The autonomous vehicle, as an emerging and rapidly growing field, has received extensive
attention for its futuristic driving experiences. Although the fast developing depth sensors …

Dense urban traffic environments can produce situations where accurate prediction and
dynamic models are insufficient for successful autonomous vehicle motion planning. We …

Autonomous vehicles, popularly known as self-driving cars, have the potential to transform
travel behavior. However, existing analyses have ignored strategic interactions with other …

Building effective, enjoyable, and safe autonomous vehicles is a lot harder than has
historically been considered. The reason is that, simply put, an autonomous vehicle must …

To safely and efficiently navigate in complex urban traffic, autonomous vehicles must make
responsible predictions in relation to surrounding traffic-agents (vehicles, bicycles …

We address one of the crucial aspects necessary for safe and efficient operations of
autonomous vehicles, namely predicting future state of traffic actors in the autonomous …

To operate reliably in real-world traffic, an autonomous car must evaluate the consequences
of its potential actions by anticipating the uncertain intentions of other traffic participants. This …

Autonomous vehicles are expected to display human-like behavior, at least to the extent that
their decisions can be intuitively understood by other road users. If this is not the case, the …

Predicting the future motion of traffic agents is crucial for safe and efficient autonomous
driving. To this end, we present PredictionNet, a deep neural network (DNN) that predicts …

Performing safe and efficient lane changes is a crucial feature for creating fully autonomous
vehicles. Recent advances have demonstrated successful lane following behavior using …