13 papers
Uncertainty-Aware and Temporally Regulated Expert Advice in Reinforcement Learning for Autonomous Driving
Ahmed Abouelazm, Felix Klingebiel, Philip Schörner +1
Exploration in reinforcement learning for autonomous driving is inherently unsafe: agents must experience novel behaviors to learn, yet exploration can lead to collisions or off-ro…
Beyond Scalar Rewards: Distributional Reinforcement Learning with Preordered Objectives for Safe and Reliable Autonomous Driving
Ahmed Abouelazm, Jonas Michel, Daniel Bogdoll +2
Autonomous driving involves multiple, often conflicting objectives such as safety, efficiency, and comfort. In reinforcement learning (RL), these objectives are typically combined…
Bridging Simulation and Usability: A User-Friendly Framework for Scenario Generation in CARLA
Ahmed Abouelazm, Mohammad Mahmoud, Conrad Walter +4
Autonomous driving promises safer roads, reduced congestion, and improved mobility, yet validating these systems across diverse conditions remains a major challenge. Real-world tes…
Diverse and Adaptive Behavior Curriculum for Autonomous Driving: A Student-Teacher Framework with Multi-Agent RL
Ahmed Abouelazm, Johannes Ratz, Philip Schörner +1
Autonomous driving faces challenges in navigating complex real-world traffic, requiring safe handling of both common and critical scenarios. Reinforcement learning (RL), a prominen…
Automatic Curriculum Learning for Driving Scenarios: Towards Robust and Efficient Reinforcement Learning
Ahmed Abouelazm, Tim Weinstein, Tim Joseph +2
This paper addresses the challenges of training end-to-end autonomous driving agents using Reinforcement Learning (RL). RL agents are typically trained in a fixed set of scenarios…
Balancing Progress and Safety: A Novel Risk-Aware Objective for RL in Autonomous Driving
Ahmed Abouelazm, Jonas Michel, Helen Gremmelmaier +3
Reinforcement Learning (RL) is a promising approach for achieving autonomous driving due to robust decision-making capabilities. RL learns a driving policy through trial and error…