan, Ching-Yao; Song, Xiang; Chen, Yang

doi:10.1109/tnnls.2021.3084143

Cited by 29 publications

(16 citation statements)

References 37 publications

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“…In the follow-up work, an IMU inertial unit and a magnetometer will be introduced to further optimize the matching between two frames, which can improve the robustness and accuracy of the SLAM system. Additionally, the re-identification algorithms [19][20][21] and trajectory prediction algorithm [22] can be incorporated into the system to improve the recall of loopback detection, which will reduce the cumulative error of the pose, meanwhile, real-time dense reconstruction [23], pedestrian dynamic detection [24], and object detection algorithm [25] can be added to accomplish dynamic target rejection, improve localization accuracy, and accomplish more tasks.…”

Section: Discussionmentioning

confidence: 99%

Feature Matching Combining Variable Velocity Model with Reverse Optical Flow

Zhao¹,

Sun²,

Zhang³

et al. 2023

Computer Systems Science and Engineering

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The ORB-SLAM2 based on the constant velocity model is difficult to determine the search window of the reprojection of map points when the objects are in variable velocity motion, which leads to a false matching, with an inaccurate pose estimation or failed tracking. To address the challenge above, a new method of feature point matching is proposed in this paper, which combines the variable velocity model with the reverse optical flow method. First, the constant velocity model is extended to a new variable velocity model, and the expanded variable velocity model is used to provide the initial pixel shifting for the reverse optical flow method. Then the search range of feature points is accurately determined according to the results of the reverse optical flow method, thereby improving the accuracy and reliability of feature matching, with strengthened interframe tracking effects. Finally, we tested on TUM data set based on the RGB-D camera. Experimental results show that this method can reduce the probability of tracking failure and improve localization accuracy on SLAM (Simultaneous Localization and Mapping) systems. Compared with the traditional ORB-SLAM2, the test error of this method on each sequence in the TUM data set is significantly reduced, and the root mean square error is only 63.8% of the original system under the optimal condition.

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Section: Discussionmentioning

confidence: 99%

Feature Matching Combining Variable Velocity Model with Reverse Optical Flow

Zhao¹,

Sun²,

Zhang³

et al. 2023

Computer Systems Science and Engineering

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“…With the developments of machine learning [3][4][5], researchers proposed two kinds of prediction methods, including model-driven [6,7] and data-driven [8][9][10]. For the former, some researchers used the Markov chain and Kalman filter [6,7] to perform trajectory prediction.…”

Section: Figure 1: Driving Scenario Of An Autonomous Vehiclementioning

confidence: 99%

Forecasting Future Trajectories with an Improved Transformer Network

Wu¹,

Zhang²,

Wang³

et al. 2023

Computers, Materials &Amp; Continua

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An increase in car ownership brings convenience to people's life. However, it also leads to frequent traffic accidents. Precisely forecasting surrounding agents' future trajectories could effectively decrease vehicle-vehicle and vehicle-pedestrian collisions. Long-short-term memory (LSTM) network is often used for vehicle trajectory prediction, but it has some shortages such as gradient explosion and low efficiency. A trajectory prediction method based on an improved Transformer network is proposed to forecast agents' future trajectories in a complex traffic environment. It realizes the transformation from sequential step processing of LSTM to parallel processing of Transformer based on attention mechanism. To perform trajectory prediction more efficiently, a probabilistic sparse self-attention mechanism is introduced to reduce attention complexity by reducing the number of queried values in the attention mechanism. Activate or not (ACON) activation function is adopted to select whether to activate or not, hence improving model flexibility. The proposed method is evaluated on the publicly available benchmarks nextgeneration simulation (NGSIM) and ETH/UCY. The experimental results indicate that the proposed method can accurately and efficiently predict agents' trajectories.

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“…In order to simplify the model and improve its practicability, many dynamic models based on the mathematical relationship between vehicle motion parameters (position, velocity, and acceleration) have been proposed, but these models ignore the force affecting vehicle motion. Among them, simpler models, including the Constant Velocity (CV) model and Constant Acceleration (CA) model [7][8][9][10][11], assume that a vehicle trajectory is a straight line. The Constant Turn Rate and Velocity (CTRV) model and Constant Turn Rate and Acceleration (CTRA) model consider the vehicle yaw angle and yaw rate [8,10,[12][13][14][15].…”

Section: Physics-based Motion Modelsmentioning

confidence: 99%

CAE‐GAN: A hybrid model for vehicle trajectory prediction

Chen

Zhou

Cai

et al. 2022

IET Intelligent Trans Sys

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Trajectory prediction of surrounding vehicles is a crucial capability of intelligent driving vehicles. In a scene, a vehicle and its surrounding vehicles constitute an integral system, and the vehicle's future motion trajectory is affected by the actions of surrounding vehicles. The influencing mode and degree are hidden in the relevant historical information of the vehicle and its neighbour vehicle. The existing trajectory prediction methods either do not consider the confidence of the predicted trajectory, or the accuracy requirement is ignored when considering the confidence of the predicted trajectory. In order to address this problem, a mixed Conditional AutoEncoder Generative Adversarial Network (CAE‐GAN) model based on the multi‐loss function is proposed. The proposed model uses the encoder–decoder structure with a convolutional social pool to extract general features. The generative adversarial networks (GANs) are used to extract the confidence features of the generated trajectories, which enables the proposed model to generate trajectories that are close to the real trajectories. In addition, a classifier structure based on an LSTM network is added to output the probability that the predicted trajectory belongs to a particular lateral maneuver so that the generated trajectory lateral maneuver of the model is consistent with the real trajectory lateral maneuver. The proposed model is evaluated using the publicly available NGSIM US‐101 and I‐80 datasets, and results show that the accuracy of the proposed model is higher than that of the existing methods. The proposed model achieves an average accuracy improvement of 16.34% in comparison to the most advanced existing models.

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A Novel Graph-Based Trajectory Predictor With Pseudo-Oracle

Cited by 29 publications

References 37 publications

Feature Matching Combining Variable Velocity Model with Reverse Optical Flow

Feature Matching Combining Variable Velocity Model with Reverse Optical Flow

Forecasting Future Trajectories with an Improved Transformer Network

CAE‐GAN: A hybrid model for vehicle trajectory prediction

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