The NHTSA Product Information Catalog and Vehicle Listing (vPIC) is a consolidated platform that presents data collected within the manufacturer reported data from CFR 49 Parts 551 - 574 for use in a variety of modern tools. NHTSA's vPIC platform is intended to serve as a centralized source for basic Vehicle Identification Number (VIN) decoding, Manufacturer Information Database (MID), Manufacturer Equipment Plant Identification and associated data. vPIC is intended to support the Open Data and Transparency initiatives of the agency by allowing the data to be freely used by the public without the burden of manual retrieval from a library of electronic documents (PDFs). While these documents will still be available online for viewing within the Manufacturer Information Database (MID) module of vPIC one can view and use the actual data through the VIN Decoder and Application Programming Interface (API) modules.

Manufacturers who determine that a product or piece of original equipment either has a safety defect or is not in compliance with Federal safety standards are required to notify the National Highway Traffic Safety Administration (NHTSA) within 5 business days. NHTSA requires that manufacturers file a Defect and Noncompliance report as well as quarterly recall status reports, in compliance with Federal Regulation 49 (the National Traffic and Motor Safety Act) Part 573, which identifies the requirements for safety recalls. This information is stored in the NHTSA database. Use this data to search for recall information related to:- Specific NHTSA campaigns - Product types Access to public searches of NHTSA recall databases for tires, vehicles, car seats and equipment.

The NHTSA Product Information Catalog and Vehicle Listing (vPIC) is a consolidated platform that presents data collected within the manufacturer reported data from CFR 49 Parts 551 - 574 for use in a variety of modern tools. NHTSA's vPIC platform is intended to serve as a centralized source for basic Vehicle Identification Number (VIN) decoding, Manufacturer Information Database (MID), Manufacturer Equipment Plant Identification and associated data. vPIC is intended to support the Open Data and Transparency initiatives of the agency by allowing the data to be freely used by the public without the burden of manual retrieval from a library of electronic documents (PDFs). While these documents will still be available online for viewing within the Manufacturer Information Database (MID) module of vPIC one can view and use the actual data through the VIN Decoder and Application Programming Interface (API) modules.

The NHTSA Product Information Catalog and Vehicle Listing (vPIC) is a consolidated platform that presents data collected within the manufacturer reported data from CFR 49 Parts 551 - 574 for use in a variety of modern tools. NHTSA's vPIC platform is intended to serve as a centralized source for basic Vehicle Identification Number (VIN) decoding, Manufacturer Information Database (MID), Manufacturer Equipment Plant Identification and associated data. vPIC is intended to support the Open Data and Transparency initiatives of the agency by allowing the data to be freely used by the public without the burden of manual retrieval from a library of electronic documents (PDFs). While these documents will still be available online for viewing within the Manufacturer Information Database (MID) module of vPIC one can view and use the actual data through the VIN Decoder and Application Programming Interface (API) modules.

Complaint information entered into NHTSA’s Office of Defects Investigation vehicle owner's complaint database is used with other data sources to identify safety issues that warrant investigation and to determine if a safety-related defect trend exists. Complaint information is also analyzed to monitor existing recalls for proper scope and adequacy.Source: https://www.nhtsa.gov/nhtsa-datasets-and-apis#complaintsNotes: Complaints data was created from 1995-present, covering vehicles from 1949-present.

Quick Start

For a quick introduction to this Dataset, take a look at the Kernel Traffic Fatalities Getting Started.

See the "https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812315">Fatality Analysis Reporting System FARS User’s Manual for understanding the column abbreviations and possible values.

Also, see the following reference

Original source of this data containing all files can be obtained here

Below are the files released by the (NHTSA) National Highway Traffic Safety Administration, in their original format. All but 6 files are included in this Dataset.

The NHTSA Product Information Catalog and Vehicle Listing (vPIC) is a consolidated platform that presents data collected within the manufacturer reported data from CFR 49 Parts 551 - 574 for use in a variety of modern tools. NHTSA's vPIC platform is intended to serve as a centralized source for basic Vehicle Identification Number (VIN) decoding, Manufacturer Information Database (MID), Manufacturer Equipment Plant Identification and associated data. vPIC is intended to support the Open Data and Transparency initiatives of the agency by allowing the data to be freely used by the public without the burden of manual retrieval from a library of electronic documents (PDFs). While these documents will still be available online for viewing within the Manufacturer Information Database (MID) module of vPIC one can view and use the actual data through the VIN Decoder and Application Programming Interface (API) modules.

