In a preprint paper, Uber researchers describe MultiNet, a system that detects and predicts the motions of obstacles from autonomous vehicle lidar data. They say that unlike existing models, MultiNet reasons about the uncertainty of the behavior and movement of cars, pedestrians, and cyclists using a model that infers detections and predictions and then refines those to generate potential trajectories.
Anticipating the future states of obstacles is a challenging task, but it’s key to preventing accidents on the road. Within the context of a self-driving vehicle, a perception system has to capture a range of trajectories other actors might take rather than a single likely trajectory. For example, an opposing vehicle approaching an intersection might continue driving straight or turn in front of an autonomous vehicle; in order to ensure safety, the self-driving vehicle needs to reason about these possibilities and adjust its behavior accordingly.