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From the 2 of 25 linked papers with an AI index.

most citedFoundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis

16 citations · 18 across the 16 of their papers we have counts for

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cs.RO2026

SGTP: Sampling-based Game-Theoretic Planning for Real-Time Multi-Vehicle Autonomous Racing

Zhouheng Li, Fangguo Zhao, Mattia Piccinini +6

Autonomous multi-vehicle racing requires real-time planning of diverse competitive behaviors in intense interactions. Existing planners often struggle to balance strategic diversit…

cs.RO2026

A Hybrid Sampling-Based Trajectory Planner with Game-Theoretic Guidance for Autonomous Racing

Alexander Langmann, Frederico Pita de Araujo, Mattia Piccinini +1

The paper introduces a hybrid planner that combines game-theoretic reasoning with a sampling-based motion planner to enable fast, strategic trajectory planning for autonomous racin…

cs.RO2026

Validate the Dream Before You Trust Its Verdict: Admissibility for World-Model Simulators

Christian Oefinger, Finn Rasmus Schäfer, Korbinian Moller +2

Across robotics, World Models (WMs) are increasingly used to evaluate action policies by simulating the consequences of actions in an imagined world, and returning a success or saf…

cs.RO2026

Benchmarking Empirical and Learning-Based Approaches for Feedforward Steering Control in Autonomous Racing

Georg Jank, Mattia Piccinini, Sebastian Wenk +3

Feedforward steering control is a key component of hierarchical control architectures for autonomous racing. The goal is to reduce steering corrections from the feedback controller…

cs.RO2026

Trajectory Planning and Control near the Limits: an Open Experimental Benchmark on the RoboRacer Platform

Mattia Piccinini, Patrick Zambiasi, Aniello Mungiello +3

We present a modular framework to benchmark new and existing methods for trajectory planning and control in high-acceleration maneuvers that push autonomous driving to the limits.…

cs.RO2026

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…