Driver Fatigue And Behavior Detection

## Overview

Driver Fatigue And Behavior Detection is a dataset for object detection tasks - it contains Driver Fatigue And Behavior Dete annotations for 919 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).

Off-the-shelf driver behavior data includes multiple ages, multiple time periods and multiple races (Caucasian, Black, Indian). The driver behaviors includes dangerous behavior, fatigue behavior and visual movement behavior.

DRIVER BEHAVIOR DETECTION

## Overview

DRIVER BEHAVIOR DETECTION is a dataset for object detection tasks - it contains BEHAVIOR annotations for 613 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).

304 People Multi-race - Driver Behavior Collection Data. The data includes multiple ages, multiple time periods and multiple races (Caucasian, Black, Indian). The driver behaviors includes dangerous behavior, fatigue behavior and visual movement behavior.

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).

Driving Behavior

## Overview

Driving Behavior is a dataset for object detection tasks - it contains Drinking annotations for 1,105 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).

Seven facial expressions recognition data of 1,323 drivers cover multiple ages, multiple time periods and multiple expressions. In terms of acquisition equipment, visible and infrared binocular cameras are used.

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 Forward Collision Warning 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 Forward Collision Warning 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 is either tailgating or experiences a near collision and recieves a warning through our app. These critical safety events are indicative of aggressive driving behavior and potential risks on the road. By providing data on these significant events, our dataset empowers clients to perform in-depth analysis and take proactive measures to enhance road safety.

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.

Primary Use-Cases and Verticals Driver Behavior Analysis: Organizations can leverage our dataset to analyze driving habits and identify trends in driver behavior related to tailgating and near collisions. This analysis can help in understanding patterns related to rule compliance, driver attentiveness, 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 object detection and decision-making capabilities in complex road environments.

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

Integration with Our Broader Data Offering The Forward Collision Warning 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' Forward Collision Warning Driver Behavior Data provides a unique opportunity for data buyers to access high-quality, actionable insights that drive innovation across mobility. By integrating our Forward Collision Warning Driver Behavior Data with other datasets, clients can gain a holistic view of transportation dynamics, enhancing their analytical capabilities and decision-making processes.

100 People - Face Detection & Face 96 Landmarks Annotation Data. The data includes multiple ages, multiple time periods and multiple races (Caucasian, Black, Indian).

Research Hypothesis: This study hypothesizes that drowsiness can be accurately detected in real-time through computer vision analysis of facial features, specifically eye closure patterns and yawning behavior, using affordable edge computing devices like the Raspberry Pi 5.

Data Collection Methodology: The dataset was recorded using a Raspberry Pi 5 equipped with a Camera Module 3. All recordings were captured at a consistent frame rate of 30 frames per second (FPS) and a resolution of 640×480 pixels, utilizing H.264 compression for video encoding. The recordings cover various lighting conditions with differing lux levels to simulate real-world scenarios ranging from low-light conditions (e.g., nighttime environments) to bright daylight settings.

Dataset Composition and Labeling: The dataset includes labeled categories essential for training and testing machine learning models. Labeling was performed using Edge Impulse, with the following categories:

Open Eyes: 968 training samples, 225 testing samples (Total: 1,193) Closed Eyes: 158 training samples, 49 testing samples (Total: 207) No Yawning: 496 training samples, 124 testing samples (Total: 620) Yawning: 60 training samples, 12 testing samples (Total: 72) Video-wise Annotation Data: The dataset also contains detailed video-wise annotations for testing purposes:

Open Eyes (mata_terbuka): 30,922 testing samples Closed Eyes (mata_tertutup): 4,662 testing samples No Yawning (tidak_menguap): 16,877 testing samples Yawning (menguap): 1,019 testing samples

Notable Findings: The data reveals a significant class imbalance, with open eyes representing 85.4% of image samples and 86.9% of video samples, while yawning behavior accounts for only 5.2% of image samples and 5.7% of video samples. This distribution reflects natural human behavior patterns where drowsiness indicators occur less frequently than alert states.

Data Interpretation and Usage: This dataset can be used to train machine learning models for drowsiness detection applications, particularly in automotive safety systems or workplace monitoring. The class imbalance should be addressed through appropriate sampling techniques or weighted loss functions during model training. The multi-modal nature of the data (both image-based and video-based annotations) allows for both static image classification and temporal sequence analysis approaches. Researchers should consider the lighting condition variations when evaluating model performance across different deployment scenarios.

