ITS JPO's Connected Vehicle Pilot Deployment Program integrates connected vehicle research concepts into practical and effective elements to enhance existing operational capabilities. Data were collected throughout each pilot to facilitate independent evaluations of the use of connected vehicle technology on real roadways. To encourage additional study and reuse of these data, ITS DataHub has partnered with each pilot site to make sanitized and anonymized tabular and non-tabular data from these projects available to the public. This article gives you a brief overview of what each pilot focused on and what types of CV Pilot data and tools are available on ITS DataHub.

This dataset contains V2X data collected from the Utah Connected Vehicle Data Ecosystem Program. We have submitted sample MAP messages in J2735 standards from 3 intersections in Orem, UT.

Success.ai’s Verified Company Data for the Automotive Industry in North America provides businesses with reliable, detailed insights into automotive companies and decision-makers across the region.

Drawing from over 170 million verified professional profiles and 30 million company profiles, this dataset delivers comprehensive firmographic details, business locations, and direct contact information for automotive manufacturers, suppliers, dealerships, and service providers.

Whether you’re targeting OEMs, aftermarket suppliers, or dealership networks, Success.ai ensures your outreach and strategic initiatives are supported by accurate, continuously updated, and AI-validated data, all backed by our Best Price Guarantee.

Why Choose Success.ai’s Automotive Industry Data?

1. Comprehensive Automotive Company Insights

   • Access verified firmographic details such as company size, revenue range, production capabilities, and geographic locations.
   • AI-driven validation ensures 99% accuracy, providing confidence in your data and streamlining outreach efforts.

2. Coverage of North American Automotive Markets

   • Includes profiles of manufacturers, Tier 1 and Tier 2 suppliers, dealerships, and service centers across the U.S., Canada, and Mexico.
   • Gain visibility into operational structures, market dynamics, and technology adoption trends unique to the North American automotive sector.

3. Continuously Updated Datasets

   • Real-time updates reflect leadership changes, market expansions, plant openings, and emerging business opportunities.
   • Stay ahead of industry trends and maintain alignment with the fast-paced automotive market.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other data privacy regulations, ensuring responsible and lawful use of company data in your campaigns.

Data Highlights:

• 170M+ Verified Professional Profiles: Connect with automotive executives, operations managers, engineers, and procurement specialists across North America.
• 30M Company Profiles: Access detailed insights into supply chains, dealership networks, and aftermarket providers.
• Business Location Data: Pinpoint facilities, plants, and distribution centers to refine supply chain strategies and customer engagement efforts.
• Firmographic Insights: Understand production capacities, specialization areas, and market positions of automotive businesses.

Key Features of the Dataset:

1. Decision-Maker Profiles in the Automotive Sector

   • Identify and engage with CEOs, COOs, plant managers, and R&D directors shaping production, procurement, and innovation strategies.
   • Target professionals influencing vehicle design, supplier contracts, and dealership networks.

2. Advanced Filters for Precision Targeting

   • Filter companies by segment (OEMs, aftermarket suppliers, EV manufacturers), geographic location, production volumes, or technology focus.
   • Tailor campaigns to align with regional trends, sustainability initiatives, and consumer preferences.

3. AI-Driven Enrichment

   • Profiles enriched with actionable data enable personalized messaging, highlight value propositions, and improve engagement outcomes with automotive stakeholders.

Strategic Use Cases:

1. Supplier and Vendor Development

   • Build relationships with Tier 1 and Tier 2 suppliers managing raw materials, components, or technology integrations.
   • Present products or services that enhance efficiency, cost savings, or compliance with sustainability regulations.

2. Market Entry and Expansion Strategies

   • Explore opportunities to enter new markets or expand into EV production, autonomous vehicles, or connected car technologies.
   • Analyze firmographics and location data to select regions or partners aligned with your business goals.

3. Technology and Innovation Outreach

   • Target R&D directors and engineering teams evaluating new technologies like robotics, IoT systems, or AI-driven production tools.
   • Position your solutions to support innovation in EVs, advanced manufacturing, or intelligent supply chains.

