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A general event data recorder is a device installed in automobiles to record information related to vehicle crashes or accidents. The data provide a better understanding of how certain crashes come about. This study made a prototype of a driving behaviour-based event data recorder (DBEDR), which provides the information of driving behaviours and a danger level. The authors approach is to recognise the seven behaviours: normal driving, acceleration, deceleration, changing to the left lane or right lane, zigzag driving and approaching the car in front by the hidden Markov models. All data were collected from a real vehicle and evaluated in a real road environment. The experimental results show that the proposed method achieved an average detection ratio of 95% for behaviour recognition. The danger level is inferred by fuzzy rules involved with the above behaviours. DBEDR recorded the recognised driving behaviours and the danger level, and the places were stored with the assistance of a global positioning system receiver. By integrating Google Maps, the locations, the driving behaviour occurrences, the danger level on the travel routes and the recorded images, the proposed DBEDR could be more useful compared with the traditional EDRs.

Predicting driving behavior and crash risk in real-time is a problem that has been heavily researched in the past years. Although in-vehicle interventions and gamification features in post-trip dashboards have emerged, the connection between real-time driving behavior prediction and the triggering of such interventions is yet to be realized. This is the focus of the European Horizon2020 project “i-DREAMS”, which aims at defining, developing, testing and validating a ‘Safety Tolerance Zone’ (STZ) in order to prevent drivers from risky driving behaviors using interventions both in real-time and post-trip. However, the data-driven conceptualization of STZ levels is a challenging task, and data class imbalance might hinder this process. Following the project principles and taking the aforementioned challenges into consideration, this paper proposes a framework to identify the level of risky driving behavior as well as the duration of the time spent in each risk level by private car drivers. This aim is accomplished by four classification algorithms, namely Support Vector Machines (SVMs), Random Forest (RFs), AdaBoost, and Multilayer Perceptron (MLP) Neural Networks and imbalanced learning using the Adaptive Synthetic technique (ADASYN) in order to deal with the unbalanced distribution of the dataset in the STZ levels. Moreover, as an alternative approach of risk prediction, three regression algorithms, namely Ridge, Lasso, and Elastic Net are used to predict time duration. The results showed that RF and MLP outperformed the rest of the classifiers with 84% and 82% overall accuracy, respectively, and that the maximum speed of the vehicle during a 30 s interval, is the most crucial predictor for identifying the driving time at each safety level.

Human behavior significantly contributes to severe road injuries, underscoring a critical road safety challenge. This study addresses the complex task of predicting dangerous driving behaviors through a comprehensive analysis of over 356,000 trips, enhancing existing knowledge in the field and promoting sustainability and road safety. The research uses advanced machine learning algorithms (e.g., Random Forest, Gradient Boosting, Extreme Gradient Boosting, Multilayer Perceptron, and K-Nearest Neighbors) to categorize driving behaviors into ‘Dangerous’ and ‘Non-Dangerous’. Feature selection techniques are applied to enhance the understanding of influential driving behaviors, while k-means clustering establishes reliable safety thresholds. Findings indicate that Gradient Boosting and Multilayer Perceptron excel, achieving recall rates of approximately 67% to 68% for both harsh acceleration and braking events. This study identifies critical thresholds for harsh events: (a) 48.82 harsh accelerations and (b) 45.40 harsh brakings per 100 km, providing new benchmarks for assessing driving risks. The application of machine learning algorithms, feature selection, and k-means clustering offers a promising approach for improving road safety and reducing socio-economic costs through sustainable practices. By adopting these techniques and the identified thresholds for harsh events, authorities and organizations can develop effective strategies to detect and mitigate dangerous driving behaviors.

Professional drivers play a key role in urban road network safety. It is therefore important to employ safer drivers, also find the problem, and train the existing ones. However, a direct driving test may not be very useful solely because of drivers’ consciousness. This study introduces a latent predictor to expect driving behaviors, by finding the relation between taxi drivers’ psychological characteristics and their driving behaviors. A self-report questionnaire was collected from 245 taxi drivers by which their demographic characteristics, psychological characteristics, and driving behaviors were obtained. The psychological characteristics include instrumental attitude, subjective norm, sensation seeking, aggressive mode, conscientiousness, life satisfaction, premeditation, urgency, and selfishness. Driving behaviors questionnaire (DBQ) provides information regarding drivers’ violations, aggressive violations, errors, and lapses. The standard linear regression model is used to determine the relationship between driving behavior and psychological characteristics of drivers. The findings show that social anxiety and selfishness are the best predictors of the violations; aggressive mode is a significant predictor of the aggressive violations; urgency has a perfect impact on the errors; and finally, life satisfaction, sensation seeking, conscientiousness, age, and urgency are the best predictors of the lapses.

Nowadays, AI has many applications in everyday human activities such as exercise, eating, sleeping, and automobile driving. Tech companies can apply AI to identify individual behaviors (e.g., walking, eating, driving), analyze them, and offer personalized feedback to help individuals make improvements accordingly. While offering personalized feedback is more beneficial for drivers, most smart driver systems in the current market do not use it. This paper presents AutoCoach, an intelligent AI agent that classifies drivers’ into different driving-personality groups to offer personalized feedback. We have built a cloud-based Android application to collect, analyze and learn from a driver’s past driving data to provide personalized, constructive feedback accordingly. Our GUI interface provides real-time user feedback for both warnings and rewards for the driver. We have conducted an on-the-road pilot user study. We conducted a pilot study where drivers were asked to use different agent versions to compare personality-based feedback versus non-personality-based feedback. The study result proves our design’s feasibility and effectiveness in improving the user experience when using a personality-based driving agent, with 61% overall acceptance that it is more accurate than non-personality-based.

