Total Vehicle Sales in the United States decreased to 16.10 Million in August from 16.40 Million in July of 2025. This dataset provides the latest reported value for - United States Total Vehicle Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Autos include all passenger cars, including station wagons. The U.S. Bureau of Economic Analysis releases auto and truck sales data, which are used in the preparation of estimates of personal consumption expenditures.

Total Vehicle Sales in China increased to 2857000 Units in August from 2593000 Units in July of 2025. This dataset provides - China Total Vehicle Sales- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Car Registrations in Canada increased to 179814 Units in July from 177219 Units in June of 2025. This dataset provides - Canada Total New Cars - actual values, historical data, forecast, chart, statistics, economic calendar and news.

In 2024, the auto industry in the United States sold approximately 15.9 million light vehicle units. This figure includes retail sales of about three million passenger cars and just under 12.9 million light trucks. Lower fuel consumption There are many kinds of light vehicles available in the United States. Light-duty vehicles are popular for their utility and improved fuel economy, making them an ideal choice for savvy consumers. As of Model Year 2023, the light vehicle manufacturer with the best overall miles per gallon was Kia, with one gallon of gas allowing for 30.4 miles on the road. Higher brand satisfaction When asked about light vehicle satisfaction, consumers in the United States were most satisfied with Toyota, Subaru, Tesla, and Mercedes-Benz models. Another survey conducted in 2018 and quizzing respondents on their stance regarding the leading car brands indicated that Lexus was among the most dependable brands based on the number of problems reported per 100 vehicles.

Total Vehicle Sales in Thailand decreased to 47622 Units in August from 49102 Units in July of 2025. This dataset provides - Thailand Total Vehicle Sales- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Total Vehicle Sales in Chile increased to 27462 Units in August from 26909 Units in July of 2025. This dataset provides - Chile Total Vehicle Sales- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Quarterly data on vehicle registration by fuel type, vehicle type and number of vehicles, Canada, the provinces, census metropolitan areas and census sub-divisions.

Number of units and total sales of new motor vehicles by type of vehicle, annual.

Global motor vehicle production increased by about 10.3 percent between 2022 and 2023, showing signs of recovery after the COVID-19 pandemic severely disrupted the automotive industry. Further, global motor vehicle production output surpassed pre-pandemic values. While about 92 million motor vehicles were manufactured worldwide in 2019, some 94 million were produced in 2023. Trend of declining sales and production The decline in production started in 2017 as manufacturers were beginning to respond to a general slowdown in demand. While commercial vehicle sales increased between 2017 and 2019, the segment was impacted by the pandemic, sales dropping by nine percent in 2020. In 2021, the market slightly recovered with a six-percent-increase in sales year-over-year, and ultimately increased from 2022 to 2023. Industry hit heavily by the pandemic The coronavirus pandemic has caused disruptions in major economies forcing strict lockdown measures in some regions. The standstill was first seen in China, the world’s largest producer of cars and commercial vehicles, and quickly spreading throughout the world. Globally, motor vehicle production declined by more than 15 percent year-on-year between 2019 and 2020, the largest drop in output since the 2008-09 financial crisis.

Car Registrations in Vietnam decreased to 21688 Units in August from 26573 Units in July of 2025. This dataset provides - Vietnam Car Sales - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Car Registrations in Brazil decreased to 225400 Units in August from 243238 Units in July of 2025. This dataset provides the latest reported value for - Brazil New Vehicle Registrations - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Key information about United States Motor Vehicle Sales: Passenger Cars

• United States Motor Vehicle Sales: Passenger Cars was reported at 3,116,647.000 Unit in Dec 2023.
• This records an increase from the previous number of 2,858,575.000 Unit for Dec 2022.
• US Motor Vehicle Sales: Passenger Cars data is updated yearly, averaging 6,089,403.000 Unit from Dec 2005 to 2023, with 19 observations.
• The data reached an all-time high of 7,761,592.000 Unit in 2006 and a record low of 2,858,575.000 Unit in 2022.
• US Motor Vehicle Sales: Passenger Cars data remains active status in CEIC and is reported by International Organization of Motor Vehicle Manufacturers.
• The data is categorized under World Trend Plus’s Association: Automobile Sector – Table RA.OICA.MVS: Motor Vehicle Sales: by Country and Type: Passenger Car (PC).