4. Dealership and Service Network Optimization

   • Engage with dealership owners and service managers to enhance customer experience, increase operational efficiency, or adopt digital platforms.
   • Present solutions for inventory management, CRM systems, or customer loyalty programs tailored to dealership needs.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality data at competitive prices, ensuring maximum ROI for sales, marketing, and operational initiatives targeting the automotive industry.

2. Seamless Integration

   • Incorporate verified automotive data into your CRM, marketing automation platforms, or supply chain management systems via APIs or downloadable formats.

3. Data Accuracy with AI Validation

   • Trust in 99% accuracy to guide data-driven d...

During the 2014 ITS World Congress a demonstration of the connected vehicle infrastructure in the City of Detroit was conducted. The test site included approximately 14 intersections around Detroit’s COBO convention center and involved 9 equipped vehicles. The Vehicle Situation Data (VSD) data set includes a series of data files that recorded vehicle situational data that were generated by an equipped vehicle. During the ITS World Congress, VSDs were encoded with one of two schemas. The dataset contains decoded data using both 2.0 and 2.1 ASN.1 schemas.

PhysicalAI Autonomous Vehicles

🚨🚨🚨 We have temporarily removed a portion of clips from this dataset pending an internal review. We aim to restore these clips in the coming weeks (in repo-history-preserving fashion to minimize further disruption) and will remove this alert when this process has been completed. 🚨🚨🚨

  Dataset Description

The PhysicalAI-Autonomous-Vehicles dataset provides one of the largest, most geographically diverse collections of multi-sensor data… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/PhysicalAI-Autonomous-Vehicles.

This dataset contains V2X data collected from the Utah Connected Vehicle Data Ecosystem Program. We have submitted 7 days of Signal Status Message (SSM) in J2735 standards from 3 intersections in Orem, UT. This includes: (1) vehicles equipped with OBUs (Onboard Units) for real-time V2X (Vehicle-to-Everything) data transmission and reception, (2) road-side units (RSUs) capable of capturing, transmitting, and processing data from the transportation environment, and (3) a secure, scalable cloud platform for aggregating, analyzing, and visualizing transportation data.

task_categories: - robotics tags: - physicalAI

  🚀 News Update (October 22, 2025 - Many More Scenes and Better Ease of Use)!!

We have now:

Increased our number of NuRec scenes to 924!! Added labels.json file for helping users who want to search by types of scenes based on: behavior, layout, lighting, road types, surface conditions, traffic density, vrus presence, and weather. (Note this is only available for files under Batch0002 and onwards) A front camera video file… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/PhysicalAI-Autonomous-Vehicles-NuRec.

Part of Wyoming Department of Transportation Connected Vehicle Pilot Phase 4. Test case WV2IMCT-1 Verify V2I communication for log file offload.

Test case WFCW-1 Results - FCW Stopped Vehicle Rep 2

This dataset contains V2X data collected from the Utah Connected Vehicle Data Ecosystem Program. We have submitted 7 days of deduplicated Basic Safety Messages (BSMs) in J2735 standards from 3 intersections in Orem, UT.This includes: (1) vehicles equipped with OBUs (Onboard Units) for real-time V2X (Vehicle-to-Everything) data transmission and reception, (2) road-side units (RSUs) capable of capturing, transmitting, and processing data from the transportation environment, and (3) a secure, scalable cloud platform for aggregating, analyzing, and visualizing transportation data.

WFCW-3 FCW Slow Moving Vehicle Rep 1

Please refer to https://huggingface.co/datasets/nvidia/PhysicalAI-Autonomous-Vehicle-Cosmos-Drive-Dreams

This dataset is the output of an advanced vehicular simulation performed using the VEINS OMNeT++ framework. The simulation was designed to model realistic vehicle dynamics, sensor readings, and communication parameters over a network of urban roadways connecting major US cities. The dataset provides a comprehensive view of vehicular performance and environmental conditions over a 30-day period for 500 vehicles.

