Vehicle trajectory prediction based on Hidden Markov Model

Ye, Ning; Zhang, Yingya; Wang, Ruchuan; Malekian, Reza

doi:10.3837/tiis.2016.07.016

Cited by 19 publications

(5 citation statements)

References 16 publications

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“…The driving route prediction method proposed by Ning Ye et al [25] is based on the Hidden Markov Model (HMM), enabling accurate anticipation of the complete vehicle journey at the earliest possible stage. Subsequently, a novel algorithm for predicting vehicle trajectories was introduced [26], which utilizes a double-layer hidden state Hidden Markov Model to forecast position information for the nearest neighbor units in the subsequent k stages.…”

Section: Vehicle Trajectory Prediction Based On Statistical Modelmentioning

confidence: 99%

Expressway Vehicle Trajectory Prediction Considering Historical Path Dependencies

Lai,

Xu,

Zou

et al. 2024

Sustainability

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The prediction of expressway vehicle trajectories is a crucial aspect in the development of intelligent expressways. This paper proposes a novel approach, namely the W-GRU-Attention (WGA) model, which utilizes ETC transaction data to predict trajectory selection based on historical traffic paths and previous passed gantry information. In this study, we apply the concept of word embedding models to extract contextual semantics from the historical trajectories on expressways. Additionally, we introduce an average pooling technique for converting the historical vehicle trajectory into a fixed-length Historical Trajectory Vector (HTV), enabling us to capture dependency relationships within experience paths. By combining proximity gantry vectors during transit, we accurately predict the next gantry location. Finally, our proposed method is evaluated using a real-world expressway ETC dataset. It achieves an impressive accuracy rate of 96.14% in capturing the relationship between historical trajectories and adjacent gantries, surpassing other models in path prediction.

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Section: Vehicle Trajectory Prediction Based On Statistical Modelmentioning

confidence: 99%

Expressway Vehicle Trajectory Prediction Considering Historical Path Dependencies

Lai,

Xu,

Zou

et al. 2024

Sustainability

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show abstract

“…The model-driven methods include the hidden Markov model (HMM), Gaussian mixture model (GMM), vehicle dynamics model (VDM), and polynomial model (PM). Ye et al [14] proposed a novel vehicle trajectory prediction algorithm named double hidden Markov of trajectory prediction (DHMTP). The algorithm was based on a hidden Markov model with double hidden states and predicted the vehicle trajectory at multiple subsequent moments.…”

Section: Related Workmentioning

confidence: 99%

A Framework for Trajectory Prediction of Preceding Target Vehicles in Urban Scenario Using Multi-Sensor Fusion

Zou

Hou

et al. 2022

Sensors

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Preceding vehicles have a significant impact on the safety of the vehicle, whether or not it has the same driving direction as an ego-vehicle. Reliable trajectory prediction of preceding vehicles is crucial for making safer planning. In this paper, we propose a framework for trajectory prediction of preceding target vehicles in an urban scenario using multi-sensor fusion. First, the preceding target vehicles historical trajectory is acquired using LIDAR, camera, and combined inertial navigation system fusion in the dynamic scene. Next, the Savitzky–Golay filter is taken to smooth the vehicle trajectory. Then, two transformer-based networks are built to predict preceding target vehicles’ future trajectory, which are the traditional transformer and the cluster-based transformer. In a traditional transformer, preceding target vehicles trajectories are predicted using velocities in the X-axis and Y-axis. In the cluster-based transformer, the k-means algorithm and transformer are combined to predict trajectory in a high-dimensional space based on classification. Driving data from the real-world environment in Wuhan, China, are collected to train and validate the proposed preceding target vehicles trajectory prediction algorithm in the experiments. The result of the performance analysis confirms that the proposed two transformers methods can effectively predict the trajectory using multi-sensor fusion and cluster-based transformer method can achieve better performance than the traditional transformer.

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“…ML methods can predict vehicle trajectory with a large amount of traffic data through the developed data acquisition technologies such as GPS and roadside cameras. Some ML approaches have already been applied for trajectory prediction, including hidden Markov models [9][10][11], Gaussian process regression models [12,13], Bayesian networks [14], Support Vector Machine [15][16][17], and Long Short-Term Memory [18][19][20][21][22].…”

Section: Related Researchmentioning

confidence: 99%

Extreme Gradient Boosting (XGBoost) Model for Vehicle Trajectory Prediction in Connected and Autonomous Vehicle Environment

Liu

Fan

2021

PROMET

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Connected and autonomous vehicles (CAVs) have the ability to receive information on their leading vehicles through multiple sensors and vehicle-to-vehicle (V2V) technology and then predict their future behaviour thus to improve roadway safety and mobility. This study presents an innovative algorithm for connected and autonomous vehicles to determine their trajectory considering surrounding vehicles. For the first time, the XGBoost model is developed to predict the acceleration rate that the object vehicle should take based on the current status of both the object vehicle and its leading vehicle. Next Generation Simulation (NGSIM) datasets are utilised for training the proposed model. The XGBoost model is compared with the Intelligent Driver Model (IDM), which is a prior state-of-the-art model. Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are applied to evaluate the two models. The results show that the XGBoost model outperforms the IDM in terms of prediction errors. The analysis of the feature importance reveals that the longitudinal position has the greatest influence on vehicle trajectory prediction results.

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Vehicle trajectory prediction based on Hidden Markov Model

Cited by 19 publications

References 16 publications

Expressway Vehicle Trajectory Prediction Considering Historical Path Dependencies

Expressway Vehicle Trajectory Prediction Considering Historical Path Dependencies

A Framework for Trajectory Prediction of Preceding Target Vehicles in Urban Scenario Using Multi-Sensor Fusion

Extreme Gradient Boosting (XGBoost) Model for Vehicle Trajectory Prediction in Connected and Autonomous Vehicle Environment

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