See the Future: A Semantic Segmentation Network Predicting Ego-Vehicle Trajectory with a Single Monocular Camera

Ego-vehicle trajectory prediction is important for autonomous vehicles to detect collisions and accordingly avoid accidents. Recent approaches employ prior-known or on-line acquired road topology or geometries as motion constraints for their predictive models. However, the prior-known information (e.g., pre-built maps) might become unreliable due to, for example, temporal changes caused by road constructions. Whereas on-line perception may require high-cost sensors, such as large filed-of-view laser scanners, to get an overview structure of the local environment, making the prediction difficult to afford, especially for driving assistance systems. So in this letter, we provide a solution without using road topology or geometries for ego-vehicle trajectory prediction. We formulate this problem as a two-class semantic segmentation problem and develop a novel sequence-based deep neural network to predict the trajectory. The only sensor we need during runtime is a single front-view monocular camera. The inputs to our network are several consecutive images, and the output is the predicted trajectory mask that can be directly overlaid on the current front-view image. We create our datasets with different prediction horizons from KITTI. The experimental results confirm the effectiveness of our approach and the superiority over the baselines.