Safe Reinforcement Learning with Scene Decomposition for Navigating Complex Urban Environments. Navigating urban environments represents a complex task for automated vehicles. They must reach their goal safely and efficiently while considering a multitude of traffic participants. We propose a modular decision making algorithm to autonomously navigate intersections, addressing challenges of existing rulebased and reinforcement learning (RL) approaches. We first present a safe RL algorithm relying on a modelchecker to ensure safety guarantees. To make the decision strategy robust to perception errors and occlusions, we introduce a belief update technique using a learning based approach. Finally, we use a scene decomposition approach to scale our algorithm to environments with multiple traffic participants. We empirically demonstrate that our algorithm outperforms rulebased methods and reinforcement learning techniques on a complex intersection scenario.

Continuous Deep Maximum Entropy Inverse Reinforcement Learning using online POMDP A vehicle navigating in an urban environment must obey traffic rules by properly setting its speed, such as staying below the road speed limit and avoiding collision with other vehicles. This is presumably the scenario that autonomous vehicles will face: they will share the traffic roads with other vehicles (autonomous or not), cooperatively interacting with them. In other words, autonomous vehicles should not only follow traffic rules, but should also behave in such a way that resembles other vehicles behavior. However, manually specification of such behavior is a timeconsuming and errorprone task, since driving in urban roads is a complex task, which involves many factors. This paper presents a multitask decision making framework that learns an expert driveru0027s behavior driving in an urban scenario containing traffic lights and other vehicles. For this purpose, Inverse Reinforcement Learning (IRL) is used to learn a reward function that explains the expert driveru0027s behavior. Most IRL approaches require solving a Markov Decision Process (MDP) in each iteration of the algorithm to compute the optimal policy given the current rewards. Nevertheless, the computational cost of solving an MDP is high when considering large state spaces. To overcome this issue, the optimal policy is estimated by sampling trajectories in regions of the space with higher rewards. To do so, the problem is modeled as a continuous Partially Observed Markov Decision Process (POMDP), in which the intentions of other vehicles are only partially observed. An online solver is employed in order to sample trajectories given the current rewards. The efficiency of the proposed framework is demonstrated through simulations, showing that the controlled vehicle is be able to mimic an expert driveru0027s behavior.