KI-PMF: Knowledge Integrated Plausible Motion Forecasting
This addresses safety and reliability issues in autonomous driving by preventing off-road predictions, though it is incremental as it builds on existing trajectory forecasting methods.
The paper tackles the problem of motion forecasting for autonomous vehicles by incorporating explicit knowledge priors to ensure predictions adhere to physical laws and environmental constraints, resulting in accurate and safe trajectory forecasts.
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.