… [47] propose a novel paradigm, named direct perception method, to predict an affordance
for urban autonomous driving scenarios. The affordance represents a BEV format that clearly …

… motion prediction model that enables large-scale real-time simulation and planning for
autonomous driving… GAMMA treats the prediction task as constrained optimization in traffic agents…

… Thus reliable motion prediction should involve the modeling of the agent’s previous … for
autonomous driving systems. Early work on motion prediction considers the timeseries prediction …

… in autonomous driving (AD), motion prediction aims to predict … -step manner where each
predicted time-step is conditioned … previously predicted time-steps, ie, autoregressive prediction. …

… motion and accident prediction, which can be used to directly evaluate the accident prediction
ability for different autonomous driving … used to assess motion prediction performance. For …

… effIcient Motion Prediction baseLine (SIMPL) for autonomous … real-time, accurate motion
predictions for all relevant traffic … the network to forecast future motion for all road users in a …

… We compare the considered methods using error metrics relevant for motion prediction:
displacement (1), as well as along- and cross-track errors [45] measuring longitudinal and lateral …

… predictions due to their deterministic nature. In this paper, a probabilistic lateral motion prediction
… assessment algorithms are essential for safe and reliable autonomous driving systems. …

… Also, we observe that existing methods of generating future states for motion prediction can
be heavily memory-consuming and prevent multi-task learning. Therefore, we propose the …

… perception, prediction and planning in the field of autonomous driving. … (a) we embrace a
new outlook of autonomous driving … of prediction tasks in our framework, ie, motion forecasting …