Recently significant progress has been made in vehicle prediction and planning algorithms
for autonomous driving. However, it remains quite challenging for an autonomous vehicle to …

To navigate safely in urban environments, an autonomous vehicle (ego vehicle) must
understand and anticipate its surroundings, in particular the behavior and intents of other …

Deep neural network controllers for autonomous driving have recently benefited from
significant performance improvements, and have begun deployment in the real world. Prior …

Abstract We introduce a Deep Stochastic IOC RNN Encoder-decoder framework, DESIRE,
for the task of future predictions of multiple interacting agents in dynamic scenes. DESIRE …

In most classical Autonomous Vehicle (AV) stacks, the prediction and planning layers are
separated, limiting the planner to react to predictions that are not informed by the planned …

This paper presents a probabilistic framework to obtain both reliable and fast uncertainty
estimates for predictions with Deep Neural Networks (DNNs). Our main contribution is a …

Artificial neural networks are being proposed for automated decision making under
uncertainty in many visionary contexts, including high-stake tasks such as navigating …

Next generation Unmanned Aerial Vehicles (UAVs) must reliably avoid moving obstacles.
Existing dynamic collision avoidance methods are effective where obstacle trajectories are …

As vehicle automation advances, motion planning algorithms face escalating challenges in
achieving safe and efficient navigation. Existing Advanced Driver Assistance Systems …

Motion prediction of surrounding vehicles is one of the most important tasks handled by a
self-driving vehicle, and represents a critical step in the autonomous system necessary to …