After a preliminary review of consumer complaints and other information related to alleged defects, NHTSA obtains information from the manufacturer(including data on complaints, crashes, injuries, warranty claims, modifications, and part sales) and determines whether further analysis is warranted. If warranted, the investigator will conduct a more detailed and complete analysis of the character and scope of the alleged defect.Source: https://www.nhtsa.gov/nhtsa-datasets-and-apis#investigations

Complaint information entered into NHTSA-ODI's vehicle owner's complaint database is used with other data sources to identify safety issues that warrant investigation and to determine if a safety-related defect trend exists. Complaint information is also analyzed to monitor existing recalls for proper scope and adequacy.

The NHTSA Product Information Catalog and Vehicle Listing (vPIC) is a consolidated platform that presents data collected within the manufacturer reported data from CFR 49 Parts 551 - 574 for use in a variety of modern tools. NHTSA's vPIC platform is intended to serve as a centralized source for basic Vehicle Identification Number (VIN) decoding, Manufacturer Information Database (MID), Manufacturer Equipment Plant Identification and associated data. vPIC is intended to support the Open Data and Transparency initiatives of the agency by allowing the data to be freely used by the public without the burden of manual retrieval from a library of electronic documents (PDFs). While these documents will still be available online for viewing within the Manufacturer Information Database (MID) module of vPIC one can view and use the actual data through the VIN Decoder and Application Programming Interface (API) modules.

Contains public complaint data from the NHTSA complaint database.

https://www.nhtsa.gov/nhtsa-datasets-and-apis#complaints

Data was pulled for Model S, Model X, Model Y, and Model 3 cars from 2012-2022.

The Child Safety Seat Inspection Station Locations are used to make it easier for all citizens to get their Child Safety Seats properly installed. Car crashes are the largest cause of fatalities and serious injuries for children between ages 2 and 15. Also, surveys indicate that a high percentage of Child Safety Seats are not installed properly. Information updates for each station are reported to NHTSA and enterred by NHTSA staff. NHTSA staff will also attempt to validate the station locations using a comercial Geographic database so this data will, in most cases, be able to be used for driving directions.

The files in this data environment were produced using the Vehicle Awareness Device (VAD) installed on one test vehicle over a two month period. The VAD installed in the test car is identical to the VADs installed in over 2800 vehicles participating in the Safety Pilot Model Demonstration conducted from August 2012 through August 2013 by the National Highway Traffic Safety Administration (NHTSA) in Ann Arbor, Michigan.

This legacy dataset was created before data.transportation.gov and is only currently available via the attached file(s). Please contact the dataset owner if there is a need for users to work with this data using the data.transportation.gov analysis features (online viewing, API, graphing, etc.) and the USDOT will consider modifying the dataset to fully integrate in data.transportation.gov.

Fatality Analysis Reporting System (FARS) was created in the United States by the National Highway Traffic Safety Administration (NHTSA) to provide an overall measure of highway safety, to help suggest solutions, and to help provide an objective basis to evaluate the effectiveness of motor vehicle safety standards and highway safety programs.

FARS contains data on a census of fatal traffic crashes within the 50 States, the District of Columbia, and Puerto Rico. To be included in FARS, a crash must involve a motor vehicle traveling on a trafficway customarily open to the public and result in the death of a person (occupant of a vehicle or a non-occupant) within 30 days of the crash. FARS has been operational since 1975 and has collected information on over 989,451 motor vehicle fatalities and collects information on over 100 different coded data elements that characterizes the crash, the vehicle, and the people involved.

FARS is vital to the mission of NHTSA to reduce the number of motor vehicle crashes and deaths on our nation's highways, and subsequently, reduce the associated economic loss to society resulting from those motor vehicle crashes and fatalities. FARS data is critical to understanding the characteristics of the environment, trafficway, vehicles, and persons involved in the crash.

NHTSA has a cooperative agreement with an agency in each state government to provide information in a standard format on fatal crashes in the state. Data is collected, coded and submitted into a micro-computer data system and transmitted to Washington, D.C. Quarterly files are produced for analytical purposes to study trends and evaluate the effectiveness highway safety programs.

