Driver behavior modeling near intersections using Hidden Markov Model based on genetic algorithm

Amsalu, Seifemichael B.; Homaifar, Abdollah

doi:10.1109/icite.2016.7581332

Cited by 20 publications

(16 citation statements)

References 20 publications

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“…For estimating the intention at a road intersection, two algorithms based on the hybrid state framework are proposed: one is the HMM optimized by genetic algorithm (GA), and the other one is the discrete HMM method. The results show that the accuracy of these two methods is 89.45% and 97%, respectively [20], [21]. Actually, it is challenging to identify some confusing intention as it considers only state sequences with the greatest log likelihood and ignores other state sequences.…”

Section: Introductionmentioning

confidence: 99%

Driving Intention Identification Based on Long Short-Term Memory and A Case Study in Shifting Strategy Optimization

et al. 2019

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Identification of driving intentions has increasingly attracted wide attention since it can be a valuable reference input of vehicle intelligent control systems. In this study, the long short-term memory (LSTM) is employed to identify the longitudinal intention online with high precision. To this end, the driving intentions when the vehicle runs on a straight and flat road are divided into five categories. The vehicle driving states such as the vehicle speed and acceleration are pre-processed to label the road test data. Subsequently, a LSTM classification model is established to identify the driving intention with inputs of opening degree of the accelerator pedal, vehicle speed and brake pedal force. Identification results reveal that the highest accuracy of the proposed algorithm attains 95.36%, which is around 20% higher than that of the traditional back propagation neural network. Finally, a driving intention-perceptive gear shifting strategy is developed with the help of the built recognition algorithm, and simulation results highlight that the strategy can effectively reduce the number of shifts and achieve better fuel economy.INDEX TERMS Driving intention, long short-term memory (LSTM), back propagation neural network (BPNN), dual clutch transmission (DCT), shift strategy, fuel economy.

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Section: Introductionmentioning

confidence: 99%

Driving Intention Identification Based on Long Short-Term Memory and A Case Study in Shifting Strategy Optimization

et al. 2019

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“…The problem of driving behavior recognition/inference/estimation is transformed into a problem of classification, recognition, or prediction of a time series. Many studies have used machine learning algorithms to analyze driving behavior, such as a continuous hidden Markov model (CHMM), Gaussian mixture model (GMM) [ 24 , 25 , 26 , 27 , 28 , 29 ], support vector machine (SVM) [ 30 , 31 ], back-propagation (BP) neural network [ 18 , 32 ], random forest, and Adaboost algorithm [ 33 ]. Specifically, in [ 24 , 27 ], an algorithm combining an HMM and Bayesian filtering (BF) was proposed to model the vehicle behavior while entering the intersection and performing a lane change.…”

Section: Introductionmentioning

confidence: 99%

“…The authors used public data and highway driving as a scenario to recognize and predict the lane change behavior of the target vehicle from the perspective of the host vehicle. In [ 25 , 29 ], a hybrid-state system (HSS) and HMM framework were integrated to model vehicle turning behavior at an intersection. In the HSS, the driver’s decision was modeled as a discrete state system, and the vehicle dynamics were modeled as a continuous state system.…”

Section: Introductionmentioning

confidence: 99%

“…Most of the above studies were conducted from the perspective of the host vehicle, and the results may not be applicable to the behavior prediction and recognition of surrounding vehicles because the eye movement and facial tracking data of the drivers of the surrounding vehicles cannot be obtained [ 44 , 45 ]. In addition, most research on driving behavior recognition at intersections recognizes turning behaviors in the early stages after the maneuver has started [ 24 , 25 , 29 , 31 ]. Whereas references [ 15 , 38 , 39 ], and [ 40 ] claim to achieve intent prediction, ADAS or autonomous vehicles cannot record extensive historical data of the target vehicle to achieve intent prediction.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Approach for Turning Intention Prediction Based on Time Series Forecasting and Deep Learning

Zhang

2020

Sensors

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At an intersection with complex traffic flow, the early detection of the intention of drivers in surrounding vehicles can enable advanced driver assistance systems (ADAS) to warn the driver in advance or prompt its subsystems to assess the risk and intervene early. Although different drivers show various driving characteristics, the kinematic parameters of human-driven vehicles can be used as a predictor for predicting the driver’s intention within a short time. In this paper, we propose a new hybrid approach for vehicle behavior recognition at intersections based on time series prediction and deep learning networks. First, the lateral position, longitudinal position, speed, and acceleration of the vehicle are predicted using the online autoregressive integrated moving average (ARIMA) algorithm. Next, a variant of the long short-term memory network, called the bidirectional long short-term memory (Bi-LSTM) network, is used to detect the vehicle’s turning behavior using the predicted parameters, as well as the derived parameters, i.e., the lateral velocity, lateral acceleration, and heading angle. The validity of the proposed method is verified at real intersections using the public driving data of the next generation simulation (NGSIM) project. The results of the turning behavior detection show that the proposed hybrid approach exhibits significant improvement over a conventional algorithm; the average recognition rates are 94.2% and 93.5% at 2 s and 1 s, respectively, before initiating the turning maneuver.

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“…A set of stochastic processes that produces the sequence of observed symbols is used to infer an underlying stochastic process that is not observable (hidden states). HMMs have been widely utilized in many application areas including speech recognition [1], bioinformatics [2], finance [3], computer vision [4], and driver behavior modeling [5,6]. A comprehensive survey on the applications of HMMs is presented in [7].…”

Section: Introductionmentioning

confidence: 99%

A Simplified Matrix Formulation for Sensitivity Analysis of Hidden Markov Models

2017

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Abstract:In this paper, a new algorithm for sensitivity analysis of discrete hidden Markov models (HMMs) is proposed. Sensitivity analysis is a general technique for investigating the robustness of the output of a system model. Sensitivity analysis of probabilistic networks has recently been studied extensively. This has resulted in the development of mathematical relations between a parameter and an output probability of interest and also methods for establishing the effects of parameter variations on decisions. Sensitivity analysis in HMMs has usually been performed by taking small perturbations in parameter values and re-computing the output probability of interest. As recent studies show, the sensitivity analysis of an HMM can be performed using a functional relationship that describes how an output probability varies as the network's parameters of interest change. To derive this sensitivity function, existing Bayesian network algorithms have been employed for HMMs. These algorithms are computationally inefficient as the length of the observation sequence and the number of parameters increases. In this study, a simplified efficient matrix-based algorithm for computing the coefficients of the sensitivity function for all hidden states and all time steps is proposed and an example is presented.

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Driver behavior modeling near intersections using Hidden Markov Model based on genetic algorithm

Cited by 20 publications

References 20 publications

Driving Intention Identification Based on Long Short-Term Memory and A Case Study in Shifting Strategy Optimization

Driving Intention Identification Based on Long Short-Term Memory and A Case Study in Shifting Strategy Optimization

A Hybrid Approach for Turning Intention Prediction Based on Time Series Forecasting and Deep Learning

A Simplified Matrix Formulation for Sensitivity Analysis of Hidden Markov Models

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