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Driver behavior is receiving increasing attention as a result of the staggering number of road accidents. Many road safety reports regard human behavior as the most important factor in the likelihood of accidents. The detection and classification of aggressive or abnormal driver behavior is an essential requirement in the real world to avoid deadly...
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... acceleration applied to a vehicle in all three dimensions (x, y, and z), excluding the force of gravity. Gravity and acceleration inputs from all three axes are required to calculate linear acceleration. In addition to acceleration features, several others are extracted from data sources and used many times in the articles studied. The following Fig. 7 and Fig. 8 show the must-used features that can be extracted from each data source and dataset. From these figures, we can derive features that allow drivers to describe and identify their behavior; furthermore, these features can be exploited to create classification models of driver behavior. Fig. 7 allows us to deduce that: The simulator helps ...
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... times in the articles studied. The following Fig. 7 and Fig. 8 show the must-used features that can be extracted from each data source and dataset. From these figures, we can derive features that allow drivers to describe and identify their behavior; furthermore, these features can be exploited to create classification models of driver behavior. Fig. 7 allows us to deduce that: The simulator helps extract a lot of information, such as acceleration, deceleration, acceleration (lateral and longitudinal), throttle, direction, vehicle position, and vehicle speed. GPS can be used to measure the speed and acceleration of a vehicle. The accelerometer can provide acceleration on three axes, ...
Citations
... The authors in [18] systematically reviewed driver behaviour classification research. The review encompassed various data types, data sets, features, data preprocessing, and AI techniques. ...
The automotive industry is transforming because of the incorporation of cutting-edge technologies like Big Data, deep learning (DL), machine learning (ML), and the Internet of Things (IoT). This is especially true regarding improving driver behaviour analysis and vehicle performance. Road safety and car diagnostics are greatly aided by real-time monitoring and predictive analytics, made possible by connected vehicles’ massive data generation via onboard sensors, IoT devices, and telematics. Previous studies have mainly focused on individual technologies and lacked comprehensive discussions on the integration of generative AI. This paper covers the literature on driving behaviour analysis, generative AI, predictive maintenance and profiling. It emphasizes how well ML and DL models categorize driver behaviour, spot dangerous driving habits, and forecast when a car requires maintenance. Furthermore, the function of Generative AI is examined in terms of giving drivers personalized and dynamic feedback, enhancing overall driving performance, safety, and fuel efficiency. The paper also addresses data privacy challenges, real-time monitoring, and combining various data sources. Emerging trends in hybrid AI models and large language models are discussed as promising directions for improving predictive maintenance systems and optimizing vehicle performance.
... These studies collectively demonstrate how factors like impaired visibility, precipitation, and extreme temperatures significantly influence driver behavior and vehicle handling. For instance, driving simulation experiments indicate that fog reduces both driving speeds and acceleration rates while increasing the distance drivers maintain from the vehicle ahead [35]. Beyond general environmental influences, specific factors such as road surface friction coefficients under varying weather conditions directly impact energy consumption. ...
This paper examines the energy efficiency of smart electric vehicles equipped with regenerative braking systems under challenging weather conditions. While Advanced Driver Assistance Systems (ADAS) are primarily designed to enhance driving safety, they often overlook energy efficiency. This study proposes a Weather-Adaptive Regenerative Braking Strategy (WARBS) system, which leverages onboard sensors and data processing capabilities to enhance the energy efficiency of regenerative braking across diverse weather conditions while minimizing unnecessary alerts. To achieve this, we develop driving style recognition models that integrate road conditions, such as weather and road friction, with different driving styles. Next, we propose an adaptive deceleration plan that aims to maximize the conversion of kinetic energy into electrical energy for the vehicle’s battery under varying weather conditions, considering vehicle dynamics and speed constraints. Given that the potential for energy recovery through regenerative braking is diminished on icy and snowy roads compared to dry ones, our approach introduces a driving context recognition system to facilitate effective speed planning. Both simulation and experimental validation indicate that this approach can significantly enhance overall energy efficiency.
... Driving styles are typically analyzed objectively through direct measurements such as vehicle telemetry data, including speed patterns, acceleration, braking habits, and steering behavior [13,14]. These objective metrics, derived from in-vehicle monitoring systems, provide precise quantifications of driving dynamics, enabling researchers to draw clear correlations between specific driving behaviors and accident rates. ...
... In recent years, growing urbanization and the increasing number of vehicles on the road have introduced new challenges related to transportation safety, efficiency, and sustainability, leading to growing interest in the Internet of Vehicles (IoV) [1]. Within this framework, the monitoring systems of driver, vehicle, and road conditions have become key components of Intelligent Transportation Systems (ITSs) and smart cities [2]. These systems leverage advanced technologies, including sensors, IoT devices, and Artificial Intelligence (AI) to collect, analyze, and utilize real-time data. ...
