From the 1 of 13 linked papers with an AI index.
13 papers
Risk-Aware Motion Planning with Learned Trajectory Primitives and Probabilistic Safety Assessment
Marc Kaufeld, Dian Zhuang, Johannes Betz
The paper proposes a motion planning framework for urban autonomous driving that uses a radial basis function network to generate jerk‑minimal trajectory primitives and evaluates t…
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…
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…
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.…