29 citations · 121 across the 63 of their papers we have counts for
67 papers
FutureLift: Lifting Trajectory Proposals into Bidirectional Future Graphs for Motion Forecasting
Daniel Grimm, J. Marius Zöllner
Accurate motion prediction is critical for safe autonomous driving, yet even strong forecasters leave residual errors in their multimodal proposals. Existing refiners compress comp…
Temporal Residual Bottleneck for Robust Asynchronous Collaborative Perception
Melih Yazgan, Ahmed Abouelazm, J. Marius Zöllner
Collaborative perception extends the sensing range of autonomous vehicles, but its performance degrades when shared features arrive stale or incomplete. Most latency-robust methods…
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…
Safety-Critical Scenarios Emerge from Initial Scenes
Yin Wu, Jiarong Wei, Carl Esselborn +4
Safety-critical driving scenario generation has largely focused on manipulating the behavior of surrounding agents while starting from an initial scene from driving data. This assu…
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…
CIG: Exploration via Conditional Information Gain
Tim Joseph, Marcus Fechner, Philipp Stegmaier +2
Intrinsic rewards for exploration in reinforcement learning condition on different contexts: lifelong rewards score each transition against accumulated experience but ignore within…
Real-World On-Vehicle Evaluation of Embedding-Based Anomaly Detection
Albert Schotschneider, Daniel Bogdoll, Svetlana Pavlitska +2
Detecting anomalies in traffic scenes is crucial for ensuring safety in autonomous driving, yet collecting representative anomalous data remains challenging. Existing anomaly detec…
Design and Behavior of Sparse Mixture-of-Experts Layers in CNN-based Semantic Segmentation
Svetlana Pavlitska, Haixi Fan, Konstantin Ditschuneit +1
Sparse mixture-of-experts (MoE) layers have been shown to substantially increase model capacity without a proportional increase in computational cost and are widely used in transfo…
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…
Segment to Focus: Guiding Latent Action Models in the Presence of Distractors
Marcus Fechner, Hamza Adnan, Constantin C. Lüth +3
Latent action models (LAMs) offer a promising path to pre-training embodied agents on large amounts of action-free video. They infer latent actions between consecutive observations…
Datasets for Valence and Arousal Inference: A Survey
Helen Schneider, Svetlana Pavlitska, Helen Gremmelmaier +1
Understanding human affect can be used in robotics, marketing, education, human-computer interaction, healthcare, entertainment, autonomous driving, and psychology to enhance decis…
Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers
Svetlana Pavlitska, Haixi Fan, Konstantin Ditschuneit +1
Robustifying convolutional neural networks (CNNs) against adversarial attacks remains challenging and often requires resource-intensive countermeasures. We explore the use of spars…
Extracting Uncertainty Estimates from Mixtures of Experts for Semantic Segmentation
Svetlana Pavlitska, Beyza Keskin, Alwin Faßbender +2
Estimating accurate and well-calibrated predictive uncertainty is important for enhancing the reliability of computer vision models, especially in safety-critical applications like…
EffiComm: Bandwidth Efficient Multi Agent Communication
Melih Yazgan, Allen Xavier Arasan, J. Marius Zöllner
Collaborative perception allows connected vehicles to exchange sensor information and overcome each vehicle's blind spots. Yet transmitting raw point clouds or full feature maps ov…
Why Braking? Scenario Extraction and Reasoning Utilizing LLM
Yin Wu, Daniel Slieter, Vivek Subramanian +3
The growing number of ADAS-equipped vehicles has led to a dramatic increase in driving data, yet most of them capture routine driving behavior. Identifying and understanding safety…
LanePerf: a Performance Estimation Framework for Lane Detection
Yin Wu, Daniel Slieter, Ahmed Abouelazm +2
Lane detection is a critical component of Advanced Driver-Assistance Systems (ADAS) and Automated Driving System (ADS), providing essential spatial information for lateral control.…
Online Performance Assessment of Multi-Source-Localization for Autonomous Driving Systems Using Subjective Logic
Stefan Orf, Sven Ochs, Marc René Zofka +1
Autonomous driving (AD) relies heavily on high precision localization as a crucial part of all driving related software components. The precise positioning is necessary for the uti…
Functionality Assessment Framework for Autonomous Driving Systems using Subjective Networks
Stefan Orf, Sven Ochs, Valentin Marotta +3
In complex autonomous driving (AD) software systems, the functioning of each system part is crucial for safe operation. By measuring the current functionality or operability of ind…
Contrast & Compress: Learning Lightweight Embeddings for Short Trajectories
Abhishek Vivekanandan, Christian Hubschneider, J. Marius Zöllner
The ability to retrieve semantically and directionally similar short-range trajectories with both accuracy and efficiency is foundational for downstream applications such as motion…
Centralized Decision-Making for Platooning By Using SPaT-Driven Reference Speeds
Melih Yazgan, Süleyman Tatar, J. Marius Zöllner
This paper introduces a centralized approach for fuel-efficient urban platooning by leveraging real-time Vehicle- to-Everything (V2X) communication and Signal Phase and Timing (SPa…
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…
The ATLAS of Traffic Lights: A Reliable Perception Framework for Autonomous Driving
Rupert Polley, Nikolai Polley, Dominik Heid +3
Traffic light perception is an essential component of the camera-based perception system for autonomous vehicles, enabling accurate detection and interpretation of traffic lights t…
A Chefs KISS -- Utilizing semantic information in both ICP and SLAM framework
Sven Ochs, Marc Heinrich, Philip Schörner +2
For utilizing autonomous vehicle in urban areas a reliable localization is needed. Especially when HD maps are used, a precise and repeatable method has to be chosen. Therefore acc…