In this paper, we present LookOut, a novel autonomy system that perceives the environment,
predicts a diverse set of futures of how the scene might unroll and estimates the trajectory of …

In self-driving, predicting future in terms of location and motion of all the agents around the
vehicle is a crucial requirement for planning. Recently, a new joint formulation of perception …

Reliable forecasting of the future behavior of road agents is a critical component to safe
planning in autonomous vehicles. Here, we represent continuous trajectories as sequences …

Directly producing planning results from raw sensors has been a long-desired solution for
autonomous driving and has attracted increasing attention recently. Most existing end-to …

Forecasting the long-term future motion of road actors is a core challenge to the deployment
of safe autonomous vehicles (AVs). Viable solutions must account for both the static …

We present CoverNet, a new method for multimodal, probabilistic trajectory prediction for
urban driving. Previous work has employed a variety of methods, including multimodal …

Safe local motion planning for autonomous driving in dynamic environments requires
forecasting how the scene evolves. Practical autonomy stacks adopt a semantic object …

Autonomous driving promises transformative improvements to transportation, but building
systems capable of safely navigating the unstructured complexity of real-world scenarios …

Predicting future trajectories of surrounding agents is essential for safety-critical autonomous
driving. Most existing work focuses on predicting marginal trajectories for each agent …

Vehicle trajectory prediction is crucial for autonomous driving and advanced driver assistant
systems. While existing approaches may sample from a predicted distribution of vehicle …