罗伯特·博世公司

Passenger-centred comfort measures are needed to enable adaptive driving styles in automated vehicles, particularly where comfort begins to decrease. This study examined passengers' head motion during automated braking at a crosswalk. Data from thirty-four participants were analysed, who experienced three deceleration levels (−1.5, −2.5, −3.5 m/s2) and two pedestrian visibility conditions, reflecting how quickly the AV braked after the pedestrian appeared, rating comfort respectively. As expected, head pitch and translational acceleration increased with deceleration intensity, while pedestrian visibility had no effect on head motion, even though faster AV responses were previously found more comfortable, suggesting this reflects passengers' judgement of the AV's response rather than head stability. Head-motion measures did not add predictive value beyond vehicle kinematics, likely because comfort ratings were similar across conditions. In this scenario, head-motion mainly reflected physical response to braking rather than subjective evaluation. Future research should test head-motion measures in more complex driving conditions.

Vision foundation models (VFMs) trained on large-scale image datasets provide high-quality features that have significantly advanced 2D visual recognition. However, their potential in 3D scene segmentation remains largely untapped, despite the common availability of 2D images alongside 3D point cloud datasets. While significant research has been dedicated to 2D-3D fusion, recent state-of-the-art 3D methods predominantly focus on 3D data, leaving the integration of VFMs into 3D models underexplored. In this work, we challenge this trend by introducing DITR, a generally applicable approach that extracts 2D foundation model features, projects them to 3D, and finally injects them into a 3D point cloud segmentation model. DITR achieves state-of-the-art results on both indoor and outdoor 3D semantic segmentation benchmarks. To enable the use of VFMs even when images are unavailable during inference, we additionally propose to pretrain 3D models by distilling 2D foundation models. By initializing the 3D backbone with knowledge distilled from 2D VFMs, we create a strong basis for downstream 3D segmentation tasks, ultimately boosting performance across various datasets.

Especially in urban traffic, the field of view of road users is often occluded by buildings, vegetation, or other road users. Thus, conservative driving decisions have to be made, which allow one to always react appropriately and obey traffic rules. Intelligent infrastructure along the roadside, data fusion and cooperative planning services on edge computing servers, and vehicle-to-anything communication can help overcome this limitation and-thus-improve traffic efficiency while retaining safety for all road users. This article summarizes the architecture of such a system, prototyped in Ulm, Germany. Further, it presents results from tests involving connected automated and non-automated vehicles and connected vulnerable road users in regular non-connected traffic, realizing different cooperative maneuvers. Besonders im Stadtverkehr behindern Geb & auml;ude, Vegetation oder andere Verkehrsteilnehmer die Sicht der Verkehrsteilnehmer. Dies erfordert eine entsprechend vorsichtigere Fahrweise, um in jeder Situation angemessen reagieren zu k & ouml;nnen und dabei stets die Verkehrsregeln einzuhalten. Intelligente Infrastruktur entlang der Stra beta e, Datenfusion und kooperative Planungsdienste k & ouml;nnen in Kombination mit vernetzten Fahrzeugen dazu beitragen, diese Einschr & auml;nkung zu & uuml;berwinden und damit die Effizienz des Verkehrsflusses zu verbessern, w & auml;hrend die Sicherheit der Verkehrsteilnehmer weiter gew & auml;hrleistet wird. Dieser Beitrag gibt einen & Uuml;berblick & uuml;ber die Architektur und Funktionsweise einer solchen intelligenten Infrastruktur, die prototypisch in Ulm realisiert wurde. Dar & uuml;ber hinaus werden Ergebnisse von Tests verschiedener kooperativer Man & ouml;ver mit vernetzten, automatisierten Fahrzeugen und vernetzten verletzlichen Verkehrsteilnehmern im ansonsten unvernetzten, realen Stra beta enverkehr vorgestellt.

For robots to work reliably in commercial and industrial applications, recent advances in agentic coding systems have potential to combine interpretable robot programming with the open-world adaptability of model-free policies. We focus on "Variational Automation" (VA), a class of tasks that have larger variations in object geometry and pose than fixed automation. Model-free policies often struggle to close the reliability gap for VA tasks, which must be executed persistently and reliably in commercial and industrial applications. Motivated by prior work on Task and Motion Planning (TAMP) and the Robot Operating System (ROS), we introduce Graph-as-Policy (GaP), a multi-agent coding harness that generates directed computation graphs with perception, planning, and control nodes from a Modular Open Robot Skill Library (MORSL). GaP generates an internal simulation environment to rehearse task instances with different graphs in parallel to iteratively refine the graph structure and parameters to improve success rates and throughput. Evaluation with 8 new open VA task benchmarks, 4 in simulation and 4 in real-world, suggests that GaP can achieve success rates that significantly outperform baselines. Details, code, and data: https://graph-robots.github.io/gap

Hydraulic excavators remain among the least automated construction machines due to nonlinear hydraulics, varying environment interactions, and strict real-time requirements. We present a physics-informed online learning framework that provides a standardized velocity-level control interface, abstracting machine-specific hydraulics and enabling integration with higher-level automation. A physics-motivated base model is augmented with an online residual learner to compensate nonlinearities in real time. Experiments on a 12-ton excavator in leveling, grading, and a demanding 4-point cycle show substantial improvements over the base model alone: maximum absolute tracking error is reduced by up to 63 % in leveling and 70 % in the 4-point cycle, while RMSE is reduced by up to 54 % and 69 %. Hydraulische Bagger z & auml;hlen nach wie vor zu den am wenigsten automatisierten Baumaschinen. Gr & uuml;nde hierf & uuml;r sind die Nichtlinearit & auml;ten der Hydraulik, variierende Interaktionen mit der Umgebung sowie strenge Echtzeitanforderungen. Wir stellen ein physik-informiertes Online-Lernframework vor, das eine standardisierte Regelungsschnittstelle auf Geschwindigkeitsebene bereitstellt. Dadurch werden maschinenspezifische hydraulische Eigenschaften abstrahiert und die Integration in & uuml;berlagerte Automatisierungsfunktionen erm & ouml;glicht. Ein physikalisch motiviertes Basismodell wird um einen online trainierten Residual-Lerner erweitert, der Nichtlinearit & auml;ten in Echtzeit kompensiert. Experimente mit einem 12-Tonnen-Bagger bei Planier- und Gradierungsaufgaben sowie in einem anspruchsvollen 4-Punkt-Zyklus zeigen deutliche Verbesserungen gegen & uuml;ber dem alleinigen Basismodell: Der maximale absolute Regelfehler wird beim Planieren um bis zu 63 % und im 4-Punkt-Zyklus um bis zu 70 % reduziert, w & auml;hrend der RMSE um bis zu 54 % bzw. 69 % sinkt.