Authors: Muhammad Ali & Tariq Qayyum

Below is a detailed description of each feature included in the dataset:

Temporal and Geospatial Data

VehicleID: A unique identifier for each vehicle in the simulation. Timestamp: The exact date and time for each record, allowing temporal analysis of vehicle behavior. Day, Hour, Minute, Second: These fields break down the timestamp into finer granularity for detailed time-series analysis. Latitude and Longitude: The real-time geographic coordinates of the vehicle as it progresses along its route. StartingPointLatitude and StartingPointLongitude: The coordinates of the vehicle's origin (selected from major US cities). DestinationLatitude and DestinationLongitude: The target coordinates where the vehicle is headed. Mobility and Performance Metrics

Speed: The current speed of the vehicle (in km/h), adjusted based on simulated traffic, weather conditions, and road quality. Direction: The vehicle's heading in degrees, indicating its travel direction. Odometer: The cumulative distance traveled by the vehicle during the simulation. VehicleType: Categorical variable indicating the type of vehicle (Car, Truck, Bus, Motorcycle). VehicleAge: The age of the vehicle, measured in years. EngineTemperature: The simulated engine temperature (in °C), which responds dynamically to vehicle speed. FuelLevel: The remaining fuel level, tracking consumption over distance traveled. BatteryLevel: The current battery level, which decreases based on speed and usage. TirePressure, BrakeFluidLevel, CoolantLevel, OilLevel, WiperFluidLevel: These parameters simulate vehicle maintenance metrics, reflecting system status during operation. Environmental and Traffic Conditions

WeatherCondition: The prevailing weather conditions (Clear, Rain, Fog, or Snow) set for each simulation day. RoadCondition: A categorical assessment of road quality (Good, Moderate, or Poor). TrafficDensity: The simulated traffic level at each time step (Low, Medium, or High), affecting vehicle speed and behavior. Communication and Processing Attributes

CPU_Available and Memory_Available: Simulated computational resource levels that might impact on-board processing and task execution. NetworkLatency: The simulated network latency in milliseconds, indicating communication delays. SignalStrength: The wireless signal strength measured in dBm. GPSStatus, WiFiStatus, BluetoothStatus, CellularStatus: Boolean flags indicating the operational status of various communication systems on the vehicle. RadarStatus, LidarStatus, CameraStatus, IMUStatus: Status indicators for the vehicle’s sensor suite, providing situational awareness. TaskType: The category of a computational or communication task being executed (Navigation, Entertainment, DataAnalysis, or Safety). TaskSize: A numeric value representing the computational or data size requirement of the task. TaskPriority: The priority level of the task (High, Medium, or Low), potentially affecting its execution order. TaskOffloaded: A boolean flag indicating whether a task was offloaded, influenced by the vehicle’s battery level and simulated decision-making. Additional Vehicle Features

HeadlightStatus: A boolean indicating if the headlights are on, based on the time of day and weather conditions. BrakeLightStatus: Indicates whether brake lights are activated when the vehicle is decelerating. TurnSignalStatus: A boolean showing if the vehicle’s turn signal is active. HazardLightStatus: Indicates the status of the hazard lights. ABSStatus: Reflects the functioning status of the Anti-lock Braking System. AirbagStatus: Indicates whether the airbag system is active (typically remains enabled during the simulation). Simulation Environment

The dataset encapsulates a realistic simulation of vehicular movement and behavior in urban settings. Key aspects include:

Dynamic Speed Adjustment: Vehicles adjust speeds based on time-of-day (e.g., rush hour), weather impacts, and traffic density. Resource Management: The simulation models the gradual depletion of battery, fuel, and other vehicular maintenance parameters. Sensor and Communication Systems: Real-time statuses of sensor suites and communication modules are recorded, emulating modern connected vehicles. Route Progression: Each vehicle's journey is tracked from a starting city to a destination, with geospatial progression computed using a distance estimation formula. Potential Applications

Researchers and practitioners can utilize thi...

The Vehicle License Plate Images (US) dataset contains diverse car images with visible license plates, supporting autonomous driving, traffic monitoring, and computer vision applications such as license plate detection and recognition.

This dataset contains V2X data collected from the Utah Connected Vehicle Data Ecosystem Program. We have submitted 7 days of deduplicated Signal Request Messages (SRMs) in J2735 standards from 3 intersections in Orem, UT. This includes: (1) vehicles equipped with OBUs (Onboard Units) for real-time V2X (Vehicle-to-Everything) data transmission and reception, (2) road-side units (RSUs) capable of capturing, transmitting, and processing data from the transportation environment, and (3) a secure, scalable cloud platform for aggregating, analyzing, and visualizing transportation data.

