Gauss mixture hidden Markov model to characterise and model discretionary lane‐change behaviours for autonomous vehicles

Jiang, Hao; Duan, Chunguang; Liu, Yang; Lu, Pingping

doi:10.1049/iet-its.2019.0446

Cited by 28 publications

(15 citation statements)

References 23 publications

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“…The input parameters are distances and speed differences with the surrounding vehicles. Many authors used hidden Markov models and naïve Bayes classifiers to predict the lane changes few seconds before they occur [46,49,50,51,52]. Generally speaking, non-linear algorithms by artificial neural networks [53,54,55] and especially deep learning including feedback mechanisms (e.g., long short term memory (LSTM) algorithm) [19,21,56,57,58] provide the most accurate predictions.…”

Section: Data-based Algorithmsmentioning

confidence: 99%

Predicting highway lane-changing maneuvers: A benchmark analysis of machine and ensemble learning algorithms

Khelfa¹,

Ba²,

Tordeux³

2022

Preprint

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Understanding and predicting highway lane-change maneuvers is essential for driving modeling and its automation. The development of data-based lane-changing decision-making algorithms is nowadays in full expansion. We compare empirically in this article different machine and ensemble learning classification techniques to the MOBIL rule-based model using trajectory data of European two-lane highways. The analysis relies on instantaneous measurements of up to twenty-four spatial-temporal variables with the four neighboring vehicles on current and adjacent lanes. Preliminary descriptive investigations by principal component and logistic analyses allow identifying main variables intending a driver to change lanes. We predict two types of discretionary lanechange maneuvers: Overtaking (from slow to fast lane) and fold-down (from fast to slow lane). The prediction accuracy is quantified using total, lane-changing and lane-keeping errors and associated receiver operating characteristic curves. The benchmark analysis includes logistic model, linear discriminant, decision tree, naïve Bayes classifier, support vector machine, neural network machine learning algorithms, and up to ten bagging and stacking ensemble learning meta-heuristics. If the rule-based model provides limited predicting accuracy, especially in case of fold-down, the data-based algorithms, devoid of modeling bias, allow significant prediction improvements. Cross validations show that selected neural networks and stacking algorithms allow predicting from a single observation both fold-down and overtaking maneuvers up to four seconds in advance with high accuracy.

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Section: Data-based Algorithmsmentioning

confidence: 99%

Predicting highway lane-changing maneuvers: A benchmark analysis of machine and ensemble learning algorithms

Khelfa¹,

Ba²,

Tordeux³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Kamrani et al [12] used high-frequency and diverse driving data to study the generation of driving decisions and analyzed drivers' willingness to maintain constant speed, acceleration, and deceleration. Jin et al [13] proposed a lane-changing behavior decision-making model based on the Gauss mixture hidden Markov model (GM-HMM), and through data verification, the similarity between this model and the driver's real behavior was found to reach 95.4%. In addition, many other studies have used the HMM to solve traffic problems, such as accident detection [14], prediction of braking behavior [15], trajectory map matching [16], and prediction of traffic conditions [17].…”

Section: Literature Reviewmentioning

confidence: 99%

Lane-Changing Behavior Prediction Based on Game Theory and Deep Learning

Jia

Hui

Wei

et al. 2021

Journal of Advanced Transportation

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Lane changing is an important scenario in traffic environments, and accurate prediction of lane-changing behavior is essential to ensure traffic and driver safety. To achieve this goal, a vehicle lane-changing prediction model based on game theory and deep learning is developed. In the game theory component, the interaction between vehicles during lane changing is analyzed according to the running state of the vehicle, with the probability of lane changing as its output. For the deep-learning component, long short-term memory and a convolutional neural network are used to extract and learn data features during the lane-changing process as well as combine the output of the game theory component to obtain the prediction result of whether the vehicle will change lanes. By using an open-source traffic dataset to train and verify the proposed model, the verification results show that the prediction accuracy can reach 94.56% within 0.4 s of lane-changing operation and that the model can achieve timely and accurate prediction of the lane-changing behavior of vehicles.

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“…Gao et al introduce a lane-change behavior detection approach using multiple differing modality data [ 20 ]. Jin et al present an optimal lane-change timing prediction model based on the driver's habits [ 21 ]. Huang et al present a trajectory planning and control approach based on user preferences [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Intelligent Approach to Lane-Change Behavior Prediction for Intelligent and Connected Vehicles

Chen

et al. 2022

Computational Intelligence and Neuroscience

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The prediction of lane-change behavior is a challenging issue in intelligent and connected vehicles (ICVs), which can help vehicles predict in advance and change lanes safely. In this paper, a novel intelligent approach, which considering both the driving style-based lane-change environment and the driving trajectory-related parameters of the ICV and surrounding vehicles, is proposed to predict the lane-change behaviors for ICVs. By analyzing the characteristics of the lane-change behavior of the vehicle, a modified dataset for the prediction of lane-change behavior was established based on the Next-Generation Simulation (NGSIM) dataset, which is made up of real vehicle trajectories collected by camera. In the proposed approach, the hidden Markov model (HMM)-based model is designed to judge whether the current environment is suitable for lane change according to the driving environment parameters around the vehicle; then according to the driving state of the vehicle, a learning-based prediction-then-judgment model is proposed and designed to realize the prediction of the ICV’s lane-change behavior. Experiments are implemented by using the modified dataset. From the experimental results, the lane-change probability value on the target lane in the truth of the lane-change behavior calculated by the designed HMM-based model is basically above 0.5, indicating that the model can make a more accurate judgment on the surrounding lane-change environment. The proposed learning-based prediction-then-judgment model has an accuracy of 99.32% for the prediction of lane-change behavior, and the accuracy of the lane-change detection algorithm in the model is 99.56%. The experimental results show that the proposed approach has a good performance in the prediction of lane-change behavior, which could effectively assist ICVs to change lanes safely.

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Gauss mixture hidden Markov model to characterise and model discretionary lane‐change behaviours for autonomous vehicles

Cited by 28 publications

References 23 publications

Predicting highway lane-changing maneuvers: A benchmark analysis of machine and ensemble learning algorithms

Predicting highway lane-changing maneuvers: A benchmark analysis of machine and ensemble learning algorithms

Lane-Changing Behavior Prediction Based on Game Theory and Deep Learning

A Novel Intelligent Approach to Lane-Change Behavior Prediction for Intelligent and Connected Vehicles

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