Driving behavior plays a vital role in maintaining safe and sustainable transport, and specifically, in the area of traffic management and control, driving behavior is of great importance since specific driving behaviors are significantly related with traffic congestion levels. Beyond that, it affects fuel consumption, air pollution, public health as well as personal mental health and psychology. Use of Smartphone sensors for data acquisition has emerged as a means to understand and model driving behavior. Our aim is to analyze driving behavior using on Smartphone sensors’ data streams. We present Smartphone sensor (Accelerometer, Gyroscope, Proximity, etc.) data recorded in live traffic while driver was executing the driving events. The datasets folder include .csv files of sensor data like Accelerometer, Gyroscope, etc. This data was recorded in live traffic while driver was executing certain driving events. The travel time for each one way trip was approximately 5kms - 20kms. The smartphone position was fixed horizontally in the vehicles utility box. Vehicle type used for data recording was LMV.

Driver activity dataset

Dataset consists of 2,400+ images capturing the driving behaviors of 304 individuals through the use of RGB and infrared cameras. This extensive dataset is specifically designed for behavior analysis and driver monitoring, focusing on various driving scenarios and environments to enhance traffic and road safety. By utilizing this dataset, researchers and developers can advance their understanding and capabilities in recognition tasks such as driver behavior… See the full description on the dataset page: https://huggingface.co/datasets/UniDataPro/driver-behaviour-dataset.

Context

Driver behavior is one of the most important aspects in the design, development, and application of Advanced Driving Assistance Systems (ADAS) and Intelligent Transportation Systems (ITS), which can be affected by many factors. If you are able to measure the driving style of your staff, there is a lot of actions you can take in order to improve fleet safety, global road safety as well as fuel efficiency and emissions.

Content

• Dataset for modeling risky driver behaviors based on accelerometer (X,Y,Z axis in meters per second squared (m/s2)) and gyroscope (X,Y, Z axis in degrees per second (°/s) ) data.
• Sampling Rate: Average 2 samples (rows) per second
• Cars: Ford Fiesta 1.4, Ford Fiesta 1.25, Hyundai i20
• Drivers: 3 different drivers with the ages of 27, 28 and 37
• Driver Behaviors: 1.Sudden Acceleration (Class Label: 1) 2.Sudden Right Turn (Class Label: 2) 3.Sudden Left Turn (Class Label: 3) 4.Sudden Break (Class Label: 4)
• Best Window Size: 14 seconds
• Sensor: MPU6050
• Device: Raspberry Pi 3 Model B

Acknowledgements

Yuksel, Asim; Atmaca, Şerafettin (2020), “Driving Behavior Dataset”, Mendeley Data, V2, doi: 10.17632/jj3tw8kj6h.2

Data set is available in below link- Click here

Dataset for modeling risky driver behaviors based on accelerometer (X,Y,Z axis in meters per second squared (m/s2)) and gyroscope (X,Y, Z axis in degrees per second (°/s) ) data. Sampling Rate: Average 2 samples (rows) per second Cars: Ford Fiesta 1.4, Ford Fiesta 1.25, Hyundai i20 Drivers: 3 different drivers with the ages of 27, 28 and 37 Driver Behaviors: Sudden Acceleration (Class Label: 1), Sudden Right Turn (Class Label: 2), Sudden Left Turn (Class Label: 3), Sudden Break (Class Label: 4) Best Window Size: 14 seconds Sensor: MPU6050 Device: Raspberry Pi 3 Model B Please See Summary Table for summary of the collected data.

[y_axis]

Unidata’s Driver Behaviour dataset helps AI analyze real-world driving patterns to enhance road safety and autonomous systems

Driver Behaviour

## Overview

Driver Behaviour is a dataset for object detection tasks - it contains Seatbelt Notseat Sleep Not_sleep annotations for 300 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

air pollution

Distracted driving-related accidents are a critical global issue, especially as road traffic increases in densely populated areas. To address the challenge of driver distraction, we introduce a novel dataset that supports the development of real-time monitoring and detection systems by capturing authentic driver behaviors. Collected in Ashulia, Dhaka, Bangladesh, in October 2024, this dataset includes images captured under real-world driving conditions within both private vehicles and public buses. The photos were taken using personal mobile phones, ensuring a realistic and diverse set of visual data. This dataset spans a wide range of driving behaviors, including safe driving, turning, texting, talking on the phone, and other potentially risky behaviors, such as drowsy driving. By depicting these behaviors in everyday driving scenarios, the dataset serves as a valuable resource for training and evaluating models designed to detect unsafe driving practices in real-time.The dataset includes high-resolution photos taken inside public buses and personal cars in Ashulia, Dhaka, Bangladesh, under actual driving circumstances. The photographs, which were taken using the cameras on personal cell phones, offer a genuine and varied collection of visual information under normal driving circumstances. The following five behavioral classes comprise the dataset: I. Safe Driving: Images showing a driver who seems to be paying attention to the road, both hands on the wheel, and concentrated or 1 hand on the steering wheel and other on the gear stick. This is the perfect example of driving without distractions. II. Turning: Photographs that show drivers changing direction during turns by moving their heads or full bodies. This behavior is crucial for figuring out how focused the driver is on everyday tasks like rotating the steering wheel. III. Texting Phone: Pictures of drivers using their phones, whether it is to type messages or to interact with the screen. Since texting and driving is one of the main causes of distracted driving, this training is very important for identifying it. IV. Talking Phones: When drivers talk on their phones or hold them up to their ears while driving a vehicle. This category aids in identifying actions connected to phone talks, which are another frequent source of interruptions. V. Others: Contains any actions that go against safe driving practices, like drinking water or anything while driving, sleeping while driving, or talking with someone behind while driving. Relevant photos are included in each session, and they differ in terms of vehicle type and illumination to represent the variety of driving situations found in the real world. Because the images are unprocessed and unannotated, there is freedom in how machine learning