Content

There are 40 separate data tables. You can find the manual, which is too large to reprint in this space, here.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.nhtsa_traffic_fatalities.[TABLENAME]. Fork this kernel to get started.

Acknowledgements

This dataset was provided by the National Highway Traffic Safety Administration.

Motivation

Drowsiness is an intermediate condition that fluctuates between alertness and sleep. It reduces the consciousness level andhinders a person from responding quickly to important road safety issues [1]. The American Automobile Association (AAA) has reported that about 24% of 2,714 drivers that participated in a survey revealed being extremely drowsy while driving, at least once in the last month [2]. In 2017, the National Highway Transportation Safety Administration (NHTSA) also reported 795 fatalities in motor vehicle crashes involving drowsy drivers [3]. Drowsy driving has caused about 2.5% of fatal accidents from 2011 through 2015 in the USA, and it is estimated to produce an economic loss of USD 230 billion annually [4]. Klauer et al. have found in their study that drowsy drivers contributed to 22-24% of crashes or near-crash risks [5]. The German Road Safety Council (DVR) has reported that one out of four fatal highway crashes has been caused by drowsy drivers [6]. In a study carried out in 2015, it has been reported that the average prevalence of falling asleep while driving in the previous two years was about 17% in 19 European countries [6]. The results of these studies emphasize the importance of detecting drowsiness early enough to initiate preventive measures. Drowsiness detection systems are intended to warn the drivers before an upcoming level of drowsiness gets critical to prevent drowsiness-related accidents.

Intelligent Systems that automate motor vehicle driving on the roads are being introduced to the market step-wise. The Society of Automotive Engineers (SAE) issued a standard defining six levels ranging from no driving automation (level 0) to full driving automation (level 5) [7]. While the SAE levels 0-2 require that an attentive driver carries out or at least monitors the dynamic driving task, in the SAE level 3 of automated driving, drivers will be allowed to do a secondary task allowing the system to control the vehicle under limited conditions, e.g., on a motorway. Still, the automation system has to hand back the vehicle guidance to the driver whenever it cannot control the state of the vehicle any more. However, the handover of vehicle control to a drowsy driver is not safe. Therefore, the system should be informed about the state of the driver.

To date, different Advanced Driver Assistance Systems (ADAS) have been made by car manufactures and researchers to improve driving safety and manage the traffic flow. ADAS systems have been benefited from advanced machine perception methods, improved computing hardware systems, and intelligent vehicle control algorithms. By recently increasing the availability of huge amounts of sensor data to ADAS, data-driven approaches are extensively exploited to enhance their performance. The driver drowsiness detection systems have gained much attention from researchers. Before its use in the development of driving automation, drowsiness warning systems have been produced for the direct benefit of avoiding accidents.

The aim of the WACHSens project was to collect a big data set to detect the different levels of driver drowsiness during performing two different driving modes: manual and automated.

To retrieve this data set, please send a request to: arno.eichberger@tugraz.at

References:

[1] M. Awais, N. Badruddin, and M. Drieberg, "A Hybrid Approach to Detect Driver Drowsiness Utilizing Physiological Signals to Improve System Performance and Wearability,"Sensors, vol. 17, no. 9, 2017, doi: 10.3390/s17091991

[2] AAA Foundation for Traffic Safety, "2019 Traffic Safety Culture Index (Technical Report), June 2020," Washington, D.C., Jun. 2020. [Online]. Available: https://aaafoundation.org/2019-traffic-safety-culture-index/

[3] National Highway Traffic Safety Administration, "Traffic Safety Facts: 2017 Fatal Motor Vehicle Crashes: Overview," NHTSA's National Center for Statistics and Analysis, 1200 New Jersey Avenue SE., Washington DOT HS 812 603, Oct. 2018. Accessed: Apr. 14 2021. [Online]. Available: https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812603

[4] Agustina Garcés Correa, Lorena Orosco, and Eric Laciar, "Automatic detection of drowsiness in EEG records based on multimodal analysis," Medical Engineering & Physics, vol. 36, no. 2, pp. 244–249, 2014, doi: 10.1016/j.medengphy.2013.07.011

[5] S. Klauer, V. Neale, T. Dingus, Jeremy Sudweeks, and D. J. Ramsey, "The Prevalence of Driver Fatigue in an Urban Driving Environment : Results from the 100-Car Naturalistic Driving Study," in 2006.

