A major aspect in the development of advanced driving assistance systems (ADASs) is the research in developing human driving behavior prediction and recognition models. Recent contributions focus on developing these models for estimating different driving behaviors like lane or speed change. Thus, the models are incorporated into the ADAS to generate warnings and hints for safe maneuvers. Driving behavior recognition and prediction models are generally developed based on machine learning (ML) algorithms and are proven to generate accurate estimations. Previous review research contributions tend to focus on ML-based models for the prediction and recognition of speed change, trajectory change, and even driving styles. Due to high number of driving errors occurring during a lane change, a state-of-art review of different ML-based models for lane-changing behavior prediction and recognition is helpful to present a comparison between different models in terms of structure, influencing input variables, and performance. This enables the integration of the most efficient model for the development of ADASs to avoid accidents during a lane change. First, definitions and terms related to the model’s task and evaluation metrics used to evaluate the model’s performance are described to improve the readability. Then, the different input variables of the models affecting the lane-changing behaviors are presented. Next, a review of the models developed based on well-known approaches, such as artificial neural network (ANN), hidden Markov model (HMM), and support vector machine (SVM), using different input variables is given. Three lane-changing behaviors are focused on here: left/right lane change and lane keeping. The advantages and disadvantages of the different ML models with a comparison are summarized as well. Finally, the improvements required in the future are discussed.

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.

Research in understanding human behavior is a growing field within the development of Advanced Driving Assistance Systems (ADASs). In this contribution, a state machine approach is proposed to develop a driving behavior recognition model. The state machine approach is a behavior model based on the current state and a given set of inputs. Transitions to different states occur or we remain in the same state producing outputs. The transition between states depends on a set of environmental and driving variables. Based on a heuristic understanding of driving situations modeled as states, as well as one of the related actions modeling the state, using an assumed relation between them as the state machine topology, in this paper, a crisp approach is applied to adapt the model to real behaviors. An important aspect of the contribution is to introduce a trainable state machine-based model to describe drivers’ lane changing behavior. Three driving maneuvers are defined as states. The training of the model is related to the definition/tuning of transition variables (and state definitions). Here, driving data are used as the input for training. The non-dominated sorting genetic algorithm II is used to generate the optimized transition threshold. Comparing the data of actual human driving behaviors collected using driving simulator experiments and the calculated driving behaviors, this approach is able to develop a personalized behavior recognition model. The newly established algorithm presents an easy to apply, reliable, and interpretable AI approach.

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.

Driving intention recognition is an important aspect of Advanced Driving Assistance Systems (ADAS) for giving drivers suggestions to maneuver safely. The intention recognition algorithms in ADAS are often developed using Machine Learning-based models. The model's input, such as environmental (ENV) and eye-tracking (ET) features affect the model's recognition performance. In this contribution, an Artificial Neural Network-based state machine is used for lane changing intention recognition. Three lane changing behaviors are considered, left/right lane change and lane keeping. Here, data consisting of ENV and ET information are collected using a driving simulator and eye-tracker. The aim is to investigate the effect of different feature types on the model's intention recognition performance. First, a 10-cross validation is performed to evaluate the model's performance, using only ENV and both ENV and ET features. The validation results show that the model with only ENV features performs better with respect to different metrics. Thus in the test, only ENV features are used to evaluate the performance. Accuracy values of higher than 80 % are achieved. Furthermore, the recognition performance of the model is compared with other Machine Learning models. The approach introduced outperforms other models in most metrics.