Abstract Context Machine Learning (ML) has been at the heart of many innovations over the
past years. However, including it in so-called “safety-critical” systems such as automotive or …

Compared with model-based control and optimization methods, reinforcement learning (RL)
provides a data-driven, learning-based framework to formulate and solve sequential …

Academic research in the field of autonomous vehicles has reached high popularity in
recent years related to several topics as sensor technologies, V2X communications, safety …

Merging is, in general, a challenging task for both human drivers and autonomous vehicles,
especially in dense traffic, because the merging vehicle typically needs to interact with other …

Reinforcement learning has gradually demonstrated its decision-making ability in
autonomous driving. Reinforcement learning is learning how to map states to actions by …

This paper develops an integrated safety-enhanced reinforcement learning (RL) and model
predictive control (MPC) framework for autonomous vehicles (AVs) to navigate unsignalized …

Abstract Deep Reinforcement Learning (RL) has considerably advanced over the past
decade. At the same time, state-of-the-art RL algorithms require a large computational …

Autonomous vehicles in highway driving scenarios are expected to become a reality in the
next few years. Decision-making and motion planning algorithms, which allow autonomous …

Autonomous driving is a promising technology to reduce traffic accidents and improve
driving efficiency. Although significant progress has been achieved, existing decision …

This paper presents a safe reinforcement learning system for automated driving that benefits
from multimodal future trajectory predictions. We propose a safety system that consists of two …