works on

From the 1 of 10 linked papers with an AI index.

activity
20242026
collaborators

10 papers

cs.RO2026

Mosaic: An Extensible Framework for Composing Rule-Based and Learned Motion Planners

Nick Le Large, Marlon Steiner, Lingguang Wang +4

Mosaic is a framework that combines rule‑based and learned motion planners using arbitration graphs, separating trajectory verification from selection to improve safety and perform…

cs.CV2026

Creating Impactful Autonomous Driving Datasets: A Strategic Guide from Research Gap to Benchmark

Richard Schwarzkopf, Jonas Merkert, Frank Bieder +22

Well-designed autonomous driving datasets have fundamentally shaped research progress, yet existing literature primarily describes what datasets contain rather than how to strategi…

cs.CV2026

The Road Ahead in Autonomous Driving: The KITScenes Multimodal Dataset

Richard Schwarzkopf, Fabian Immel, Alexander Blumberg +21

Existing autonomous driving datasets have enabled major progress, but fall short in sensor fidelity, map completeness, or geographic diversity. We present KITScenes Multimodal, a E…

cs.CV2026

RetroMotion: Retrocausal Motion Forecasting Models are Instructable

Royden Wagner, Omer Sahin Tas, Felix Hauser +7

Motion forecasts of road users (i.e., agents) vary in complexity depending on the number of agents, scene constraints, and interactions. In particular, the output space of joint tr…

cs.CV2026

LongTail Driving Scenarios with Reasoning Traces: The KITScenes LongTail Dataset

Royden Wagner, Omer Sahin Tas, Jaime Villa +18

In real-world domains such as self-driving, generalization to rare scenarios remains a fundamental challenge. To address this, we introduce a new dataset designed for end-to-end dr…

cs.CV2026

Closing the Navigation Compliance Gap in End-to-end Autonomous Driving

Hanfeng Wu, Marlon Steiner, Michael Schmidt +2

Trajectory-scoring planners achieve high navigation compliance when following the expert's original command, yet they struggle at intersections when presented with alternative comm…