Abstract:
Autonomous vehicles are set to be a part of everyday traffic. Their presence in traffic dominated by humans possesses some challenges. Any experience with driving in traffic shows us that each driver is unique in their driving style. So far, this richness in differences in human behavior has not been projected into the models used in traffic simulations. These models are an essential part of the development of autonomous vehicles; from the inference of other vehicle intentions to virtual testing. Therefore creating a more realistic traffic environment is a very important task. In this work, a deep dive into the state of the problem is given. Then, a framework that accounts for different driving styles, as well as different vehicle types, is introduced. Firstly, an in depth analysis of distinct patterns of driving is carried out in the dataset. Then these distinct patterns are modeled with simulated agents using reinforcement learning. In inference time, a traffic scene is observed, each vehicle is assigned to the pre-trained driver model and a simulation is carried out. As a result, a traffic scene is reconstructed with data-validated models. This new approach that incorporates previous driver mod eling work with a behavioral component, paves the way for a more realistic model of the traffic. This realistic traffic model can be used in AV testing and validation.
