… our approach on autonomous driving datasets for both marginal and joint motion prediction,
and … can unify a variety of motion prediction tasks from joint motion predictions to conditioned …

… deep learning techniques for autonomous driving,” Journal … time trajectory planning for urban
autonomous driving,” in 2015 … lqr for on-road autonomous driving motion planning,” in 2017 …

… the joint distribution for the motion of multiple agents in a … motion forecasting results. We
obtain state-of-the-art results for multi-agent motion prediction on the Waymo Open Motion …

… Perception and Motion Prediction: The majority of previous … trajectory prediction to reason
about the future state of a driving … occupancy grid prediction for urban autonomous driving: a …

… driving, which can help autonomous vehicles have a better … path prediction at a time horizon
of 2s for autonomous driving … to predict the path of pedestrian for autonomous driving with …

… dynamics is very much coveted [29] in a wide range of applications such as autonomous
driving, human tracking, and regulating the response of a robot when interacting with humans. …

… autonomous driving? Geometrybased sensor fusion has shown great promise for perception
tasks such as object detection and motion … perception and prediction in autonomous driving. …

… Human motion prediction, ie, predicting the future 3D poses of a person based on past …
vision and has many useful applications in autonomous driving [53], human robot interaction [37] …

… and autonomous driving, such as multimodal trajectory prediction. However, state-of-the-art
generative methods face limitations in representing the scene context, leading to predictions …

… Table 3 thus shows important effects of CNN’s perception in motion on path planning in
autonomous driving. Our model performs better than that of [6] (cf. Table 3), ie, 10.18 versus 45.3 …