4 citations · 6 across the 8 of their papers we have counts for
9 papers
Beyond Waypoint Regression: Query-Based Cost Learning over Reachable Ego Futures for End-to-End Driving
Ahmed Abouelazm, Rupert Polley, Qingyuan Zhang +4
End-to-end planners based on waypoint regression achieve strong open-loop accuracy, but they primarily learn to mimic expert geometry and remain difficult to adapt to deployment-ti…
Boosting LiDAR-Based Localization with Semantic Insight: Camera Projection versus Direct LiDAR Segmentation
Sven Ochs, Philip Schörner, Marc René Zofka +1
Semantic segmentation of LiDAR data presents considerable challenges, particularly when dealing with diverse sensor types and configurations. However, incorporating semantic inform…
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…
CoCar NextGen: a Multi-Purpose Platform for Connected Autonomous Driving Research
Marc Heinrich, Maximilian Zipfl, Marc Uecker +9
Real world testing is of vital importance to the success of automated driving. While many players in the business design purpose build testing vehicles, we designed and build a mod…
Leveraging Swarm Intelligence to Drive Autonomously: A Particle Swarm Optimization based Approach to Motion Planning
Sven Ochs, Jens Doll, Marc Heinrich +4
Motion planning is an essential part of autonomous mobile platforms. A good pipeline should be modular enough to handle different vehicles, environments, and perception modules. Th…
KI-PMF: Knowledge Integrated Plausible Motion Forecasting
Abhishek Vivekanandan, Ahmed Abouelazm, Philip Schörner +1
Accurately forecasting the motion of traffic actors is crucial for the deployment of autonomous vehicles at a large scale. Current trajectory forecasting approaches primarily conce…
Holistic Graph-based Motion Prediction
Daniel Grimm, Philip Schörner, Moritz Dreßler +1
Motion prediction for automated vehicles in complex environments is a difficult task that is to be mastered when automated vehicles are to be used in arbitrary situations. Many fac…
Generalizing Decision Making for Automated Driving with an Invariant Environment Representation using Deep Reinforcement Learning
Karl Kurzer, Philip Schörner, Alexander Albers +3
Data driven approaches for decision making applied to automated driving require appropriate generalization strategies, to ensure applicability to the world's variability. Current a…