This dataset contains simulated and real-world experimental data associated with the paper “Comprehensive Analysis of Optimization-Based Obstacle Avoidance for Agricultural Robotics in Greenhouse Environments.” The dataset from the simulated environment comprises multiple CSV files generated from the Gazebo simulation of a differential robot, the Stretch Robot. These files document the robot's movement, capturing data from the Gazebo model topic.
This dataset contains simulated and real-world experimental data associated with the paper “Comprehensive Analysis of Optimization-Based Obstacle Avoidance for Agricultural Robotics in Greenhouse Environments.” The dataset from the simulated environment comprises multiple CSV files generated from the Gazebo simulation of a differential robot, the Stretch Robot. These files document the robot's movement, capturing data from the Gazebo model topic.
The "Aggressive Driving Behavior IoT Data" dataset captures real-time driving behaviour through a network of Internet of Things (IoT) devices, specifically designed to monitor and analyze aggressive driving patterns. This dataset contains comprehensive recordings from various sensors embedded in vehicles, including GPS, accelerometer, gyroscope, and onboard diagnostics (OBD) systems. The data points collected provide detailed insights into vehicle speed, acceleration, braking, steering patterns, and environmental conditions over time.
Classifying the driving styles is of particular interest for enhancing road safety in smart cities. The vehicle can assist the driver by providing advice to increase awareness of potential dangers. Accordingly, dissuasive measures, such as adjusting insurance costs, can be implemented. The service is called Pay-As-You-Drive insurance (PAYD), and to address it, the paper introduces a method for constructing a database of simulated driver behaviors using the Simulation of Urban MObility Simulation of Urban MObility (SUMO) simulator.
