… the autonomous driving system at hand. In order to quantify and learn this property, we
therefore first need to pick a particular autonomous driving … Predicting scene parsing and motion …

… Autonomous Vehicle (AV) stacks, the prediction and planning layers are separated, limiting
the planner to react to predictions that … policy network learns to drive while interacting with the …

… advanced motion planning … autonomous vehicles, we propose a crossmodal embedding
framework that demonstrates the efficacy of the use of multiple sensor data for motion prediction…

… In this work, we aim to predict the future motion of vehicles in a traffic scene by explicitly …
Using a large-scale real-world driving dataset, we demonstrate that jointly predicting the …

… predict steering angles of driving in different types of roads. From Fig. 6, we can observe the
prediction … and make rational predictions of the steering angle for autonomous driving. The …

… Uncertainty-aware short-term motion prediction of traffic actors for autonomous driving. In …
Deep multi-modal object detection and semantic segmentation for autonomous driving: …

… prediction methods for autonomous driving typically rely on one-stage trajectory prediction
… What-if motion prediction for autonomous driving. arXiv preprint arXiv:2008.10587, 2020. 6 …

… While evaluating the importance of our proposed pretext tasks, we wish to underline that
motion prediction for autonomous driving is a safety-critical task, especially at intersections …

… Although interactions between the autonomous car and the predicted motion of other traffic
participants are not considered, it provides good results assuming constant velocity of other …

… of accurate behavior modeling in autonomous driving and analyze the key approaches and
… , its application to model behavior in autonomous driving is largely unexplored. As such, we …