Visual-Acoustic Vehicle Dataset

On the Development of an Acoustic-Driven Method to Improve Driver's Comfort Based on Deep Reinforcement Learning

Access our publication: Link

The Visual-Acoustic Vehicle Dataset is a set of multimodal data collected from a Lincoln MKS car including vehicle sensors information, 360º image data collected from eight cameras on top of the car, and LiDAR information. All the data has timestamps for syncronization purposes and is divided in four experiments.

Info Description Sampling Rate
nmea_sentence GPS sentences with the data type GGA 182Hz
vehicle/acc_ped_eng Includes throttle_rate, throttle_pc, and engine_rpm (revolutions per minute) 101Hz
vehicle/brake_ped Shows if brake pedal is being pressed 50Hz
vehicle/brake_torq Includes brake_torque_request, brake_torque_actual, and vehicle_speed 50Hz
vehicle/gear Which gear the vehicle is currently using 50Hz
vehicle/imu/data_raw Angular_velocity, and linear_acceleration along with the covariance 50Hz
vehicle/joint_states Position and velocity of each wheel and the steer on the front left and front right 116Hz
vehicle/steering_ang Steering wheel angle 50Hz
vehicle/steering_torq Steering wheel torque 50Hz
vehicle/suspension Front and rear suspension 50Hz
vehicle/tire_press Tire pressure for each wheel 2Hz
vehicle/turn_sig Whether turn signal was being used or not 1Hz
vehicle/twist Linear and angular twist of entire vehicle 50Hz
vehicle/wheel_speeds Records the speed of each of the four wheels in miles per hour 100Hz

Vehicle data

Tables containing car info topics as steering wheel angle, brake pedal status, tire pressure for each wheel, and IMU. (See all topics and sampling rates here)



Camera and Audio data

Images from the eight Logitech C922 video cameras pointing outwards towards the street in a circular formation to get a 360º view from the vehicle. Audio data from the Olympus ME-51S microphones placed outside and inside the vehicle.

Audio only: 1.5GB (Images coming soon)


LiDAR data

Depth information of the car surroundings collected from a Velodyne 64E-S2.

Coming Soon