1,334 People - Driver Gesture Recognition Data covers multiple age groups, multiple time periods, and multiple gestures. In terms of acquisition equipment, visible light and infrared binocular cameras are used. Each person collected 18 static gestures and 23 dynamic gestures.

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 Distracted Driving Alert Insurance 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 Distracted Driving Alert Insurance Data is distinguished by its real-time collection capabilities, utilizing our built-in computer vision technology to identify and capture instances where a driver is displaying distracted behavior and receives a warning through our app. While videos of drivers' faces are unavailable to protect privacy, the value of this data lies in understanding the different contexts in which a driver becomes distracted, the driving behavior exhibited by distracted drivers, and the broader effects of distracted driving on road safety. By providing data on these significant events, our dataset empowers clients to perform in-depth analysis and take proactive measures to enhance road safety.

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.

Primary Use-Cases and Verticals Driver Behavior Analysis: Organizations can leverage our dataset to analyze driving habits and identify trends in driver behavior related to tailgating and near collisions. This analysis can help in understanding patterns related to rule compliance, driver attentiveness, 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 object detection and decision-making capabilities in complex road environments.

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

Integration with Our Broader Data Offering Distracted Driving Alert Insurance 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' Distracted Driving Alert Insurance Data provides a unique opportunity for data buyers to access high-quality, actionable insights that drive innovation across mobility. By integrating our Distracted Driving Alert Insurance Data with other datasets, clients can gain a holistic view of transportation dynamics, enhancing their analytical capabilities and decision-making processes.

9,000 Images of 180 People - Driver Gesture 21 Landmarks Annotation Data. This data diversity includes multiple age periods, multiple time periods, multiple gestures, multiple vehicle types, multiple time periods.

Driver Behaviour

## Overview

Driver Behaviour is a dataset for object detection tasks - it contains Driver Behaviour Gesture annotations for 4,500 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 global Smart AI Dash Cam market is experiencing robust growth, driven by increasing demand for advanced driver-assistance systems (ADAS) and fleet management solutions. The market's expansion is fueled by several key factors, including rising concerns about road safety, the increasing affordability of AI-powered cameras, and stringent government regulations promoting driver monitoring and vehicle safety. The integration of AI capabilities, such as object detection, driver behavior analysis, and automatic incident reporting, enhances the value proposition of these cameras beyond basic video recording. This leads to significant benefits for both individual drivers and commercial fleets, including reduced insurance premiums, improved driver behavior, and optimized operational efficiency. We estimate the 2025 market size to be around $2.5 billion, with a Compound Annual Growth Rate (CAGR) of 15% projected through 2033, indicating substantial market potential. Key market segments include passenger vehicles and commercial fleets, with regional variations in adoption rates driven by factors such as infrastructure development, technological advancements, and regulatory landscapes. Competition among established players like Samsara, Lytx, and Netradyne, alongside emerging innovative companies, is intensifying, leading to product diversification and continuous technological advancements. The market's growth trajectory will likely be shaped by ongoing advancements in AI algorithms and computer vision technologies, enabling more accurate and sophisticated analyses of driving behavior and road conditions. The increasing adoption of connected car technologies and the development of integrated telematics solutions will further fuel the demand for smart AI dash cams. However, challenges remain, including data privacy concerns, the high initial investment costs for advanced systems, and the need for robust data infrastructure to support large-scale deployments. Addressing these challenges through enhanced data security protocols, flexible financing options, and scalable cloud-based solutions will be crucial for sustaining the market's momentum and realizing its full potential in the coming years. The continued integration of AI features will be crucial for maintaining a competitive edge, driving innovation, and further securing the growth of this dynamic market.