Driving behaviour multimodal human factors preprocessed dataset, including EEG, EMG, ECG and GSR.

Abnormal Driver Behaviour

## Overview

Abnormal Driver Behaviour is a dataset for object detection tasks - it contains Seatbelt Drink Phone annotations for 2,110 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The Driver Behavior Management (DBM) market is experiencing robust growth, driven by increasing concerns over road safety, stringent regulatory compliance mandates, and the potential for significant cost savings through reduced insurance premiums and fuel efficiency improvements. The market, estimated at $2 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 15% between 2025 and 2033, reaching approximately $7 billion by 2033. Key application segments include logistics and transportation, public transportation, and vehicle fleets, with driver identification, activity detection, and dangerous behavior monitoring as the primary system types. North America currently holds the largest market share, followed by Europe and Asia Pacific, driven by early adoption of advanced driver-assistance systems and the presence of established telematics players. However, increasing connectivity and technological advancements in emerging economies are fostering significant growth potential in Asia Pacific and other regions. The growth trajectory is further fueled by technological advancements like AI-powered video analytics, improving the accuracy and efficiency of driver behavior monitoring. Integration with fleet management systems offers valuable insights into operational efficiency, allowing businesses to optimize routes, reduce idle time, and enhance overall productivity. While high initial investment costs and data privacy concerns pose certain restraints, the long-term benefits in terms of risk mitigation, improved safety, and enhanced operational efficiency are expected to outweigh these challenges, pushing market expansion. The competitive landscape is characterized by a mix of established telematics companies and emerging technology providers, fostering innovation and driving down costs, ultimately contributing to market growth. The market's future hinges on the continued adoption of advanced technologies, stringent regulatory measures, and a growing awareness of the benefits of DBM across diverse industries.

The Driver Behavior Management (DBM) market is experiencing robust growth, driven by increasing demand for enhanced road safety, reduced fuel consumption, and improved fleet efficiency. The market, estimated at $5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $15 billion by 2033. This expansion is fueled by several key factors. Stringent government regulations regarding driver safety and fuel efficiency are compelling businesses to adopt DBM solutions. Furthermore, advancements in telematics technology, including the integration of Artificial Intelligence (AI) and machine learning (ML) for more accurate and insightful data analysis, are driving innovation and adoption within the market. The increasing affordability and accessibility of DBM systems, coupled with the rising awareness of the significant return on investment (ROI) associated with improved driver behavior, are also contributing to the market's expansion. The competitive landscape is characterized by a mix of established players and emerging technology companies, constantly vying for market share through product innovation and strategic partnerships. The market segmentation reveals significant opportunities across various sectors. Transportation and logistics are major adopters, followed by the automotive and commercial fleet segments. Geographically, North America and Europe currently dominate the market, but rapid growth is anticipated in the Asia-Pacific region fueled by expanding infrastructure projects and increasing vehicle ownership. However, challenges remain. High initial investment costs for implementing DBM systems can be a barrier to entry for smaller businesses. Concerns regarding data privacy and security also necessitate careful consideration and robust regulatory frameworks. Despite these challenges, the long-term growth outlook for the DBM market remains positive, underpinned by a growing focus on safety, efficiency, and sustainability across various industries.

The Driver Behavior Management (DBM) market is experiencing robust growth, driven by increasing concerns over road safety, stringent government regulations, and the potential for significant cost savings through reduced insurance premiums and fuel consumption. The market, segmented by application (Logistics & Transportation, Public Transportation, Vehicle Fleets, Others) and type of behavior monitoring (Driver Identification, Activity Detection, Dangerous Behavior, Others), shows a strong preference for solutions focusing on safety and efficiency. The integration of advanced technologies like AI and machine learning is enhancing the accuracy and effectiveness of DBM systems, leading to more precise identification of risky driving behaviors and proactive interventions. This market is further fueled by the rising adoption of telematics and connected vehicle technologies, which provide the necessary data for effective DBM implementation. The North American and European markets currently hold significant shares, but growth potential in emerging economies within Asia-Pacific and Middle East & Africa is considerable, driven by improving infrastructure and expanding fleet sizes. Competition in the DBM market is intense, with a mix of established players and emerging technology companies vying for market share. Established telematics providers are leveraging their existing customer bases and infrastructure to integrate DBM capabilities, while newer entrants are focusing on innovative solutions and data analytics. The success of these companies hinges on their ability to offer comprehensive, user-friendly platforms that integrate seamlessly with existing fleet management systems. Future growth will depend on continuous innovation, the development of more sophisticated algorithms for behavior analysis, and the ability to address evolving customer needs and regulatory requirements. The market is expected to see consolidation as larger companies acquire smaller players to expand their product offerings and geographical reach. Focus on data privacy and security will also be a crucial factor driving market developments in the years to come.

