Description
Recent advances in autonomous vehicles and their deployment in the real world compel us to analyze the decision-making algorithms in safety-critical scenarios. We consider dilemmas in which collisions cannot be avoided, and collision avoidance involves ethical issues and bias.
We use reinforcement learning algorithms to control the vehicle and apply computer vision techniques to improve perception, which serves as input to our RL algorithm.