The global Night Vision System (NVS) and Driver Monitoring System (DMS) market is poised for substantial expansion, projected to reach an estimated USD 12,500 million by 2025, with a robust Compound Annual Growth Rate (CAGR) of 18% through 2033. This impressive trajectory is primarily fueled by the escalating demand for enhanced vehicle safety features, driven by increasing automotive production, stringent regulatory mandates for accident prevention, and a growing consumer awareness of the benefits these advanced technologies offer. The integration of NVS and DMS is no longer a luxury but a critical component in reducing road fatalities and improving the overall driving experience, particularly in low-light conditions and for combating driver fatigue and distraction. Key applications span across Passenger Vehicles, Light Commercial Vehicles, and Heavy Commercial Vehicles, with ongoing advancements in sensor technology, artificial intelligence, and data analytics further accelerating adoption. The market is witnessing significant investment in research and development, leading to more sophisticated and affordable solutions. The competitive landscape is characterized by the presence of established automotive component manufacturers such as Robert Bosch, Continental, Denso, and Aisin Seiki, alongside specialized players like Autoliv and Valeo, who are actively innovating to capture market share. Trends indicate a shift towards integrated NVS and DMS solutions that offer a synergistic approach to driver and road safety. The adoption of AI-powered algorithms for real-time driver behavior analysis and object detection in NVS is a significant trend. However, challenges such as the high cost of initial implementation, consumer education gaps regarding the full capabilities of these systems, and concerns over data privacy may pose moderate restraints to rapid market penetration. Despite these hurdles, the compelling safety advantages and the continuous drive for autonomous driving capabilities are expected to propel the NVS and DMS market to new heights. The Asia Pacific region, led by China and India, is anticipated to emerge as a dominant force due to its massive automotive manufacturing base and burgeoning demand for advanced safety features. Here is a unique report description for the Night Vision System and Driver Monitoring System market, incorporating your specified elements and constraints:

Edge AI Cameras for Fleet Safety Market Outlook

According to our latest research, the global Edge AI Cameras for Fleet Safety market size reached USD 2.34 billion in 2024, driven by rapid advancements in artificial intelligence and increasing demand for real-time fleet monitoring solutions. The market is projected to grow at a robust CAGR of 18.6% from 2025 to 2033, reaching an estimated USD 12.37 billion by 2033. This tremendous growth is fueled by the integration of AI-powered cameras into commercial vehicles, which enhances driver safety, optimizes fleet operations, and reduces operational risks. As per our latest research, the growing emphasis on regulatory compliance and accident prevention is a primary catalyst propelling the adoption of Edge AI cameras across diverse fleet applications worldwide.

One of the most significant growth factors for the Edge AI Cameras for Fleet Safety market is the rising concern for road safety and stringent government regulations across the globe. Fleet operators are under increasing pressure to ensure the safety of their drivers, cargo, and vehicles, especially in industries such as logistics, public transportation, and delivery services. The implementation of AI-powered cameras enables real-time driver monitoring, incident detection, and immediate response to potentially hazardous situations, thereby reducing accidents and liability costs. These capabilities not only help organizations comply with safety regulations but also foster a culture of accountability and proactive risk management within fleet operations. As a result, the adoption of Edge AI cameras has become a strategic imperative for fleet operators aiming to enhance safety standards and operational efficiency.

Another pivotal driver of market expansion is the technological evolution in AI and edge computing, which has made it feasible to process and analyze high-definition video data locally within the vehicle. This advancement minimizes latency, reduces bandwidth costs, and ensures that critical safety decisions are made in real-time, even in environments with limited or unreliable connectivity. The integration of advanced features such as facial recognition, object detection, and behavioral analysis into Edge AI cameras allows for comprehensive monitoring of both the driver and the vehicle’s surroundings. This technological leap is particularly important for fleet operators managing large and geographically dispersed fleets, as it provides actionable insights without the need for constant connectivity to central servers.

The increasing adoption of telematics and fleet management solutions is also contributing to the surge in demand for Edge AI cameras. Businesses are recognizing the value of data-driven decision-making and are investing in solutions that offer real-time visibility into fleet operations. Edge AI cameras, when combined with telematics platforms, provide a holistic view of driver behavior, vehicle status, and environmental conditions. This integration empowers fleet managers to implement targeted training programs, optimize routes, and reduce fuel consumption, ultimately leading to significant cost savings and improved service delivery. The ability to integrate Edge AI cameras seamlessly with existing fleet management systems is thus a key factor driving market growth.

