16 citations · 16 across the 3 of their papers we have counts for
7 papers
ARC: Adversarially Robust Control Policies for Autonomous Vehicles
Sampo Kuutti, Saber Fallah, Richard Bowden
Deep neural networks have demonstrated their capability to learn control policies for a variety of tasks. However, these neural network-based policies have been shown to be suscept…
Adversarial Mixture Density Networks: Learning to Drive Safely from Collision Data
Sampo Kuutti, Saber Fallah, Richard Bowden
Imitation learning has been widely used to learn control policies for autonomous driving based on pre-recorded data. However, imitation learning based policies have been shown to b…
Deep Learning Traversability Estimator for Mobile Robots in Unstructured Environments
Marco Visca, Sampo Kuutti, Roger Powell +2
Terrain traversability analysis plays a major role in ensuring safe robotic navigation in unstructured environments. However, real-time constraints frequently limit the accuracy of…
Self-adaptive Torque Vectoring Controller Using Reinforcement Learning
Shayan Taherian, Sampo Kuutti, Marco Visca +1
Continuous direct yaw moment control systems such as torque-vectoring controller are an essential part for vehicle stabilization. This controller has been extensively researched wi…
Weakly Supervised Reinforcement Learning for Autonomous Highway Driving via Virtual Safety Cages
Sampo Kuutti, Richard Bowden, Saber Fallah
The use of neural networks and reinforcement learning has become increasingly popular in autonomous vehicle control. However, the opaqueness of the resulting control policies prese…
Training Adversarial Agents to Exploit Weaknesses in Deep Control Policies
Sampo Kuutti, Saber Fallah, Richard Bowden
Deep learning has become an increasingly common technique for various control problems, such as robotic arm manipulation, robot navigation, and autonomous vehicles. However, the do…