Lane changing behavior recognition based on Artificial Neural Network-based State Machine approach

David, Ruth; Rothe, Sandra; Söffker, Dirk

doi:10.1109/itsc48978.2021.9564919

Cited by 3 publications

(6 citation statements)

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“…One of the benefits of using a state machine model is its easy designing process and flexibility [16]. Another advantage includes its easy state reachability as the states can be defined finitely [17] various areas, such as in tribology experiments to develop a lifetime model based on acoustic emission data [18] and in driving behavior experiments to develop driving behavior recognition models [13], [15]. Typical state machine models are developed in [18] and [13] whereby, the conditions for state transitions are based on threshold limits of certain variables.…”

Section: Methodology a State Machine Modelmentioning

confidence: 99%

“…The transition conditions differ from [13] which uses threshold conditions instead. The structure of this model is similar to the ANN-based state machine model [15], which uses the ANN estimations instead as the transition or remaining conditions. Driving decisions depend on environmental variables as well as individual driving behaviors.…”

Section: Hmm-based State Machine Modelmentioning

confidence: 99%

“…Therefore, the parameters are determined such that the model achieves in parallel high ACC, DR, and low FAR. Similar objective functions as in [13] and [15] are chosen for the NSGA-II. As objective functions This article has been accepted for publication in IEEE Access.…”

Section: B Optimizationmentioning

confidence: 99%

“…Comparisons between the proposed approach with an improved HMM approach and an ANN-based state machine approach are also part of the evaluation process. The ANNbased state machine approach is developed in [15], while the HMM is based on [6]. The prefilter thresholds are optimized as well in the improved HMM, while the ANN-based state machine use biases and weights (as ANN parameters) defined by optimization to develop estimations.…”

Section: B Comparisons With Other Approachesmentioning

confidence: 99%

“…The aim is also to test the generability of the model such that same parameter values can also be used to obtain optimal estimations for different drivers. In addition, comparisons between the proposed model, an individual improved HMM model [6], and a previously developed ANNbased state machine model [15] are performed to evaluate the effectiveness of the new model. This paper is organized as follows: in Section II the methodology of the state machine model, HMM, and improved HMM are described.…”

Section: Introductionmentioning

confidence: 99%

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A Study on a HMM-Based State Machine Approach for Lane Changing Behavior Recognition

David

Söffker

2022

IEEE Access

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In recent years, the development of advanced driving assistance systems (ADAS) has grown significantly within the transportation industry to assist drivers for making safe maneuvers. A major component in developing these assistance systems are driving behavior prediction and recognition models. These models aim to infer driving behaviors based on different sources and parameters using complex mathematical models. Machine learning algorithms are being used increasingly to develop these models. In this contribution, two formerly developed trainable models, which are an improved Hidden Markov Model (HMM) and a state machine model, are combined for the recognition of three lane changing behaviors (lane change to the right (LCR), lane keeping (LK), and lane change to the left (LCL). In the improved HMM, a prefilter is implemented on two sets of observation variables (input variables of HMM): one consisting of distances and velocity deviation, while the other consists of time to collision (TTC) variables. To develop an optimal model, thresholds of the prefilter are optimized using a Non-Dominated Sorting Genetic-Algorithm-II. The aim is to investigate if the proposed model is able to produce estimations with high accuracy (ACC), detection rates (DR), and low false alarm rates (FAR). In addition, the performance based on applying the prefilter on the two sets of variables are compared. Comparisons to an individual improved HMM and an ANN-based state machine approach are also addressed. The obtained results show that the application of prefilter on the TTC variables improves the estimation performance. Furthermore, the proposed approach outperforms other approaches.INDEX TERMS Advanced driving assistance systems, Hidden Markov Models, state machine model, prefilter, lane changing behaviors recognition

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Section: Methodology a State Machine Modelmentioning

confidence: 99%

Section: Hmm-based State Machine Modelmentioning

confidence: 99%

Section: B Optimizationmentioning

confidence: 99%

Section: B Comparisons With Other Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Study on a HMM-Based State Machine Approach for Lane Changing Behavior Recognition

David

Söffker

2022

IEEE Access

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Effect of environmental and eye-tracking information: An Artificial Neural Network-based state machine approach for human driver intention recognition

David

Söffker

2022

2022 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)

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State Machine-Based Approach for Estimating the Capacity Loss of Lithium-Ion Batteries

David,

Söffker

2023

PHM_CONF

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The use of Lithium-Ion Batteries (LIBs) have increased in recent years in many applications such as hybrid electrical vehicles (HEV), consumer electronic equipment, and electricity grid. The batteries undergo degradation during usage due to material aging and electrochemical processes, leading to efficiency reduction of battery-powered systems as well as catastrophic events. Several stress factors such as battery temperature, ambient temperature, and C-rate in the loading profiles influence the degradation. Therefore, predicting the health of the battery has gained attention. The service life can be extended or a system failure can be avoided by maintenance measures precisely matched to the function loss or by changing usage strategies. The State-of Health (SoH) condition of the battery can be determined by the application of lifetime models. Various health indicators such as remaining useful lifetime (RuL) and capacity fade are determined by the models based on the stress factors (utilization variables). For optimal use of the battery, it is helpful to develop an accurate lifetime model to represent the dynamic properties. However, models developed are less computationally efficient and unable to represent the non-linear degradation behavior well. The development of a precise model with correct parameterization is also costly. This is particularly true for models developed based on physical and chemical properties of the battery. In this contribution, an artificial neural network (ANN)-based state machine approach is introduced for capacity fade estimation. The degradation process is represented using three states modeling three different levels and the progression from the first state to the last. Capacity associated with each state is described using the non-linear auto regressive neural network with external input (NARX). The NARX is selected due to its ability to accurately model non linear behavior and time series data. Unlike known models, which are developed using analytical mathematical equations related to the battery properties, a combined machine learning approach is used here instead to learn the capacity behavior from historical data. Battery data sets from NASA are used for experimental verification. Based on the results, the estimated capacity fade show close proximity to actual capacity fade, with a low mean square error for different data sets. In addition, the estimated state progression follows the actual state progression.

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Lane changing behavior recognition based on Artificial Neural Network-based State Machine approach

Cited by 3 publications

References 10 publications

A Study on a HMM-Based State Machine Approach for Lane Changing Behavior Recognition

A Study on a HMM-Based State Machine Approach for Lane Changing Behavior Recognition

Effect of environmental and eye-tracking information: An Artificial Neural Network-based state machine approach for human driver intention recognition

State Machine-Based Approach for Estimating the Capacity Loss of Lithium-Ion Batteries

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