Regionally, North America dominates the Edge AI Cameras for Fleet Safety market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The high adoption rate in North America is attributed to early technology adoption, robust regulatory frameworks, and significant investments in fleet safety infrastructure. Europe is witnessing substantial growth due to stringent safety regulations and the presence of major automotive OEMs. Meanwhile, the Asia Pacific region is emerging as a lucrative market, driven by the rapid expansion of the logistics and e-commerce sectors, particularly in countries like China, India, and Japan. The Middle East & Africa and Latin America are also showing promising growth trajectories, supported by increasing awareness of fleet safety and government initiatives to modernize transportation systems.

Car Datasets:

Car datasets are collections of data related to vehicles, including images, specifications, performance metrics, and more. These datasets are useful for machine learning and computer vision projects, such as

Vehicle recognition: Identifying makes, models, and years of cars Object detection: Detecting cars, pedestrians, lanes, and other objects in images and videos Autonomous driving Developing self-driving car technology that can detect and respond to its surroundings

Types of Car Data:

Image data: Photos of cars from various angles, lighting conditions, and environments Sensor data: Data from sensors like GPS, accelerometers, and lidar Performance data: Metrics like speed, acceleration, and fuel efficiency Specification data: Information about car models, including engine type, transmission, and features

Applications:

Self-driving cars: Developing autonomous vehicles that can detect and respond to their surroundings Vehicle safety: Improving safety features like lane departure warning and blind spot detection Traffic analysis: Analyzing traffic patterns and optimizing traffic flow Car insurance: Assessing risk and determining insurance premiums based on driving behavior

Popular Car Datasets:

KITTI Vision Benchmark Suite ApolloScape Udacity Self-Driving Car Dataset nuScenes Bosch Small Traffic Lights Dataset

Night Driving Assist Market Outlook

According to our latest research, the global Night Driving Assist market size reached USD 4.28 billion in 2024, demonstrating robust momentum driven by rising safety concerns and technological advancements in the automotive industry. The market is expected to maintain a healthy growth trajectory, registering a CAGR of 10.7% from 2025 to 2033. By the end of 2033, the Night Driving Assist market is forecasted to achieve a value of USD 10.52 billion, underpinned by increasing adoption of advanced driver assistance systems (ADAS), regulatory mandates for vehicle safety, and growing consumer awareness about the risks associated with night-time driving.

The primary growth factor fueling the Night Driving Assist market is the escalating demand for enhanced automotive safety features, particularly in passenger and commercial vehicles. As road accidents during night hours account for a significant proportion of fatalities, automotive manufacturers and technology providers are prioritizing the integration of night driving assist technologies such as adaptive headlights, night vision systems, and driver monitoring systems. These innovations not only improve visibility but also reduce driver fatigue, thereby lowering accident rates. The proliferation of electric vehicles and autonomous driving technologies further accelerates the uptake of night driving assist solutions, as these vehicles rely heavily on advanced sensor technologies to ensure optimal performance in low-light conditions.

Another critical driver is the tightening of vehicle safety regulations and standards across major automotive markets such as North America, Europe, and Asia Pacific. Regulatory authorities are increasingly mandating the inclusion of advanced safety features in both new and existing vehicle fleets to address the persistent challenge of night-time road accidents. This regulatory push is compelling original equipment manufacturers (OEMs) to incorporate night driving assist technologies as standard or optional features, thereby expanding the addressable market. Additionally, the aftermarket segment is witnessing substantial growth as vehicle owners seek to upgrade their existing vehicles with advanced night driving assist systems, further contributing to market expansion.

Technological advancements in sensor technologies, including infrared, thermal imaging, and radar, are also playing a pivotal role in shaping the Night Driving Assist market. The continuous evolution of these technologies has led to the development of more reliable, accurate, and cost-effective systems, making them accessible to a broader range of vehicles beyond premium segments. The integration of artificial intelligence and machine learning algorithms into night driving assist solutions enables real-time analysis of road conditions, object detection, and driver behavior monitoring, thereby enhancing system performance and user experience. These innovations are expected to drive widespread adoption and foster long-term market growth.

From a regional perspective, Asia Pacific is emerging as the fastest-growing market for night driving assist technologies, supported by rapid urbanization, increasing vehicle ownership, and rising disposable incomes. North America and Europe, on the other hand, continue to dominate the market in terms of revenue share, owing to early adoption of advanced automotive safety features and stringent regulatory frameworks. Latin America and the Middle East & Africa are also witnessing gradual adoption, driven by improving road infrastructure and growing awareness about vehicle safety. The regional dynamics are expected to evolve further as global players expand their presence and local manufacturers invest in technology upgrades.

