scholarly journals Vehicle Lane-Changes Trajectory Prediction Model Considering External Parameters

2021 ◽  
Vol 33 (5) ◽  
pp. 745-754
Author(s):  
Xuchuan Li ◽  
Lingkun Fan ◽  
Tao Chen ◽  
Shuaicong Guo
Keyword(s):  
Autonomous Vehicles ◽  
Short Term Memory ◽  
Sensitive Area ◽  
Flow State ◽  
Trajectory Prediction ◽  
Training Time ◽  
Lane Changes ◽  
Straight Line ◽  
Historical Trajectory ◽  
Hidden States

The ability to predict the motion of vehicles is essential for autonomous vehicles. Aiming at the problem that existing models cannot make full use of the external parameters including the outline of vehicles and the lane, we proposed a model to use the external parameters thoroughly when predicting the trajectory in the straight-line and non-free flow state. Meanwhile, dynamic sensitive area is proposed to filter out inconsequential surrounding vehicles. The historical trajectory of the vehicles and their external parameters are used as inputs. A shared Long Short-Term Memory (LSTM) cell is proposed to encode the explicit states obtained by mapping historical trajectory and external parameters. The hidden states of vehicles obtained from the last step are used to extract latent driving intent. Then, a convolution layer is designed to fuse hidden states to feed into the next prediction circle and a decoder is used to decode the hidden states of the vehicles to predict trajectory. The experiment result shows that the dynamic sensitive area can shorten the training time to 75.86% of the state-of-the-art work. Compared with other models, the accuracy of our model is improved by 23.7%. Meanwhile, the model's ability of anti-interference of external parameters is also improved.

scholarly journals TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents

2019 ◽  
Vol 33 ◽  
pp. 6120-6127 ◽  
Author(s):  
Yuexin Ma ◽  
Xinge Zhu ◽  
Sibo Zhang ◽  
Ruigang Yang ◽  
Wenping Wang ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Short Term Memory ◽  
Autonomous Vehicle ◽  
Large City ◽  
Urban Traffic ◽  
Heterogeneous Traffic ◽  
Prediction Algorithm ◽  
Trajectory Prediction ◽  
Long Short Term Memory ◽  
Traffic Agents

To safely and efficiently navigate in complex urban traffic, autonomous vehicles must make responsible predictions in relation to surrounding traffic-agents (vehicles, bicycles, pedestrians, etc.). A challenging and critical task is to explore the movement patterns of different traffic-agents and predict their future trajectories accurately to help the autonomous vehicle make reasonable navigation decision. To solve this problem, we propose a long short-term memory-based (LSTM-based) realtime traffic prediction algorithm, TrafficPredict. Our approach uses an instance layer to learn instances’ movements and interactions and has a category layer to learn the similarities of instances belonging to the same type to refine the prediction. In order to evaluate its performance, we collected trajectory datasets in a large city consisting of varying conditions and traffic densities. The dataset includes many challenging scenarios where vehicles, bicycles, and pedestrians move among one another. We evaluate the performance of TrafficPredict on our new dataset and highlight its higher accuracy for trajectory prediction by comparing with prior prediction methods.

scholarly journals Prediction of Lane-Changing Maneuvers with Automatic Labeling and Deep Learning

2020 ◽  
Vol 2674 (7) ◽  
pp. 336-347 ◽  
Author(s):  
Vishal Mahajan ◽  
Christos Katrakazas ◽  
Constantinos Antoniou
Keyword(s):  
Real Time ◽  
Autonomous Vehicles ◽  
Short Term Memory ◽  
Highway Safety ◽  
Unlabeled Data ◽  
Support Vector ◽  
False Alarms ◽  
Lane Changing ◽  
Lane Changes ◽  
Significant Research