Unlock insights into road scenes with our comprehensive Vehicle Image Captioning Dataset. This dataset comprises a diverse collection of images capturing vehicles in various settings. Each image is accompanied by detailed captions generated and verified by humans.

These captions, following a specific question format, describe every object on the road, including vehicle color, windshield presence, door and window status, vehicle type, visible wheels, number plate details, logos or brands, vehicle and people activity, and background description. With a 60-70 word description, this dataset offers rich contextual information for image understanding and captioning tasks.

Optimized for Generative AI, Visual Question Answering, Image Classification, and LMM development, this dataset provides a strong basis for achieving robust model performance.

Features:

• 1000+ high-resolution images captured across diverse Indian road scenes.
• Detailed captions describing each object on the road from left to right.
• Captions include vehicle color, windshield presence, door and window status, vehicle type, visible wheels, number plate details, logos or brands, vehicle and people activity, and background description.
• Images sourced from various cities and regions across India, covering day and night scenarios, varied distances, different backgrounds, viewpoints, and more.
• Ideal for image captioning, object detection, scene understanding, and AI research tasks.

Applications:

• Image captioning and description generation.
• Object detection and recognition.
• Autonomous vehicle navigation and scene understanding.
• Traffic analysis and management.
• Urban planning and infrastructure development.

Dataset with Bounding Boxes: The dataset also includes bounding box annotation for Indian Vehicles in 15+ classes. To access the dataset, please visit: https://www.kaggle.com/datasets/dataclusterlabs/indian-vehicle-dataset

The images in this dataset are exclusively owned by Data Cluster Labs and were not downloaded from the internet. To access a larger portion of the training dataset for research and commercial purposes, a license can be purchased. For more details contact us at sales@datacluster.ai or visit www.datacluster.ai

Automotive Scenario Database Market Outlook

According to our latest research, the global automotive scenario database market size reached $1.37 billion in 2024, driven by escalating investments in autonomous vehicle development and advanced driver assistance systems (ADAS). With a robust compound annual growth rate (CAGR) of 15.8% projected from 2025 to 2033, the market is forecasted to soar to $4.91 billion by 2033. This impressive growth trajectory is fueled primarily by the increasing complexity of vehicle automation and the need for comprehensive scenario databases to validate, test, and enhance the safety and reliability of next-generation automotive technologies.

One of the most significant growth factors for the automotive scenario database market is the rapid evolution of autonomous vehicles. As the automotive industry shifts towards higher levels of automation, the necessity for extensive and diverse scenario databases becomes paramount. These databases are essential for simulating a wide array of real-world and synthetic driving conditions, enabling manufacturers and developers to test and refine the performance of autonomous systems without the risks and constraints of physical road testing. The proliferation of machine learning and artificial intelligence in automotive applications further accentuates the need for robust scenario datasets, as these technologies rely heavily on large volumes of varied and high-quality data for training and validation.

Another major driver is the tightening regulatory landscape and the growing emphasis on vehicle safety. Regulatory bodies across North America, Europe, and Asia Pacific are introducing stringent standards for the testing and validation of autonomous and semi-autonomous vehicles. Compliance with these regulations necessitates the use of well-structured scenario databases that can replicate a multitude of traffic, weather, and pedestrian scenarios. Automotive OEMs and Tier 1 suppliers are increasingly investing in scenario database solutions to ensure their vehicles meet or exceed regulatory requirements, minimize liability, and enhance consumer trust in automated driving technologies.

The surge in connected vehicle technologies and the integration of ADAS features are also propelling the automotive scenario database market forward. As vehicles become more connected and equipped with advanced sensors, the scope for scenario-based testing expands significantly. Scenario databases enable manufacturers to simulate complex interactions between vehicles, infrastructure, and other road users, supporting the development of sophisticated ADAS functionalities such as collision avoidance, lane keeping, and adaptive cruise control. The ongoing digital transformation of the automotive sector, coupled with the adoption of cloud computing and big data analytics, is further amplifying the demand for scalable and easily accessible scenario database platforms.