Product Type Analysis

The Night Driving Assist market is segmented by product type into adaptive headlights, night vision systems, driver monitoring systems, and others. Adaptive headlights have gained significant traction due to their ability to automatically adjust beam patterns based on vehicle speed, steering angle, and road conditions, thereby providing optimal illumination during night-time driving. The increasing adoption of adaptive headlights in both passenger and commercial vehicles is attributed to their proven effectiveness in reducing glare and enhancing driver visibility on poorly-lit roads. Automotive manufacturers are continuously innovating in this space, introduc

Truck 360 Camera DVR Market Outlook

According to our latest research, the global Truck 360 Camera DVR market size reached USD 1.32 billion in 2024. The market is experiencing robust expansion, with a recorded CAGR of 13.4% from 2025 to 2033. By the end of the forecast period in 2033, the market is projected to attain a value of USD 4.16 billion. This rapid growth is primarily fueled by increasing regulatory mandates for vehicle safety, the rising adoption of advanced driver assistance systems (ADAS), and the expanding logistics and transportation sectors worldwide. As per the latest research, the Truck 360 Camera DVR market is poised to witness significant transformation, driven by technological advancements and the growing need for comprehensive fleet monitoring and security solutions.

One of the primary growth factors for the Truck 360 Camera DVR market is the increasing emphasis on road safety and accident prevention. Governments and regulatory authorities across the globe are enforcing stricter safety norms, which mandate the installation of advanced camera systems in commercial vehicles. This is particularly evident in regions with high rates of road accidents involving heavy and light commercial vehicles. The integration of 360-degree camera DVRs provides drivers and fleet managers with a holistic view of the vehicle's surroundings, significantly reducing blind spots and enhancing situational awareness. Fleet operators are increasingly recognizing the value proposition of these systems, not only in minimizing accidents and liabilities but also in reducing insurance premiums and improving overall operational efficiency. As awareness of these benefits grows, adoption rates are expected to surge, driving sustained market expansion.

Another significant driver is the rapid technological evolution in imaging and connectivity. The transition from traditional camera systems to high-definition (HD), full HD, and even 4K camera resolutions has greatly enhanced the clarity and reliability of visual data captured by Truck 360 Camera DVRs. This advancement is complemented by the proliferation of wireless connectivity solutions, which facilitate real-time data transmission and remote monitoring. Fleet managers can now access live feeds, receive instant alerts, and analyze recorded footage from any location, enabling proactive decision-making and faster incident response. The integration of artificial intelligence (AI) and machine learning algorithms further augments the capabilities of these systems, allowing for features such as object detection, lane departure warnings, and driver behavior analysis. These technological strides are making Truck 360 Camera DVRs indispensable tools for modern fleet management.

The expansion of the logistics and transportation sectors, driven by the boom in e-commerce and global trade, is also propelling the Truck 360 Camera DVR market. As companies strive to optimize their supply chains and ensure timely deliveries, the need for enhanced vehicle surveillance and driver monitoring becomes paramount. Fleet operators are under pressure to minimize downtime, prevent cargo theft, and ensure compliance with industry regulations. The deployment of advanced 360 camera DVR systems addresses these challenges by providing comprehensive surveillance, real-time tracking, and detailed incident documentation. Furthermore, the increasing trend toward fleet digitization and the adoption of telematics solutions are creating synergies that further boost the demand for integrated camera DVR systems. As the logistics landscape becomes more competitive, the adoption of such technologies is expected to become standard practice across the industry.

Regionally, Asia Pacific is emerging as a dominant force in the Truck 360 Camera DVR market, accounting for the largest share in 2024. This growth is underpinned by the rapid expansion of the commercial vehicle sector in countries like China, India, and Japan, coupled with increasing investments in transportation infrastructure. North America and Europe are also significant markets, driven by stringent safety regulations and high adoption rates of advanced vehicle technologies. Meanwhile, Latin America and the Middle East & Africa are witnessing steady growth, supported by ongoing infrastructure development and the gradual modernization of commercial fleets. Each region presents unique opportunities and challenges, shaping the competitive dynamics and overall trajectory of the global Truck 360 Camera DVR market.