KI-PMF: Knowledge Integrated Plausible Motion Forecasting
Abhishek Vivekanandan Ahmed Abouelazm Philip Schörner J. Marius Zöllner
Accurately forecasting the motion of traffic actors is crucial for the
deployment of autonomous vehicles at a large scale. Current trajectory
forecasting approaches primarily concentrate on optimizing a loss function with
a specific metric, which can result in predictions that do not adhere to
physical laws or violate external constraints. Our objective is to incorporate
explicit knowledge priors that allow a network to forecast future trajectories
in compliance with both the kinematic constraints of a vehicle and the geometry
of the driving environment. To achieve this, we introduce a non-parametric
pruning layer and attention layers to integrate the defined knowledge priors.
Our proposed method is designed to ensure reachability guarantees for traffic
actors in both complex and dynamic situations. By conditioning the network to
follow physical laws, we can obtain accurate and safe predictions, essential
for maintaining autonomous vehicles' safety and efficiency in real-world
settings.In summary, this paper presents concepts that prevent off-road
predictions for safe and reliable motion forecasting by incorporating knowledge
priors into the training process.