Highway safety has attracted significant research interest in recent years, especially as innovative technologies such as connected and autonomous vehicles (CAVs) are fast becoming a reality. Identification and prediction of driving intention are fundamental for avoiding collisions as it can provide useful information to drivers and vehicles in their vicinity. However, the state-of-the-art in maneuver prediction requires the utilization of large labeled datasets, which demand a significant amount of processing and might hinder real-time applications. In this paper, an end-to-end machine learning model for predicting lane-change maneuvers from unlabeled data using a limited number of features is developed and presented. The model is built on a novel comprehensive dataset (i.e., highD) obtained from German highways with camera-equipped drones. Density-based clustering is used to identify lane-changing and lane-keeping maneuvers and a support vector machine (SVM) model is then trained to learn the boundaries of the clustered labels and automatically label the new raw data. The labeled data are then input to a long short-term memory (LSTM) model which is used to predict maneuver class. The classification results show that lane changes can efficiently be predicted in real-time, with an average detection time of at least 3 s with a small percentage of false alarms. The utilization of unlabeled data and vehicle characteristics as features increases the prospects of transferability of the approach and its practical application for highway safety.

scholarly journals Vehicle Trajectory Prediction with Lane Stream Attention-Based LSTMs and Road Geometry Linearization

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8152
Author(s):  
Dongyeon Yu ◽  
Honggyu Lee ◽  
Taehoon Kim ◽  
Sung-Ho Hwang
Keyword(s):  
Autonomous Vehicles ◽  
Large Scale ◽  
Driving Simulator ◽  
Short Term Memory ◽  
Attention Mechanism ◽  
Trajectory Prediction ◽  
The Road ◽  
Road Geometry ◽  
Vehicle Trajectory ◽  
Road Data

It is essential for autonomous vehicles at level 3 or higher to have the ability to predict the trajectories of surrounding vehicles to safely and effectively plan and drive along trajectories in complex traffic situations. However, predicting the future behavior of vehicles is a challenging issue because traffic vehicles each have different drivers with different driving tendencies and intentions and they interact with each other. This paper presents a Long Short-Term Memory (LSTM) encoder–decoder model that utilizes an attention mechanism that focuses on certain information to predict vehicles’ trajectories. The proposed model was trained using the Highway Drone (HighD) dataset, which is a high-precision, large-scale traffic dataset. We also compared this model to previous studies. Our model effectively predicted future trajectories by using an attention mechanism to manage the importance of the driving flow of the target and adjacent vehicles and the target vehicle’s dynamics in each driving situation. Furthermore, this study presents a method of linearizing the road geometry such that the trajectory prediction model can be used in a variety of road environments. We verified that the road geometry linearization mechanism can improve the trajectory prediction model’s performance on various road environments in a virtual test-driving simulator constructed based on actual road data.

scholarly journals Evaluation of Mixed Deep Neural Networks for Reverberant Speech Enhancement

Biomimetics ◽  
2019 ◽  
Vol 5 (1) ◽  
pp. 1 ◽  
Author(s):  
Michelle Gutiérrez-Muñoz ◽  
Astryd González-Salazar ◽  
Marvin Coto-Jiménez
Keyword(s):  
Neural Networks ◽  
Short Term Memory ◽  
Computational Cost ◽  
Real Life ◽  
Fixed Number ◽  
Training Procedure ◽  
Statistical Validation ◽  
Significant Drop ◽  
Training Time ◽  
Important Solution

Speech signals are degraded in real-life environments, as a product of background noise or other factors. The processing of such signals for voice recognition and voice analysis systems presents important challenges. One of the conditions that make adverse quality difficult to handle in those systems is reverberation, produced by sound wave reflections that travel from the source to the microphone in multiple directions. To enhance signals in such adverse conditions, several deep learning-based methods have been proposed and proven to be effective. Recently, recurrent neural networks, especially those with long short-term memory (LSTM), have presented surprising results in tasks related to time-dependent processing of signals, such as speech. One of the most challenging aspects of LSTM networks is the high computational cost of the training procedure, which has limited extended experimentation in several cases. In this work, we present a proposal to evaluate the hybrid models of neural networks to learn different reverberation conditions without any previous information. The results show that some combinations of LSTM and perceptron layers produce good results in comparison to those from pure LSTM networks, given a fixed number of layers. The evaluation was made based on quality measurements of the signal’s spectrum, the training time of the networks, and statistical validation of results. In total, 120 artificial neural networks of eight different types were trained and compared. The results help to affirm the fact that hybrid networks represent an important solution for speech signal enhancement, given that reduction in training time is on the order of 30%, in processes that can normally take several days or weeks, depending on the amount of data. The results also present advantages in efficiency, but without a significant drop in quality.