In recent years, the growing number of vehicles on the road have exacerbated issues related to safety and traffic congestion. However, the advent of the Internet of Vehicles (IoV) holds the potential to transform mobility, enhance traffic management and safety, and create smarter, more interconnected road networks. This paper addresses key road safety concerns, focusing on driver condition detection, vehicle monitoring, and traffic and road management. Specifically, various models proposed in the literature for monitoring the driver’s health and detecting anomalies, drowsiness, and impairment due to alcohol consumption are illustrated. The paper describes vehicle condition monitoring architectures, including diagnostic solutions for identifying anomalies, malfunctions, and instability while driving on slippery or wet roads. It also covers systems for classifying driving style, as well as tire and emissions monitoring. Moreover, the paper provides a detailed overview of the proposed traffic monitoring and management solutions, along with systems for monitoring road and environmental conditions, including the sensors used and the Machine Learning (ML) algorithms implemented. Finally, this review also presents an overview of innovative commercial solutions, illustrating advanced devices for driver monitoring, vehicle condition assessment, and traffic and road management.
... Key elements such as driver error, vehicle overturning, and mechanical issues are consistent with studies that identify these factors as significant contributors to accident severity. Both datasets highlight the substantial impact of human error, reinforcing earlier research that emphasizes the universal influence of driver conduct on road incidents [60][61][62][63][64][65]. Additionally, the increase in crash severity during nighttime driving is supported by road safety trends, where reduced visibility is linked to higher accident risks [66] This study also advances the understanding of vehicle-related factors by identifying technical defects as a major predictor of crash severity in Jordan, which reflects challenges seen in other developing regions where aging vehicle fleets and inadequate maintenance standards prevail. ...
Understanding the cultural and environmental influences on roadway crash patterns is essential for designing effective prevention strategies. This study applies advanced AI techniques, including Bidirectional Encoder Representations from Transformers (BERT) and Shapley Additive Explanations (SHAP), to examine traffic crash patterns in the United States and Jordan. By analyzing tabular data and crash narratives, the research reveals significant regional differences: in the USA, vehicle overturns and roadway conditions, such as guardrails, are major factors in fatal crashes, whereas in Jordan, technical defects and driver behavior play a more critical role. SHAP analysis identifies “driver” and “damage” as pivotal terms across both regions, while country-specific terms such as “overturn” in the USA and “technical” in Jordan highlight regional disparities. Using BERT/Bi-LSTM models, the study achieves up to 99.5% accuracy in crash severity prediction, demonstrating the robustness of AI in traffic safety analysis. These findings underscore the value of contextualized AI-driven insights in developing targeted, region-specific road safety policies and interventions. By bridging the gap between developed and developing country contexts, the study contributes to the global effort to reduce road traffic injuries and fatalities.
... As shown in Figure 8, the determinant coefficient (R 2 ) was achieved at 0.99, meaning that input features can produce a 99% variation in energy consumption. HistGBRT predictive modeling ability in transportation research field and will expand to encompass various new energy vehicles, such as plug-in hybrids, and explore novel machine learning methodologies like Bidirectional Gated Recurrent Unit renowned for their robust predictive capabilities (Kotapati et al. 2023;Abediasl et al. 2023;Bouhsissin's, Sael, and Benabbou 2023). ...
... • Complexity of Driver Behavior: Despite advancements in ITS technology, understanding and predicting driver behavior remains challenging due to the multifaceted nature of human behavior [3]. ...
... It is a multifaceted concept influenced by many factors, rendering its precise description and analysis challenging. It includes different types such as speeding, distracted driving, aggressive driving, drowsy driving, and other categories described in this systematic literature review [3]. ...
The advancement of intelligent transportation systems is crucial for improving road safety and optimizing traffic flow. In this paper, we present SafeSmartDrive, an integrated transportation monitoring system designed to detect and assess critical elements in the driving environment while simultaneously monitoring driver behavior. The system is structured into four key layers: perception, filtering and preparation, detection and classification, and alert. SafeSmartDrive focuses on two primary objectives: (1) detecting and assessing essential traffic elements, including vehicles (buses, cars, motorcycles, trucks, bicycles), traffic signs and lights, pedestrians, animals, infrastructure damage, accident classification, and traffic risk assessment, and (2) evaluating driver behavior across various road types, such as highways, secondary roads, and intersections. Machine learning and deep learning algorithms are employed throughout the system’s components. For traffic element detection, we utilize YOLOv9 in this paper, which outperforms previous versions like YOLOv7 and YOLOv8, achieving a precision of 83.1%. Finally, we present the evaluation of the SafeSmartDrive system’s real-time detection capabilities in a specific scenario in Casablanca. SafeSmartDrive’s comprehensive architecture offers a novel approach to improving road safety through the integration of advanced detection, classification, and risk assessment capabilities.
