16 citations · 16 across the 4 of their papers we have counts for
5 papers · 1 filter
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…
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…
A Survey of Deep Learning Applications to Autonomous Vehicle Control
Sampo Kuutti, Richard Bowden, Yaochu Jin +2
Designing a controller for autonomous vehicles capable of providing adequate performance in all driving scenarios is challenging due to the highly complex environment and inability…