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cs.AI2026
PlannerForge: LLM Agents for Scenario-Based Testing of Motion Planners in Autonomous Driving
Yuan Gao, Sebastian Müller, Mattia Piccinini +5
Ensuring the safety of autonomous driving is a critical challenge. Scenario-based testing is a systematic process used to validate Autonomous Driving Systems (ADSs), but it remains…
cs.RO2026
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…
cs.RO2026
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…