6 papers
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…
Extracting Uncertainty Estimates from Mixtures of Experts for Semantic Segmentation
Svetlana Pavlitska, Beyza Keskin, Alwin FaÃbender +2
Estimating accurate and well-calibrated predictive uncertainty is important for enhancing the reliability of computer vision models, especially in safety-critical applications like…
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.…
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…
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…