10.17862/cranfield.rd.6270233.v2
Alaa Alzoubi
Alaa
Alzoubi
David Nam
David
Nam
Vehicle Obstacle Interaction Dataset (VOIDataset)
Cranfield Online Research Data (CORD)
2018
Vehicle Activity Recognition
Qualitative Trajectory Calculus
Transfer Learning
Deep Convolutional Neural Networks
Computer Vision
Artificial Intelligence and Image Processing
2018-10-11 12:26:42
Dataset
https://cord.cranfield.ac.uk/articles/dataset/Vehicle_Obstacle_Interaction_Dataset_VOIDataset_/6270233
<div>Vehicle-Obstacle Interaction Dataset (VOIDataset) includes 277 trajectories (sequences of x,y positions of the vehicle and the obstacle) of three different scenarios (67 crash, 106 left-pass, and 104 right-pass trajectories). The distance between the vehicle and the obstacle (length of the trajectory) is 50 meters. The trajectories were manually annotated, and used to evaluate our activity recognition method. </div><div><br></div><div>Data was gathered using a simulation environment developed in Virtual Battlespace 3 (VBS3), with the Logitech G29 Driving Force Racing Wheel and pedals. Here a model of a Dubai highway was used. We consider a six lane road with an obstacle in the centre lane. The experiment consisted of 40 participants, all of varying ages, genders and driving experiences. Participants were asked to use their driving experience to avoid the obstacle. A Skoda Octavia was used in all trails, and with maximum speed 50KPH. We recorded the obstacle and ego-vehicle's coordinates (the centre position of the vehicle), velocity, heading angle, and distance from each other. The generated trajectories were recorded at 10Hz.</div><div><br></div><div><b>Version 2: </b>no change to the dataset, but appending contact details for more information:</div><div>* Alaa Alzoubi: alaa.alzoubi@buckingham.ac.uk</div><div>* David Nam: d.nam@cranfield.ac.uk<br></div>