Introduction

This repository hosts the Testing Roads for Autonomous VEhicLes (TRAVEL) dataset. TRAVEL is an extensive collection of virtual roads that have been used for testing lane assist/keeping systems (i.e., driving agents) and data from their execution in state of the art, physically accurate driving simulator, called BeamNG.tech. Virtual roads consist of sequences of road points interpolated using Cubic splines.

Along with the data, this repository contains instructions on how to install the tooling necessary to generate new data (i.e., test cases) and analyze them in the context of test regression. We focus on test selection and test prioritization, given their importance for developing high-quality software following the DevOps paradigms.

This dataset builds on top of our previous work in this area, including work on

• test generation (e.g., AsFault, DeepJanus, and DeepHyperion) and the SBST CPS tool competition (SBST2021),
• test selection: SDC-Scissor and related tool
• test prioritization: automated test cases prioritization work for SDCs.

Dataset Overview

The TRAVEL dataset is available under the data folder and is organized as a set of experiments folders. Each of these folders is generated by running the test-generator (see below) and contains the configuration used for generating the data (experiment_description.csv), various statistics on generated tests (generation_stats.csv) and found faults (oob_stats.csv). Additionally, the folders contain the raw test cases generated and executed during each experiment (test.).

The following sections describe what each of those files contains.

Experiment Description

The experiment_description.csv contains the settings used to generate the data, including:

• Time budget. The overall generation budget in hours. This budget includes both the time to generate and execute the tests as driving simulations.
• The size of the map. The size of the squared map defines the boundaries inside which the virtual roads develop in meters.
• The test subject. The driving agent that implements the lane-keeping system under test. The TRAVEL dataset contains data generated testing the BeamNG.AI and the end-to-end Dave2 systems.
• The test generator. The algorithm that generated the test cases. The TRAVEL dataset contains data obtained using various algorithms, ranging from naive and advanced random generators to complex evolutionary algorithms, for generating tests.
• The speed limit. The maximum speed at which the driving agent under test can travel.
• Out of Bound (OOB) tolerance. The test cases' oracle that defines the tolerable amount of the ego-car that can lie outside the lane boundaries. This parameter ranges between 0.0 and 1.0. In the former case, a test failure triggers as soon as any part of the ego-vehicle goes out of the lane boundary; in the latter case, a test failure triggers only if the entire body of the ego-car falls outside the lane.

Experiment Statistics

The generation_stats.csv contains statistics about the test generation, including:

• Total number of generated tests. The number of tests generated during an experiment. This number is broken down into the number of valid tests and invalid tests. Valid tests contain virtual roads that do not self-intersect and contain turns that are not too sharp.
• Test outcome. The test outcome contains the number of passed tests, failed tests, and test in error. Passed and failed tests are defined by the OOB Tolerance and an additional (implicit) oracle that checks whether the ego-car is moving or standing. Tests that did not pass because of other errors (e.g., the simulator crashed) are reported in a separated category.

The TRAVEL dataset also contains statistics about the failed tests, including the overall number of failed tests (total oob) and its breakdown into OOB that happened while driving left or right. Further statistics about the diversity (i.e., sparseness) of the failures are also reported.

Test Cases and Executions

Each test. contains information about a test case and, if the test case is valid, the data observed during its execution as driving simulation.

The data about the test case definition include:

• The road points. The list of points in a 2D space that identifies the center of the virtual road, and their interpolation using cubic splines (interpolated_points)
• The test ID. The unique identifier of the test in the experiment.
• Validity flag and explanation. A flag that indicates whether the test is valid or not, and a brief message describing why the test is not considered valid (e.g., the road contains sharp turns or the road self intersects)

The test data are organized according to the following JSON Schema and can be interpreted as RoadTest objects provided by the tests_generation.py module.

{
 "type": "object",
 "properties": {
  "id": { "type": "integer" },
  "is_valid": { "type": "boolean" },
  "validation_message": { "type": "string" },
  "road_points": { §\label{line:road-points}§
   "type": "array",
   "items": { "$ref": "schemas/pair" },
  },
  "interpolated_points": { §\label{line:interpolated-points}§
   "type": "array",
   "items": { "$ref": "schemas/pair" },
  },
  "test_outcome": { "type": "string" }, §\label{line:test-outcome}§
  "description": { "type": "string" },
  "execution_data": { 
   "type": "array",
   "items": { "$ref" : "schemas/simulationdata" }
  }
 },
 "required": [
  "id", "is_valid", "validation_message",
  "road_points", "interpolated_points"
 ]
}

Finally, the execution data contain a list of timestamped state information recorded by the driving simulation. State information is collected at constant frequency and includes absolute position, rotation, and velocity of the ego-car, its speed in Km/h, and control inputs from the driving agent (steering, throttle, and braking). Additionally, execution data contain OOB-related data, such as the lateral distance between the car and the lane center and the OOB percentage (i.e., how much the car is outside the lane).

