5 citations · 5 across the 13 of their papers we have counts for
14 papers
Dense Coverage, Sparse Refinement: Byte-Constrained Cooperative Perception
Melih Yazgan, Timon Müller, J. Marius Zöllner
Collaborative perception improves autonomous perception by sharing intermediate Bird's-Eye-View (BEV) features across connected agents, but dense feature exchange is difficult to d…
Recall to Predict: Grounding Motion Forecasting in Interpretable Motion Bank
Abhishek Vivekanandan, Ahmed Abouelazm, J. Marius Zöllner
Motion forecasting often requires trading interpretability for predictive accuracy. Standard anchor-based architectures rely on opaque latent queries that are highly prone to laten…
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…
Goal-based Trajectory Prediction for improved Cross-Dataset Generalization
Daniel Grimm, Ahmed Abouelazm, J. Marius Zöllner
To achieve full autonomous driving, a good understanding of the surrounding environment is necessary. Especially predicting the future states of other traffic participants imposes…
2.5D Object Detection for Intelligent Roadside Infrastructure
Nikolai Polley, Yacin Boualili, Ferdinand Mütsch +3
On-board sensors of autonomous vehicles can be obstructed, occluded, or limited by restricted fields of view, complicating downstream driving decisions. Intelligent roadside infras…
DigiT4TAF -- Bridging Physical and Digital Worlds for Future Transportation Systems
Maximilian Zipfl, Pascal Zwick, Patrick Schulz +22
In the future, mobility will be strongly shaped by the increasing use of digitalization. Not only will individual road users be highly interconnected, but also the road and associa…
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…
Fool the Stoplight: Realistic Adversarial Patch Attacks on Traffic Light Detectors
Svetlana Pavlitska, Jamie Robb, Nikolai Polley +2
Realistic adversarial attacks on various camera-based perception tasks of autonomous vehicles have been successfully demonstrated so far. However, only a few works considered attac…
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…
Collaborative Perception Datasets in Autonomous Driving: A Survey
Melih Yazgan, Mythra Varun Akkanapragada, J. Marius Zoellner
This survey offers a comprehensive examination of collaborative perception datasets in the context of Vehicle-to-Infrastructure (V2I), Vehicle-to-Vehicle (V2V), and Vehicle-to-Ever…
Towards Sensor Data Abstraction of Autonomous Vehicle Perception Systems
Hannes Reichert, Lukas Lang, Kevin Rösch +6
Full-stack autonomous driving perception modules usually consist of data-driven models based on multiple sensor modalities. However, these models might be biased to the sensor setu…
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…