Visual Motion of Vehicle Interactions for Autonomous Driving

Vehicles move on roads by performing regular actions on the ground, taking into account the road environment and traffic rules. Vehicle cameras capture such motion of surrounding vehicles on the ego-vehicle in a video. According to the visual appearance of vehicles and their motion trajectories, human drivers can identify the interactions of surrounding vehicles during stable vehicle action\/driving without difficulty. This dataset collects visual data of driving scenes in videos, and we label the qualitative vehicle actions around the camera based on the detailed motion trajectories of surrounding vehicles. The data are used in the deep learning of temporal events and vehicle behaviors in the sensing of autonomous vehicles. Vehicle interactions are limited to a range of 50m. Depending on different lanes and relative speed around the ego-vehicle, 13 classes of qualitative motion (interaction) are specified. They are identified in the video for preparing ego-vehicle action and avoiding collision. This is particularly important for fast-moving vehicles to plan vehicle motion and path ahead, and respond promptly in accident avoidance. To obtain the motion of vehicles moving on a horizontal plane, the video is sampled at the height of the horizon to produce the Motion Profile at 30fps temporally, which contains the trajectories of vehicles and background scenes. Along with the Motion Profile, the width of the vehicle in the images, which reflects the depth of the vehicle, is also obtained and plotted with the Motion Profile, as input to a spatial-temporal deep network to understand vehicle interactions. The motion trajectories are annotated with the interaction classes in specified colors, separated at the moment when a new interaction starts.