[6] Fraunhofer-Gesellschaft,Eyetracker warns against momentary driver drowsiness - Press Release Oktober 12, 2010. [Online]. Available: https://www.fraunhofer.de/en/press/research-news/2010/10/eye-tracker-driver-drowsiness.html (accessed: Apr. 14 2021).

[7] T. Inagaki and T. B. Sheridan, "A critique of the SAE conditional driving automation definition, and analyses of options for improvement," Cogn Tech Work, vol. 21, no. 4, pp. 569–578, 2019, doi: 10.1007/s10111-018-0471-5

Research shows that crime and pedestrian related injuries/fatalities can be reduced through adequate street lighting. This map indicates who is responsible for the ABQ area street lights and which communities are impacted more than others. This map contains layers derived from the following sources: Citelum Group, a subcontractor of the City of Albuquerque, Street Light DataUS Census Bureau's American Community Survey Race/Ethnicity and Median Family Income DateNM Department of Transportation Motor Vehicle Crash DataCity of Albuquerque Sector Development Plan International District Boundaries DataSee also - https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812493This map runs this App - https://nmcdc.maps.arcgis.com/home/item.html?id=64b591c04b544f1b97a927ec57c3f01d

Content

The Motor Vehicle Collisions crash table contains details on the crash event. Each row represents a crash event. The Motor Vehicle Collisions data tables contain information from all police reported motor vehicle collisions in NYC. The police report (MV104-AN) is required to be filled out for collisions where someone is injured or killed, or where there is at least $1000 worth of damage (https://www.nhtsa.gov/sites/nhtsa.dot.gov/files/documents/ny_overlay_mv-104an_rev05_2004.pdf). It should be noted that the data is preliminary and subject to change when the MV-104AN forms are amended based on revised crash details.

Due to success of the CompStat program, NYPD began to ask how to apply the CompStat principles to other problems. Other than homicides, the fatal incidents with which police have the most contact with the public are fatal traffic collisions. Therefore in April 1998, the Department implemented TrafficStat, which uses the CompStat model to work towards improving traffic safety. Police officers complete form MV-104AN for all vehicle collisions. The MV-104AN is a New York State form that has all of the details of a traffic collision. Before implementing Trafficstat, there was no uniform traffic safety data collection procedure for all of the NYPD precincts. Therefore, the Police Department implemented the Traffic Accident Management System (TAMS) in July 1999 in order to collect traffic data in a uniform method across the City. TAMS required the precincts manually enter a few selected MV-104AN fields to collect very basic intersection traffic crash statistics which included the number of accidents, injuries and fatalities. As the years progressed, there grew a need for additional traffic data so that more detailed analyses could be conducted. The Citywide traffic safety initiative, Vision Zero started in the year 2014. Vision Zero further emphasized the need for the collection of more traffic data in order to work towards the Vision Zero goal, which is to eliminate traffic fatalities. Therefore, the Department in March 2016 replaced the TAMS with the new Finest Online Records Management System (FORMS). FORMS enables the police officers to electronically, using a Department cellphone or computer, enter all of the MV-104AN data fields and stores all of the MV-104AN data fields in the Department’s crime data warehouse. Since all of the MV-104AN data fields are now stored for each traffic collision, detailed traffic safety analyses can be conducted as applicable.

Context

This is a dataset hosted by the City of New York. The city has an open data platform found here and they update their information according the amount of data that is brought in. Explore New York City using Kaggle and all of the data sources available through the City of New York organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by Marc-Olivier Jodoin on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

According to Cognitive Market Research, the global Advanced Driver Assistance Systems Market size will be USD 30628.5million in 2025. It will expand at a compound annual growth rate (CAGR) of 11.50% from 2025 to 2033.

