Vehicle Obstacle Interaction Dataset (VOIDataset) - Alzoubi, Alaa; Nam, David (Cranfield University, 2018)
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. 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. Version 2: no change to the dataset, but appending contact details for more information: Alaa Alzoubi: alaa.alzoubi@buckingham.ac.uk David Nam: d.nam@cranfield.ac.uk
Keywords: 'Vehicle Activity Recognition'; 'Qualitative Trajectory Calculus'; 'Transfer Learning'; 'Deep Convolutional Neural Networks'; 'Computer Vision'; 'Artificial Intelligence and Image Processing'