From a regional perspective, North America currently holds the largest share of the automotive scenario database market, underpinned by the presence of leading technology companies, automotive OEMs, and regulatory frameworks that support autonomous vehicle testing. Europe follows closely, benefiting from strong government initiatives, a mature automotive industry, and a collaborative ecosystem involving research institutes and regulatory bodies. The Asia Pacific region is emerging as a high-growth market, driven by rapid urbanization, increasing investments in smart mobility, and the expansion of automotive manufacturing hubs. Latin America and the Middle East & Africa are gradually catching up, supported by rising interest in vehicle automation and mobility innovation.

Database Type Analysis

The automotive scenario database market is segmented by database type into simulation scenario databases, real-world scenario databases, and synthetic scenario databases. Simulation scenario databases are pivotal for virtual testing environments, allowi

Synthetic Data Privacy for Automotive Market Outlook

According to our latest research, the synthetic data privacy for automotive market size reached USD 1.21 billion globally in 2024. The market is exhibiting robust momentum, driven by the escalating integration of AI and data-driven technologies in the automotive sector. The market is forecasted to expand at a CAGR of 28.6% from 2025 to 2033, reaching a projected value of USD 10.43 billion by 2033. This remarkable growth trajectory is fueled by stringent data privacy regulations, the proliferation of connected and autonomous vehicles, and the increasing need for high-quality, privacy-compliant data for training advanced automotive systems.

One of the primary growth drivers for the synthetic data privacy for automotive market is the intensifying regulatory landscape surrounding data privacy and protection. With regulations such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States, automotive manufacturers and technology providers face mounting pressure to safeguard personal and sensitive data collected from vehicles and users. Synthetic data, which is artificially generated and devoid of real-world personal identifiers, offers a compelling solution to these compliance challenges. By enabling the creation of large, diverse, and privacy-preserving datasets, synthetic data empowers automotive companies to accelerate the development and validation of AI models, while ensuring adherence to global data privacy standards.

Another significant factor propelling market growth is the rapid adoption of advanced driver-assistance systems (ADAS), autonomous vehicles, and connected vehicle technologies. These innovations rely heavily on vast quantities of high-fidelity data to train machine learning algorithms for perception, decision-making, and safety. However, collecting and annotating real-world data at scale is both time-consuming and fraught with privacy risks. Synthetic data bridges this gap by providing scalable, customizable, and privacy-compliant datasets that mirror real-world scenarios. This capability not only expedites the development cycle but also enhances the robustness and generalizability of AI models, thereby accelerating the commercialization of next-generation automotive technologies.

The market is further buoyed by the growing collaboration between automotive original equipment manufacturers (OEMs), Tier 1 suppliers, and technology firms to harness synthetic data for simulation, testing, and validation purposes. As the automotive industry transitions toward software-defined vehicles and mobility-as-a-service models, the demand for secure, reliable, and diverse data sources is intensifying. Synthetic data privacy solutions are emerging as a strategic enabler, facilitating secure data sharing, cross-organizational collaboration, and the development of innovative mobility solutions. This trend is particularly pronounced in regions with high automotive R&D activity, such as North America, Europe, and Asia Pacific, where investments in AI-driven automotive technologies are surging.

Regionally, North America and Europe are at the forefront of synthetic data privacy adoption in the automotive sector, driven by robust regulatory frameworks, advanced technological infrastructure, and a strong presence of leading automotive and technology companies. Asia Pacific is rapidly catching up, fueled by the expansion of smart mobility initiatives, government support for autonomous driving, and the proliferation of connected vehicles in markets such as China, Japan, and South Korea. Latin America and the Middle East & Africa are also witnessing gradual uptake, albeit at a slower pace, as automotive innovation and regulatory awareness continue to grow. Overall, the global synthetic data privacy for automotive market is poised for exponential growth, underpinned by technological advancements, regulatory imperatives, and the relentless pursuit of data-driven innovation.

Component Analysis

WFCW-2 Stopped Vehicle Message Prioritization Rep 2