... Recently, there has been increasing interest in the development of Driver Monitoring Systems (DMS), which are expected to have a high potential in terms of driver's state/behavior estimation and situation awareness assessment [4], [5]. Situation awareness, an overarching research field covering multiple scientific domains (e.g., human factors, system engineering, dynamic systems) and industrial fields (e.g. ...
This paper presents a cognitive model designed to reproduce human drivers’ errors in predicting the motion of nearby vulnerable road users. We aim to define a computational model that, given both the trajectory of the eye gaze of a human driver and the trajectory of a bicycle, can compute the probability distribution of where the human driver believes the bicycle will be in the near future. For the design and validation of the proposed cognitive model, we tested
subjects in immersive virtual reality scenarios. The results indicate that the proposed model can generate probability distributions of the human drivers’ beliefs about the future bicycle position that are very similar, though not statistically equivalent, to those obtained experimentally. Such models could easily be generalized to describe how drivers misjudge the motion of other road users. This may enable ADAS to evaluate and improve drivers’ situational awareness. In the future, these models could also be used by autonomous cars to evaluate situational awareness of nearby humans, enabling a safer coexistence of autonomous vehicles and vulnerable road users.
... Various strategies have been proposed by governmental and social communities as well as by the OEMs (automotive industry) to reduce errors made by human drivers on urban roads and to enhance driving behavior to prevent safetycritical events [1]. However, despite the growth of vehicle automation, the number of car crashes is still unacceptably high. ...
... Deep learning uses artificial neural networks to learn complex patterns and relationships from data, while Bayesian networks are graphical models that mimic the human driving behavior by cognitive hypotheses and represent conditional dependencies between variables [9]. Various schemes have been proposed to predict driving behavior during car-following using machine learning techniques [1][2][3][4][5][6]. It explores various features, such as vehicle dynamics and driver characteristics, and applies classification algorithms to predict driver actions [7]. ...
This paper discusses the impact of Connected Cooperative and Automated Mobility (CCAM) on safety-critical events. The replacement of human drivers by autonomous vehicles (AVs) is promising improved traffic efficiency and reduction of car- crashes to zero using a baseline network traffic. Predicting driving behavior during car-following has been crucial for enhancing road safety while developing advanced driver assistance systems with adaptive cruise control. Human factors significantly influence the driving behavior of a vehicle. Thus, understanding the causal relations between human factors and driving behavior is essential for accurate prediction of vehicle behavior. This is important when autonomous vehicles are expected to behave (cooperatively, according to traffic rules and good praxis) in a human predictable manner, while driving in mixed traffic, involving autonomous, automated, and human driven vehicles. In this paper, we propose a methodology that combines convolutional neural networks (CNNs) with human factors analysis to predict driving behavior during car-following under adverse weather conditions (AWCs).
... On the other hand, driver behavior is a multidimensional concept, influenced by many factors, making its accurate description and analysis challenging [3]. These factors include social, cultural, psychological, and environmental factors [4], along with individual driver attributes [5]. ...
... In the literature, a wide range of machine learning techniques has been investigated and employed for the classification of driver behavior. The classification of driving behavior relies heavily on the utilization of these diverse ML algorithms, as highlighted in the literature [3]. The algorithms utilized in this paper are outlined below. ...
... Moreover, the literature frequently employs certain features in the analysis of driver behavior. Bouhsissin et al. [3], these commonly utilized features include speed, acceleration, accelerator pedal, rotation angle, lateral and longitudinal acceleration, as well as time of impact. The frequency of these features suggests a consensus in the literature regarding their significance and relevance for understanding diverse aspects of driver actions. ...
Machine learning (ML) techniques empower computers to learn from data and make predictions or decisions in various domains, while preprocessing methods assist in cleaning and transforming data before it can be effectively utilized by ML. Feature selection in ML is a critical process that significantly influences the performance and effectiveness of models. By carefully choosing the most relevant and informative attributes from the dataset, feature selection enhances model accuracy, reduces overfitting, and minimizes computational complexity. In this study, we leverage the UAH-DriveSet dataset to classify driver behavior, employing Filter, embedded, and wrapper methods encompassing 10 distinct feature selection techniques. Through the utilization of diverse ML algorithms, we effectively categorize driver behavior into normal, drowsy, and aggressive classes. The second objective is to employ feature selection techniques to pinpoint the most influential features impacting driver behavior. As a results, random forest emerges as the top-performing classifier, achieving an impressive accuracy of 96.4% and an F1-score of 96.36% using backward feature selection in 7.43 s, while K-nearest neighbour (K-NN) attains an accuracy of 96.29% with forward feature selection in 0.05 s. Following our comprehensive results, we deduce that the primary influential features for studying driver behavior include speed (km/h), course, yaw, impact time, road width, distance to the ahead vehicle, vehicle position, and number of detected vehicles.