North America held the major market share for more than 37% of the global revenue with a market size of USD 11332.55 million in 2025 and will grow at a compound annual growth rate (CAGR) of 9.9% from 2025 to 2033.
Europe accounted for a market share of over 29% of the global revenue with a market size of USD 8882.27million.
APAC held a market share of around 24% of the global revenue with a market size of USD 7350.84million in 2025 and will grow at a compound annual growth rate (CAGR) of 14.3% from 2025 to 2033.
South America has a market share of more than 4% of the global revenue with a market size of USD 1163.88million in 2025 and will grow at a compound annual growth rate (CAGR) of 12.2% from 2025 to 2033.
Middle East had a market share of around 4.00% of the global revenue and was estimated at a market size of USD 1225.14million in 2025 and will grow at a compound annual growth rate (CAGR) of 12.8%from 2025 to 2033.
Africa had a market share of around 2.20% of the global revenue and was estimated at a market size of USD 673.83million in 2025 and will grow at a compound annual growth rate (CAGR) of 11.8%from 2025 to 2033.
Level-4 category is the fastest growing segment of the Advanced Driver Assistance Systems industry

Market Dynamics of Advanced Driver Assistance Systems Market

Key Drivers for Advanced Driver Assistance Systems Market

Increasing attention to vehicle safetyto Boost Market Growth

In order to reduce the likelihood of accidents and raise overall safety standards, governments, regulatory agencies, and consumers are placing a greater emphasis on automobiles with cutting-edge safety measures. Alarming numbers of traffic fatalities and injuries have prompted automakers and government agencies to take proactive steps to solve this urgent problem, which has led to a greater focus on the problem. In order to enhance safety results and lower the probability of accidents, governments are implementing strict laws and safety requirements requiring the incorporation of Advanced Driver Assistance Systems technologies into automobiles. From the fundamental specifications for vital safety elements like electronic stability control (ESC) and anti-lock braking systems (ABS) to more sophisticated ADAS features like adaptive cruise control and collision avoidance systems, these rules cover it all. The National Motor Vehicle Crash Causation Survey (NMVCCS) reports that driver error is to blame for almost 90% of auto accidents in the United States. ADAS improves vehicle, occupant, and pedestrian safety and security while assisting in the reduction of traffic jams and traffic accidents. Furthermore, governments throughout the world are investing heavily in the development of safety measures, the adoption of cutting-edge technologies, and the support of automated vehicles. Government regulations also require automakers to incorporate various cutting-edge driver aid systems into every vehicle.

https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/811059

Increasing Government Safety Laws and Guidelines to Boosts the Need for Advanced Driver Assistance Systems to Boost Market Growth

Governments have put laws and rules into place requiring automobiles to have safety features like tire pressure monitoring systems and lane departure warning systems installed within a certain time frame. High-tech braking systems would be a requirement for all new commercial vehicle types in India. The motor vehicle statute is amended to accomplish this. Car airbags, passenger seat belts, lane departure warning systems, driver monitoring systems, drowsiness monitoring systems, child safety systems, pedestrian safety systems, and other technology are now required by law in a number of nations.The sale of luxury cars is anticipated to rise in tandem with consumers' increased disposable income and purchasing power, which will propel the global market for cutting-edge driver aid technologies.

Restraint Factor for the Advanced Driver Assistance Systems Market

Lack of Infrastructural Development will make growth difficult of Advanced Driver Assistance Systems Limit Market Growth, Will Limit Market Growth

One of the biggest obstacles to the widespread implementation of A...

The International District in Southeast Albuquerque sees more than its share of pedestrians being hit and killed by drivers. The data show that there are fewer streetlights and more pedestrian fatalities in this area compared to other neighborhoods across the City. This map also includes the International District boundaries for ease of analysis. This map contains layers derived from the following sources: Citelum Group, a subcontractor of the City of Albuquerque, Street Light DataUS Census Bureau's American Community Survey Race/Ethnicity and Median Family Income DateNM Department of Transportation Motor Vehicle Crash DataCity of Albuquerque Sector Development Plan International District Boundaries DataSee also - https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812493This map runs this App - https://nmcdc.maps.arcgis.com/home/item.html?id=9fc5f5d5ab7e4cf38263f49709d41971

Rate of deaths by age/gender (per 100,000 population) for motor vehicle occupants killed in crashes, 2012 & 2014. 2012 Source: Fatality Analysis Reporting System (FARS). 2014 Source: National Highway Traffic Safety Administration's (NHTSA) Fatality Analysis Reporting System (FARS), 2014 Annual Report File Note: Blank cells indicate data are suppressed. Fatality rates based on fewer than 20 deaths are suppressed.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.