Effects of Partial Observability Solver Methods on Training and Final Policies in Autonomous Driver RL: How do different methods for dealing with partial observability in the environment influence training and the robustness of final policies under various testing conditions?

Autonomous driving is a complex problem that can potentially be solved using artificial intelligence. The complexity stems from the system's need to understand the surroundings and make appropriate decisions. However, there are various challenges in constructing such a sophisticated system. One of the main challenges is to make the agent learn...

Evaluating Robustness of Deep Reinforcement Learning for Autonomous Driving: How does entropy maximization affect the training and robustness of final policies under various testing conditions?

This research paper aims to investigate the effect of entropy while training the agent on the robustness of the agent. This is important because robustness is defined as the agent's adaptability to different environments. A self-driving car should adapt to every environment that it is being used in since a mistake could cost someone's life....

How Human-Machine Interaction keeps pace with Automated Vehicles: A systematic review

Human Machine Interface (HMI) is a design concept that improves the interaction between the driver and the automated vehicle, which leads to greater safety and comfort for the driver and greater safety for the road user. Therefore, many papers and patents are published every year. Many papers use different methodologies and materials due to some...

Plastic intensive, purpose-built car: Autonomous urban transport in 2029

It is expected that the automotive industry will be facing a radical shift, going from human driven cars to cars that are driving autonomously. In this project I started out with performing an in depth literature research to get a good understanding on what is expected to change. My expectations that the design and solutions for a self-driving...