Current research and development in recognizing and predicting driving behaviors plays an important role in the development of Advanced Driver Assistance Systems (ADAS). For this reason, many machine learning approaches have been developed and applied. Hidden Markov Model (HMM) is a suitable algorithm due to its ability to handle time series data and state transition descriptions. Therefore, this contribution will focus on a review of HMM and its applications. The aim of this contribution is to analyze the current state of various driving behavior models and related HMM-based algorithms. By examining the current available approaches, a review is provided with respect to: i) influencing factors of driving behaviors corresponding to the research objectives of different driving models, ii) summarizing HMM related methods applied to driving behavior studies, and iii) discussing limitations, issues, and future potential works of the HMM-based algorithms. Conclusions with respect to the development of intelligent driving assistant system and vehicle dynamics control systems are given.

Advanced Driver Assistance Systems (ADAS) are systems developed to assist the human driver and therefore to make driving safer and better. Understanding and predicting human driving behavior play an important role in the development of assistance systems. In this contribution, the development of a driver assistance system is based on the prediction of driving behaviors. The driving patterns of three different behaviors are modeled including left/right lane change and lane keeping. A driving simulator is used to simulate a highway scene. The implementation of a prediction system based on different machine learning approaches such as Hidden Markov Model (HMM), Support Vector Machine (SVM), Convolutional neural networks (CNNs), and Random Forest (RF) is accomplished. In addition, eye-tracking information is integrated. The task is to predict behaviors based on the measurement. As test, a 10-fold cross-validation is used based on data sets from driving simulator and applied to compare the performance of different algorithms. In combination with related results in terms of accuracy (ACC), detection rate (DR), and false alarm rate (FAR), the performance and effectiveness of the developed prediction systems are evaluated. The results show that the performance of RF algorithm is the best of all four algorithms compared. Combining environmental and eye-tracking data the RF algorithm achieved the best results. All ACC values are larger than 99\ %. Afterwards, two RF-based prediction models with and without eye-tracking data are developed for online test. Finally, some application samples are suggested for driver assistance. The results calculated by the proposed model are shown on a user interface to help the drivers to see when it is suitable to turn left, to turn right, or to keep the direction.

In this research, a new performance assessment based on the Probability of Detection (POD) reliability measure is developed integrating and discussing the effect of further parameters on classification results and therefore establishing a new connection between relevant process parameters and the related classifier evaluation. To illustrate the introduced approach, machine learning-based recognition of complex driving situations for human drivers is interpreted. Based on sensor signals and a complex driving scenario, related dynamical changes are classified and compared using the POD approach. Based on the POD-related evaluation, different machine learning approaches can be clearly distinguished with respect to their ability to predict the correct driver behavior as a function of time prior to the event itself. The introduced approach allows a very detailed comparison of classifiers related to the effects of parameters affecting the processes to be classified. In addition to recently published results on this novel approach, an extension of the POD approach by considering false positives and varying decision threshold in the comparison process is proposed. Generalization of the introduced approach for binary and continuous data is presented.

Deployable autonomy remains a key challenge for unmanned aerial vehicles (UAVs) operating in open-ended missions. Large language models (LLMs) and their multimodal variants, which can process visual and other sensory inputs, have introduced new capabilities for semantic perception, task reasoning, and language-conditioned control. However, these capabilities do not by themselves produce flight-ready autonomy. We structure our analysis around a Perception–Cognition–Action (P–C–A) framework. At each layer, we identify the capabilities contributed by LLM-based components and examine how they connect to existing flight modules through input specifications, output representations, architectural coupling patterns, and safety mechanisms. Across the surveyed systems, LLMs extend UAV autonomy beyond fixed perception categories, scripted task plans, and pre-programmed controllers. However, field deployment depends on whether model outputs can be transformed into representations that downstream modules can parse, verify, and safely execute. Without adequate validation, captions, task plans, code, waypoints, and control commands may become failure points that propagate across the P–C–A loop. Our analysis highlights structured output contracts, independent safety barriers, and deterministic fallback mechanisms as key design elements for the reliable integration of LLM capabilities into UAV platforms.

Accurate trajectory tracking is fundamental to the autonomous operation of unmanned aerial vehicles (UAVs) in complex tasks. While proximal policy optimization (PPO) has shown strong potential in UAV control, its performance is highly sensitive to hyperparameter configuration, and manual tuning is time-consuming due to complex interparameter coupling. This paper proposes CCO–PPO, a framework integrating the cuckoo catfish optimizer (CCO) with PPO for automatic hyperparameter optimization in UAV trajectory tracking. The problem is formulated as a Markov decision process with a 20-dimensional state space, and the CCO performs offline search over a four-dimensional hyperparameter space. Evaluated across seven test environments covering diverse trajectory geometries, wind disturbances, sensor noise, and large-scale scenarios, CCO–PPO achieves the lowest tracking error in all cases. Performance gains over baseline PPO increase monotonically with task complexity, reaching 18.8% under combined wind disturbance and sensor noise, with statistically significant advantages in 85.7% of pairwise comparisons against baseline PPO, SAC, and TD3. Ablation studies confirm that joint optimization of all four hyperparameters is essential under high-disturbance conditions, and comparisons with Bayesian optimization validate the CCO’s superior cross-seed stability. These results demonstrate that metaheuristic hyperparameter optimization substantially enhances policy robustness in high-disturbance UAV trajectory tracking scenarios.