scholarly journals Development of an Improved Rapidly Exploring Random Trees Algorithm for Static Obstacle Avoidance in Autonomous Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2244
Author(s):  
S. M. Yang ◽  
Y. A. Lin
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Optimal Path ◽  
Random Trees ◽  
Average Deviation ◽  
Lane Changes ◽  
Path Smoothing ◽  
Rapidly Exploring Random Trees

Safe path planning for obstacle avoidance in autonomous vehicles has been developed. Based on the Rapidly Exploring Random Trees (RRT) algorithm, an improved algorithm integrating path pruning, smoothing, and optimization with geometric collision detection is shown to improve planning efficiency. Path pruning, a prerequisite to path smoothing, is performed to remove the redundant points generated by the random trees for a new path, without colliding with the obstacles. Path smoothing is performed to modify the path so that it becomes continuously differentiable with curvature implementable by the vehicle. Optimization is performed to select a “near”-optimal path of the shortest distance among the feasible paths for motion efficiency. In the experimental verification, both a pure pursuit steering controller and a proportional–integral speed controller are applied to keep an autonomous vehicle tracking the planned path predicted by the improved RRT algorithm. It is shown that the vehicle can successfully track the path efficiently and reach the destination safely, with an average tracking control deviation of 5.2% of the vehicle width. The path planning is also applied to lane changes, and the average deviation from the lane during and after lane changes remains within 8.3% of the vehicle width.

A prediction model of vessel trajectory based on generative adversarial network

2021 ◽  
pp. 1-11
Author(s):  
Senjie Wang ◽  
Zhengwei He
Keyword(s):  
Safety Management ◽  
Route Planning ◽  
Local Area ◽  
Trajectory Data ◽  
Trajectory Prediction ◽  
Generative Adversarial Network ◽  
Ship Traffic ◽  
Adversarial Network ◽  
Historical Trajectory ◽  
Vessel Motion

Abstract Trajectory prediction is an important support for analysing the vessel motion behaviour, judging the vessel traffic risk and collision avoidance route planning of intelligent ships. To improve the accuracy of trajectory prediction in complex situations, a Generative Adversarial Network with Attention Module and Interaction Module (GAN-AI) is proposed to predict the trajectories of multiple vessels. Firstly, GAN-AI can infer all vessels’ future trajectories simultaneously when in the same local area. Secondly, GAN-AI is based on adversarial architecture and trained by competition for better convergence. Thirdly, an interactive module is designed to extract the group motion features of the multiple vessels, to achieve better performance at the ship encounter situations. GAN-AI has been tested on the historical trajectory data of Zhoushan port in China; the experimental results show that the GAN-AI model improves the prediction accuracy by 20%, 24% and 72% compared with sequence to sequence (seq2seq), plain GAN, and the Kalman model. It is of great significance to improve the safety management level of the vessel traffic service system and judge the degree of ship traffic risk.

scholarly journals A Novel LSTM Model with Interaction Dual Attention for Radar Echo Extrapolation

2021 ◽  
Vol 13 (2) ◽  
pp. 164
Author(s):  
Chuyao Luo ◽  
Xutao Li ◽  
Yongliang Wen ◽  
Yunming Ye ◽  
Xiaofeng Zhang
Keyword(s):  
Short Term Memory ◽  
Weather Forecast ◽  
Vital Role ◽  
Data Sets ◽  
Short Term ◽  
Learning Techniques ◽  
Radar Echo ◽  
Hidden States ◽  
Better Than

