Autonomous Vehicles (AV) are the future of transportation and they will transform the dynamic of vehicle and pedestrian interaction. However, in the absence of a driver, it is not clear how an AV can ...

Autonomous Vehicles (AV) will transform transportation, but also the interaction between vehicles and pedestrians. In the absence of a driver, it is not clear how an AV can communicate its intention ...

Despite rapid advancements in automated driving systems (ADS), current human-computer interaction research tends to focus more on the safety driver in lower level vehicles. The future of automated ...

Rapid advances in every sphere of autonomous driving technology have intensified the need to be able to benchmark and compare different approaches. While many benchmarking tools tailored to different ...

Recently, deep neural networks have been capable of solving complex control tasks in certain challenging environments. However, these deep learning policies continue to be hard to interpret, explain ...

Continual learning is often confounded by \“catastrophic forgetting\” that prevents neural networks from learning tasks sequentially. In the case of real world classification systems that are ...

Visual data contains rich information about the operating environment of an intelligent robotic system. Extracting this information allows intelligent systems to reason and decide their future actions ...

Boolean SAT solvers are indispensable tools in a variety of domains in computer science and engineering where efficient search is required. Not only does this relieve the burden on the users of ...

Title Example-driven modeling: on effects of using examples on structural model comprehension, what makes them useful, and how to create them ...

The estimation of uncertainty in robotic vision, such as 3D object detection, is an essential component in developing safe autonomous systems aware of their own performance. However, the deployment of ...

Reinforcement learning (RL) is an attractive way to implement high-level decision-making policies for autonomous driving, but learning directly from a real vehicle or a high-fidelity simulator is ...

This thesis discusses two different problems in motion planning for autonomous driving. The first is the problem of optimizing a lattice planner control set for any particular autonomous driving task, ...