6 citations · 11 across the 15 of their papers we have counts for
6 papers · 2 filters
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…
TPK: Trustworthy Trajectory Prediction Integrating Prior Knowledge For Interpretability and Kinematic Feasibility
Marius Baden, Ahmed Abouelazm, Christian Hubschneider +3
Trajectory prediction is crucial for autonomous driving, enabling vehicles to navigate safely by anticipating the movements of surrounding road users. However, current deep learnin…
Boundary-Guided Trajectory Prediction for Road Aware and Physically Feasible Autonomous Driving
Ahmed Abouelazm, Mianzhi Liu, Christian Hubschneider +3
Accurate prediction of surrounding road users' trajectories is essential for safe and efficient autonomous driving. While deep learning models have improved performance, challenges…
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…