The task of precipitation nowcasting is significant in the operational weather forecast. The radar echo map extrapolation plays a vital role in this task. Recently, deep learning techniques such as Convolutional Recurrent Neural Network (ConvRNN) models have been designed to solve the task. These models, albeit performing much better than conventional optical flow based approaches, suffer from a common problem of underestimating the high echo value parts. The drawback is fatal to precipitation nowcasting, as the parts often lead to heavy rains that may cause natural disasters. In this paper, we propose a novel interaction dual attention long short-term memory (IDA-LSTM) model to address the drawback. In the method, an interaction framework is developed for the ConvRNN unit to fully exploit the short-term context information by constructing a serial of coupled convolutions on the input and hidden states. Moreover, a dual attention mechanism on channels and positions is developed to recall the forgotten information in the long term. Comprehensive experiments have been conducted on CIKM AnalytiCup 2017 data sets, and the results show the effectiveness of the IDA-LSTM in addressing the underestimation drawback. The extrapolation performance of IDA-LSTM is superior to that of the state-of-the-art methods.

Long Short-Term Memory-Based Human-Driven Vehicle Longitudinal Trajectory Prediction in a Connected and Autonomous Vehicle Environment

2021 ◽  
pp. 036119812199347
Author(s):  
Lei Lin ◽  
Siyuan Gong ◽  
Srinivas Peeta ◽  
Xia Wu
Keyword(s):  
Traffic Flow ◽  
Short Term Memory ◽  
Road Traffic ◽  
Autonomous Vehicle ◽  
Trajectory Data ◽  
Road Transportation ◽  
Trajectory Prediction ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

The advent of connected and autonomous vehicles (CAVs) will change driving behavior and travel environment, and provide opportunities for safer, smoother, and smarter road transportation. During the transition from the current human-driven vehicles (HDVs) to a fully CAV traffic environment, the road traffic will consist of a “mixed” traffic flow of HDVs and CAVs. Equipped with multiple sensors and vehicle-to-vehicle communications, a CAV can track surrounding HDVs and receive trajectory data of other CAVs in communication range. These trajectory data can be leveraged with recent advances in deep learning methods to potentially predict the trajectories of a target HDV. Based on these predictions, CAVs can react to circumvent or mitigate traffic flow oscillations and accidents. This study develops attention-based long short-term memory (LSTM) models for HDV longitudinal trajectory prediction in a mixed flow environment. The model and a few other LSTM variants are tested on the Next Generation Simulation US 101 dataset with different CAV market penetration rates (MPRs). Results illustrate that LSTM models that utilize historical trajectories from surrounding CAVs perform much better than those that ignore information even when the MPR is as low as 0.2. The attention-based LSTM models can provide more accurate multi-step longitudinal trajectory predictions. Further, grid-level average attention weight analysis is conducted and the CAVs with higher impact on the target HDV’s future trajectories are identified.

scholarly journals Evaluation of Mixed Deep Neural Networks for Reverberant Speech Enhancement

2019 ◽  
Author(s):  
Michelle Gutiérrez-Muñoz ◽  
Astryd González-Salazar ◽  
Marvin Coto-Jiménez
Keyword(s):  
Neural Networks ◽  
Short Term Memory ◽  
Computational Cost ◽  
Real Life ◽  
Fixed Number ◽  
Training Procedure ◽  
Statistical Validation ◽  
Training Time ◽  
Important Solution ◽  
Reverberant Speech

Speech signals are degraded in real-life environments, product of background noise or other factors. The processing of such signals for voice recognition and voice analysis systems presents important challenges. One of the conditions that make adverse quality difficult to handle in those systems is reverberation, produced by sound wave reflections that travel from the source to the microphone in multiple directions.To enhance signals in such adverse conditions, several deep learning-based methods have been proposed and proven to be effective. Recently, recurrent neural networks, especially those with long and short-term memory (LSTM), have presented surprising results in tasks related to time-dependent processing of signals, such as speech. One of the most challenging aspects of LSTM networks is the high computational cost of the training procedure, which has limited extended experimentation in several cases. In this work, we present a proposal to evaluate the hybrid models of neural networks to learn different reverberation conditions without any previous information. The results show that some combination of LSTM and perceptron layers produce good results in comparison to those from pure LSTM networks, given a fixed number of layers. The evaluation has been made based on quality measurements of the signal's spectrum, training time of the networks and statistical validation of results. Results help to affirm the fact that hybrid networks represent an important solution for speech signal enhancement, with advantages in efficiency, but without a significan drop in quality.

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