# POLIDriving: A Public-Access Driving Dataset for Road Traffic Safety Analysis
## 2 Related Work  
We found driving datasets for autonomous driving and driving behavior upon review. Generally, they provide high-resolution images, distance and proximity measures, vehicle parameters, and geolocation. Considering that the target of POLIDriving is the identification of risky driving patterns, we have considered the datasets that provide mainly vehicle data and geolocation. Thus, according to these requirements, the following are the most relevant driving datasets.  
COMMA.AI [7] is a 7-h driving dataset that contains images of the road and measures of sensors. This dataset includes the driving records of three drivers using one vehicle. It was generated using a frontal camera, a Light Detection and Ranging (LiDAR) sensor, and a Positioning and Orientation System (POS). Pictures were taken at 20 Hz, and the measures were integrated at 100 Hz. COMMA.AI includes information on the vehicle through the measurements of the sensors and the road conditions through the pictures of the road.  
COMMA2K19 [8] is a 33-h driving dataset that contains road and in-vehicle images and measurements of sensors. This dataset includes the driver record of one driver using two vehicles. It was generated using a set of cameras (two frontal and one internal), an On-Board Diagnostics (OBD)-II scanner, a Global Navigation Satellite System (GNSS) receiver, and a 9-axis Inertial Measurement Unit (IMU). Pictures were taken at 20 Hz and the measurements at 100 Hz. COMMA2K19 includes information on vehicle and road conditions.  
PREVENTION [9] is a 6-h driving dataset that contains front and rear images of the road and measurements of sensors. This dataset includes the driving records of three drivers using one vehicle. It was generated using two high-resolution cameras (one frontal and one rear-facing), a LiDAR sensor, three long-range radars (one narrow-field and two broad-field), a GNSS receiver, and a Controller Area Network (CAN) bus scanner. PREVENTION includes information on vehicle and road conditions.  
AUTOMOTIVE OBD-II [10] is a 6-h driving dataset containing sensor measures. This dataset includes the driving record of ten vehicles. It was generated using only an OBD-II scanner and a mobile app. Sensor measurements were taken at 10 Hz. AUTOMOTIVE OBD-II includes only information on the vehicle such as engine coolant temperature, rpm, speed, throttle position (It monitors the position of the throttle valve that later controls the air entering the engine.), intake air temperature, and others.  
A2D2 [3] is a driving dataset that contains images from six cameras and measurements of sensors. It contains 392.556 observations (frames) and each frame contains images and data files. A2D2 contains the driving record of one driver using one vehicle. It was generated using six cameras, six LiDAR sensors, one bus scanner, and one Global Positioning System (GPS) receiver. A2D2 includes information on vehicle and road conditions.  
In contrast, we present POLIDriving, an 18-h driving dataset that includes information related to the driver, vehicle, weather conditions, traffic accidents, and road geometrics characteristics. It includes information from drivers of different ages and genders, with or without medical conditions or traffic violations, and information from vehicles of different brands, models, types, and years of fabrication. POLIDriving differs from other datasets in the number of drivers, vehicles, and data sources used for the data generation. Tables 1 and 2 present the features of the reviewed driving datasets.