The simulation data adhere to the following (simplified) JSON Schema and can be interpreted as Python objects using the simulation_data.py module.

{
  "$id": "schemas/simulationdata",
  "type": "object",
  "properties": {
    "timer" : { "type": "number" },
    "pos" : { 
         "type": "array",
         "items":{ "$ref" : "schemas/triple" }
        }
    "vel" : { 
         "type": "array",
         "items":{ "$ref" : "schemas/triple" }
        }
    "vel_kmh" : { "type": "number" },
    "steering" : { "type": "number" },
    "brake" : { "type": "number" },
    "throttle" : { "type": "number" },
    "is_oob" : { "type": "number" },
    "oob_percentage" : { "type": "number" } §\label{line:oob-percentage}§
  },
 "required": [
  "timer", "pos", "vel", "vel_kmh", 
  "steering", "brake", "throttle",
  "is_oob", "oob_percentage"
 ]
}

Dataset Content

The TRAVEL dataset is a lively initiative so the content of the dataset is subject to change. Currently, the dataset contains the data collected during the SBST CPS tool competition, and data collected in the context of our recent work on test selection (SDC-Scissor work and tool) and test prioritization (automated test cases prioritization work for SDCs).

SBST CPS Tool Competition Data

The data collected during the SBST CPS tool competition are stored inside data/competition.tar.gz. The file contains the test cases generated by Deeper, Frenetic, AdaFrenetic, and Swat, the open-source test generators submitted to the competition and executed against BeamNG.AI with an aggression factor of 0.7 (i.e., conservative driver).

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In financial year 2024, Maruti Suzuki sold approximately 1.6 million passenger cars across India. This made it the leading car manufacturer in terms of car sales in India, followed distantly by Hyundai and Tata. The automobile sector across India saw recovering from the impact of the coronavirus (COVID-19) pandemic in the same year. Is the Indian automobile market in trouble? There were more than 326 million vehicles registered in India in the financial year 2020. Vehicle registrations grew at a compound annual growth rate of over 10 percent between 2017 and 2020. However, due to economic slowdown, the introduction of new Bharat Stage Emission Standards VI (BS-VI) in 2019, and the impact of the coronavirus pandemic, the domestic sales of vehicles have been declining since the financial year 2020 for two consecutive years. This continued until the financial year 2023. New trend – electric vehicles With strong government support through policy initiatives such as the National Electric Mobility Mission Plan 2020 introduced in 2013, Faster Adoption and Manufacturing of (Hybrid &) Electric Vehicles (FAME) in 2015, and further FAME phase II in 2019, India was aiming to achieve 30 percent private electric vehicle ownership. As two-wheelers were the most popular types of automobiles for the last decade, in the financial year 2023, the majority of electric vehicles sold in India were also two-wheelers. The demand for electric vehicles is expected to rise significantly in the future.

An automobile dataset typically includes information about various types of vehicles, such as cars, trucks, and motorcycles. The dataset may include information about the make, model, year, and manufacturer of the vehicle. It may also include information about the vehicle's features, such as its engine size, transmission type, fuel type, and horsepower. In addition to basic information about the vehicle, an automobile dataset may also include data on safety features, such as airbags, seat belts, and anti-lock brakes. It may also include information on the vehicle's performance, such as its acceleration, braking, and handling. Finally, an automobile dataset may include information on the vehicle's price, both new and used. This could include the manufacturer's suggested retail price (MSRP), as well as prices from various dealers and resellers. Overall, an automobile dataset can be a valuable resource for researchers, analysts, and businesses in the automotive .

This Dataset contains state, vehicle maker, vehicle category and vehicle type wise total number of vehicles registered in India in each year

Notes: 1. The data is sourced from the Vahan Portal and reflects the last update done by us. Data is updated/refreshed once every month. Any mismatches are due to frequent updates on the Vahan dashboard. 2. For datasets with year-wise data, the data of the previous years is as of 31 December of that year whereas the data of the current year is as of date.