An Integrated Approach to Probabilistic Vehicle Trajectory Prediction via Driver Characteristic and Intention Estimation

Liu, Jinxin; Luo, Yugong; Xiong, Hui; Wang, Tinghan; Huang, Heye; Zhong, Zhihua

doi:10.1109/itsc.2019.8917039

Cited by 25 publications

(15 citation statements)

References 21 publications

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“…Then, a Gaussian process is exploited to predict trajectory based on predicted LC manoeuvre. The authors of [22] reported achieving 95% accuracy in LC prediction; however, the prediction horizon for this performance has not been reported. The advantage of graphical models lies in their ability to interpret the model's prediction by examining the values of the graph nodes.…”

Section: B Prediction Modelmentioning

confidence: 98%

“…Since exact inference is intractable in DBNs, time-series information is not considered in the inference process. A three-layer DBN has been used in [22] to estimate the driver LC intention and driver characteristic. Then, a Gaussian process is exploited to predict trajectory based on predicted LC manoeuvre.…”

Section: B Prediction Modelmentioning

confidence: 99%

“…There is an arbitrary number of surrounding vehicles (SVs) driving in the vicinity of the TV. Similar to most existing studies on LC prediction [4], [7], [10], [11], [16], [19], [22], [25], we assume a BEV camera installed on an infrastructure building or a drone is observing the driving environment, detecting the vehicles, and tracking them while driving on a road section. The vehicles' tracking history, as well as the position of road markings, are assumed to be shared with the EV for the problem of LC prediction.…”

Section: System Model and Problem Definitionmentioning

confidence: 99%

See 2 more Smart Citations

Early Lane Change Prediction for Automated Driving Systems Using Multi-Task Attention-based Convolutional Neural Networks

Mozaffari,

Arnold,

Dianati

et al. 2021

Preprint

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Section: B Prediction Modelmentioning

confidence: 98%

Section: B Prediction Modelmentioning

confidence: 99%

Section: System Model and Problem Definitionmentioning

confidence: 99%

See 1 more Smart Citation

Early Lane Change Prediction for Automated Driving Systems Using Multi-Task Attention-based Convolutional Neural Networks

Mozaffari,

Arnold,

Dianati

et al. 2021

Preprint

View full text Add to dashboard Cite

“…In turn, Liu et al [62] presented a Driver Characteristic and Intention Estimation (DCIE) using Dynamic Bayesian Network (DBN) and vehicle's trajectory prediction using Gaussian Process (GP). The DCIE method is able to estimate the driver intention and also the driving style (i.e.…”

Section: Probabilistic Modelsmentioning

confidence: 99%

A Review on Intention-aware and Interaction-aware Trajectory Prediction for Autonomous Vehicles

Gomes¹,

Wolf²

2022

Preprint

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This paper presents a literature review on Intention-aware and Interaction-aware Trajectory Prediction for Autonomous Vehicle, which covers primary studies since 2008. The research questions try to understand how intention/interaction improves prediction, and what are the techniques and technologies employed in trajectory prediction.<div>Moreover, this paper is under review in the IEEE Transactions on Intelligent Transportation Systems.</div>

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“…However, only longitudinal motion was predicted, and NGSIM data were utilized. Liu et al estimated the driving style using a dynamic Bayesian network and predicted the trajectory using a Gaussian process model [13], [14]. The limitation is that they used a naturalistic vehicle trajectory data set called highD [15] which is similar to NGSIM.…”

Section: Introductionmentioning

confidence: 99%

Driving Style-Based Conditional Variational Autoencoder for Prediction of Ego Vehicle Trajectory

Kim¹,

Shon

Kweon

et al. 2021

IEEE Access

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Trajectory prediction of the ego vehicle is essential for advanced driver assistance systems to function properly. By recognizing various driving styles and predicting trajectories reflecting them, the prediction performance is enhanced, and a personalized trajectory can be generated. Therefore, we propose to combine driving style recognition and trajectory prediction tasks using only in-vehicle CAN-bus sensor data for possible application to normal vehicles. The DeepConvLstm network was utilized for driving style recognition, and a generative-based model was used for trajectory prediction. The classified driving style was added as a condition to the network. In addition, the past trajectory of the ego vehicle is estimated and utilized as an additional input for performance improvement. The performance of the proposed method is analyzed in terms of the root mean squared error (RMSE) and mean absolute error (MAE) compared with the case wherein the driving style and the past trajectory are not conditioned or given, respectively. The results demonstrate the effectiveness of the proposed method.

show abstract

An Integrated Approach to Probabilistic Vehicle Trajectory Prediction via Driver Characteristic and Intention Estimation

Cited by 25 publications

References 21 publications

Early Lane Change Prediction for Automated Driving Systems Using Multi-Task Attention-based Convolutional Neural Networks

Early Lane Change Prediction for Automated Driving Systems Using Multi-Task Attention-based Convolutional Neural Networks

A Review on Intention-aware and Interaction-aware Trajectory Prediction for Autonomous Vehicles

Driving Style-Based Conditional Variational Autoencoder for Prediction of Ego Vehicle Trajectory

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