Sample Data: https://cloud.drivertechnologies.com/shared?s=146&t=4:03&token=0f469c88-d578-4b4f-80b2-f53f195683b2

At Driver Technologies, we are dedicated to harnessing advanced technology to gather anonymized critical driving data through our innovative dash cam app, which operates seamlessly on end users' smartphones. Our Speed Over Limit Driver Behavior Data offering is a key resource for understanding driver behavior and improving safety on the roads, making it an essential tool for various industries.

What Makes Our Data Unique? Our Speed Over Limit Driver Behavior Data is distinguished by its real-time collection capabilities, utilizing our built-in computer vision technology to identify and capture instances where a driver nearly gets into an accident. This data reflects critical safety events that are indicative of potential risks and non-compliance with traffic regulations. By providing data on these significant events, our dataset empowers clients to perform in-depth analysis.

How Is the Data Generally Sourced? Our data is sourced directly from users who utilize our dash cam app, which harnesses the smartphone’s camera and sensors to record during a trip. This direct sourcing method ensures that our data is unbiased and represents a wide variety of conditions and environments. The data is not only authentic and reflective of current road conditions but is also abundant in volume, offering millions of miles of recorded trips that cover diverse scenarios. For our Speed Over Limit Driver Behavior Data, we leverage computer vision models to read speed limit signs as the driver drives past them, then compare that to speed data captured using the phone's sensor.

Primary Use-Cases and Verticals Driver Behavior Analysis: Organizations can leverage our dataset to analyze driving habits and identify trends in driver behavior. This analysis can help in understanding patterns related to rule compliance and potential risk factors.

Training Computer Vision Models: Clients can utilize our annotated data to develop and refine their own computer vision models for applications in autonomous vehicles, ensuring better decision-making capabilities in complex driving environments.

Improving Risk Assessment: Insurers can utilize our dataset to refine their risk assessment models. By understanding the frequency and context of significant events, they can better evaluate driver risk profiles, leading to more accurate premium pricing and improved underwriting processes.

Integration with Our Broader Data Offering The Speed Over Limit Driver Behavior Data is a crucial component of our broader data offerings at Driver Technologies. It complements our extensive library of driving data collected from various vehicles and road users, creating a comprehensive data ecosystem that supports multiple verticals, including insurance, automotive technology, and smart city planning.

In summary, Driver Technologies' Speed Over Limit Driver Behavior Data provides a unique opportunity for data buyers to access high-quality, actionable insights that drive innovation across mobility. By integrating our Speed Over Limit Driver Behavior Data with other datasets, clients can gain a holistic view of transportation dynamics, enhancing their analytical capabilities and decision-making processes.

1,003 People-Driver Behavior Collection Data. The data includes multiple ages and multiple time periods. The driver behaviors includes Dangerous behavior, fatigue behavior and visual movement behavior. In terms of device, binocular cameras of RGB and infrared channels were applied. This data can be used for tasks such as driver behavior analysis.

In August, 1974, the definition of the STOP sign in New South Wales was changed to correspond with accepted international practice. Driver behaviour was observed at ten open, free-flowing, orthogonal intersections posted with STOP signs and at two similar intersections posted with GIVE-WAY signs. Observations were carried out once before and twice after the date of the change. The behaviour of the drivers approaching the signs was recorded in relation to that of the other drivers with whom they interacted. The proportion of drivers who actually stopped at the STOP signs initially increased and then fell below the original level. The final proportion was similar to that observed at the GIVE-WAY signs which were not affected by the change in regulations. The proportion of drivers who yielded as required at STOP signs showed little initial change, but subsequ_ntly increased. A similar pattern was observed at GIVE-WAY signs and in the final survey the porportions were approximately equal. The marked similarity in driver behaviour between the two sign types might suggest the need for a re-evaluation of the placing of STOP signs.

and to address it

Driver Behaviors

## Overview

Driver Behaviors is a dataset for object detection tasks - it contains Seatbelt Cigarette Phone annotations for 9,901 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

103,282-Images Driver Behavior Annotation Data. The data includes multiple ages, multiple time periods and behaviors (Dangerous behaviors, Fatigue behaviors, Visual movement behaviors). In terms of annotation, 72 facial landmarks (including pupils), face attributes, gesture bounding boxes, seatbelt bounding boxes, pupil landmarks and behavior categories were annotated in the data. This data can be used for tasks such as driver behavior analysis.