Data representing driving behavior, as measured by various sensors installed in a vehicle, are collected as multi-dimensional sensor time-series data. These data often include redundant information, e.g., both the speed of wheels and the engine speed represent the velocity of the vehicle. Redundant information can be expected to complicate the data analysis, e.g., more factors need to be analyzed; even varying the levels of redundancy can influence the results of the analysis. We assume that the measured multi-dimensional sensor time-series data of driving behavior are generated from low-dimensional data shared by the many types of one-dimensional data of which multi-dimensional time-series data are composed. Meanwhile, sensor time-series data may be defective because of sensor failure. Therefore, another important function is to reduce the negative effect of defective data when extracting low-dimensional time-series data. This study proposes a defect-repairable feature extraction method based on a deep sparse autoencoder (DSAE) to extract low-dimensional time-series data. In the experiments, we show that DSAE provides high-performance latent feature extraction for driving behavior, even for defective sensor time-series data. In addition, we show that the negative effect of defects on the driving behavior segmentation task could be reduced using the latent features extracted by DSAE.

Road Safety has become a serious issue in this era as the population in terms of both people as well as vehicles has increased to a large extend. The behaviour of drivers is influenced by several factors, which include their socio-economic – demographic factors and vehicle characteristics. Accidents comprising of goods vehicles have been on the surge in Kerala over the years. Even though the numbers of heavy vehicles plying on Kerala roads are less compared to two wheelers and cars, the proportion of crashes involving heavy vehicles is a cause of concern. Fallacious driving behaviourism is generally considered as the leading root of such accidents. The present study intends to investigate the elements involved in the Driver Behaviour Questionnaire (DBQ), then to explore the relationships between the elements of the DBQ and accident involvement. The data were interpreted using percentage analysis. The analysis showed the level of violations, errors, lapses committed by drivers on the basis of driving behaviourism and non –driving behaviourism of the goods vehicle driver.

We investigated the effect of driver posture on driving control following a takeover request (TOR) from autonomous to manual driving in level 3 autonomous vehicles. When providing a TOR, driving behaviors need to be investigated to develop driver monitoring systems, and it is important to clarify the effect of driver postures. Experiments were conducted using driver postures that are likely to be adopted in autonomous driving. Driver postures were set based on combinations of two types of upper-body posture and three types of foot posture. The driver’s upper body and head were set to either a forward or sideways orientation. For each of these there were three types of foot posture: both feet on the floor, crossed legs, and cross-legged sitting. Following a TOR, we compared the braking and steering maneuvers of subjects driving normally and examined the effects of posture on driver reaction time. The results show that both the upper-body and foot postures of the driver affect the steering and braking reaction time. The driver monitoring system should be able to detect posture and activate a TOR warning, and detection times up to 2 and 1.3 times faster than those for normal postures should be considered for different upper-body and foot postures, respectively.

Although there are clear moral components to traffic violations and risky and aggressive driving behavior, few studies have examined the relationship between moral values and risky driving. This study aimed to examine the relationship between moral views of driving behavior and brain activity. Twenty healthy drivers participated in this study. A questionnaire regarding their moral values concerning driving behavior was administered to the participants. Brain activity was measured using near-infrared spectroscopy while eliciting moral emotions. Based on the results of the questionnaire, the participants were divided into two groups: one with high moral values and the other with low moral values. Brain activity was statistically compared between the two groups. Both groups had significantly lower activity in the prefrontal cortex during the self-risky driving task. The low moral group had significantly lower activity in the left dorsolateral prefrontal cortex than the high moral group, while it had lower activity in the right dorsolateral prefrontal cortex in the self-risky driving task than in the safe driving task. Regardless of their moral values, the participants were less susceptible to moral emotions during risky driving. Furthermore, our findings suggest that drivers with lower moral values may be even less susceptible to moral emotions.

Objectives: Aggressive driving and road accidents are major concerns in the public health sector. This study aimed to explore risk to aggressive and risky behaviors on the road in two-wheeler riding young men. Methods: The study comprised 433 young male two-wheeler riders from an urban city of India. A two-wheeler riding survey that captured subjective perception of difficulty in managing anger in general, easy provocability to anger, and aggressive and risky behaviors on the road, and Negative Mood Regulation (NMR) scale were administered. Results: Of the 433 participants, 83 (19%) reported experiencing problematic anger in general, whereas 175 (40.42%) did not endorse experiencing problematic anger. Based on this, two groups were formed, namely, problematic anger-present group and problematic anger-absent group. The problematic anger-present group reported high score on easy provocability to anger, difficulty in controlling anger, specific motives related to riding fast than usual, and severity of aggressive responses to frustrating situations while riding, and low score on NMR scale. Statistical analysis revealed a significant difference between the groups. Conclusion: This study highlights the relevance of assessing subjective perception of problematic anger in two-wheeler riding young men. This has implications for designing interventions for enhancing road safety.