22 citations · 46 across the 17 of their papers we have counts for
18 papers
Closed-Loop Aware Planning via Imitation-Free Speed Scoring for End-to-End Autonomous Driving
Emad Aghajanzadeh, Thomas Schwarzfischer, Marc Kaufeld +8
End-to-end autonomous driving has predominantly relied on open-loop imitation learning for training, which presents two limitations. First, expert demonstrations show only one beha…
Risk-Aware Motion Planning with Learned Trajectory Primitives and Probabilistic Safety Assessment
Marc Kaufeld, Dian Zhuang, Johannes Betz
This paper presents a radial basis function network (RBFN)-informed motion planning framework for safe and efficient urban autonomous driving. The proposed approach combines RBFN-b…
G2DP: Diffusion Planning with Spatio-Temporal Grid Guidance
Hang Yu, Ye Jin, Alessandro Canevaro +7
In autonomous driving, diffusion-based planners have emerged as a promising paradigm for robust motion planning in dense and interactive traffic, as they can effectively model dive…
Disengagement Analysis and Field Tests of a Prototypical Open-Source Level 4 Autonomous Driving System
Marvin Seegert, Christian Oefinger, Korbinian Moller +2
Proprietary Autonomous Driving Systems are typically evaluated through disengagements, unplanned manual interventions to alter vehicle behavior, as annually reported by the Califor…
StyleVLA: Driving Style-Aware Vision Language Action Model for Autonomous Driving
Yuan Gao, Dengyuan Hua, Mattia Piccinini +4
Vision Language Models (VLMs) bridge visual perception and linguistic reasoning. In Autonomous Driving (AD), this synergy has enabled Vision Language Action (VLA) models, which tra…
Modular Autonomy with Conversational Interaction: An LLM-driven Framework for Decision Making in Autonomous Driving
Marvin Seegert, Korbinian Moller, Johannes Betz
Recent advancements in Large Language Models (LLMs) offer new opportunities to create natural language interfaces for Autonomous Driving Systems (ADSs), moving beyond rigid inputs.…
Towards Safe Autonomous Driving: A Real-Time Motion Planning Algorithm on Embedded Hardware
Korbinian Moller, Glenn Johannes Tungka, Lucas Jürgens +1
Ensuring the functional safety of Autonomous Vehicles (AVs) requires motion planning modules that not only operate within strict real-time constraints but also maintain controllabi…
Reinforcement Learning-based Dynamic Adaptation for Sampling-Based Motion Planning in Agile Autonomous Driving
Alexander Langmann, Yevhenii Tokarev, Mattia Piccinini +2
Sampling-based trajectory planners are widely used for agile autonomous driving due to their ability to generate fast, smooth, and kinodynamically feasible trajectories. However, t…
Precise and Efficient Collision Prediction under Uncertainty in Autonomous Driving
Marc Kaufeld, Johannes Betz
This research introduces two efficient methods to estimate the collision risk of planned trajectories in autonomous driving under uncertain driving conditions. Deterministic collis…
Learning to Sample: Reinforcement Learning-Guided Sampling for Autonomous Vehicle Motion Planning
Korbinian Moller, Roland Stroop, Mattia Piccinini +2
Sampling-based motion planning is a well-established approach in autonomous driving, valued for its modularity and analytical tractability. In complex urban scenarios, however, uni…
Towards Safe Autonomous Driving: A Real-Time Safeguarding Concept for Motion Planning Algorithms
Korbinian Moller, Rafael Neher, Marvin Seegert +1
Ensuring the functional safety of motion planning modules in autonomous vehicles remains a critical challenge, especially when dealing with complex or learning-based software. Onli…
MultiDrive: A Co-Simulation Framework Bridging 2D and 3D Driving Simulation for AV Software Validation
Marc Kaufeld, Korbinian Moller, Alessio Gambi +2
Scenario-based testing using simulations is a cornerstone of Autonomous Vehicles (AVs) software validation. So far, developers needed to choose between low-fidelity 2D simulators t…
Pedestrian-Aware Motion Planning for Autonomous Driving in Complex Urban Scenarios
Korbinian Moller, Truls Nyberg, Jana Tumova +1
Motion planning in uncertain environments like complex urban areas is a key challenge for autonomous vehicles (AVs). The aim of our research is to investigate how AVs can navigate…
From Shadows to Safety: Occlusion Tracking and Risk Mitigation for Urban Autonomous Driving
Korbinian Moller, Luis Schwarzmeier, Johannes Betz
Autonomous vehicles (AVs) must navigate dynamic urban environments where occlusions and perception limitations introduce significant uncertainties. This research builds upon and ex…
Investigating Driving Interactions: A Robust Multi-Agent Simulation Framework for Autonomous Vehicles
Marc Kaufeld, Rainer Trauth, Johannes Betz
Current validation methods often rely on recorded data and basic functional checks, which may not be sufficient to encompass the scenarios an autonomous vehicle might encounter. In…
Overcoming Blind Spots: Occlusion Considerations for Improved Autonomous Driving Safety
Korbinian Moller, Rainer Trauth, Johannes Betz
Our work introduces a module for assessing the trajectory safety of autonomous vehicles in dynamic environments marked by high uncertainty. We focus on occluded areas and occluded…
A Reinforcement Learning-Boosted Motion Planning Framework: Comprehensive Generalization Performance in Autonomous Driving
Rainer Trauth, Alexander Hobmeier, Johannes Betz
This study introduces a novel approach to autonomous motion planning, informing an analytical algorithm with a reinforcement learning (RL) agent within a Frenet coordinate system.…
FRENETIX: A High-Performance and Modular Motion Planning Framework for Autonomous Driving
Rainer Trauth, Korbinian Moller, Gerald Wuersching +1
Our research introduces a modular motion planning framework for autonomous vehicles using a sampling-based trajectory planning algorithm. This approach effectively tackles the chal…