GAUGE: A Measurement-Grounded Benchmark for Physical Fidelity in Simulation Engines and Video World Models

Physics engines facilitate large-scale training and evaluation for embodied intelligence, while generative video world models are emerging as implicit simulators of future states and interactions. However, existing evaluations of physical fidelity are often conducted in isolation and rely heavily on perceptual similarity or human judgments, providing limited insight into which physical principles or parameters are violated. We introduce GAUGE, a real-world-grounded diagnostic benchmark for jointly evaluating how numerical simulators and generative video world models reproduce or deviate from real-world physics. It comprises 22 controlled task families covering rigid bodies, flexible cables, textiles, and volumetric deformable objects. Grounded in real-world trajectories and paired with calibrated physical metadata, uncertainty annotations, and task-specific observables, these tasks cover fundamental physical processes including collision, friction, momentum transfer, oscillation, self-contact, and deformation across diverse materials and conditions. We benchmark Isaac Sim, Genesis, and Newton on 14 task families using generalized trajectory errors, and evaluate 6 image-to-video models on 5 rigid-body tasks by testing physical-law consistency and the temporal stability of inferred parameters. Our results reveal no uniformly faithful physics engine, with the largest discrepancies arising in impulsive contact, rapid textile motion, and volumetric deformation. We further find that video world models can produce trajectories with the expected equation form while recovering incorrect accelerations, momentum transfer, and oscillation timing. GAUGE lays the groundwork for developing more physically faithful simulators and world models for embodied intelligence.

ElasticLaneNet: A Geometry-Flexible Approach for Lane Detection

The task of lane detection involves identifying the boundaries of driving areas. Recognizing lanes with complex and variable geometric structures remains a challenge. In this paper, we introduce a new lane detection framework named ElasticLaneNet (Elastic-interaction-energy guided Lane detection Network). A novel and flexible way of representing lanes, namely, implicit representation is proposed. The training strategy considers predicted lanes as moving curves that being attracted to the ground truth guided by an elastic interaction energy based loss function (EIE loss). An auxiliary feature refinement (AFR) module is designed to gather information from different layers. The method performs well in complex lane scenarios, including those with large curvature, weak geometric features at intersections, complicated cross lanes, Y-shapes lanes, dense lanes, etc. We apply our approach on three datasets: SDLane, CULane, and TuSimple. The results demonstrate the exceptional performance of our method, with the state-of-the-art results on the structure-diversity dataset SDLane, achieving F1-score of 89.51, Recall rate of 87.50, and Precision of 91.61.

Elastic Interaction Energy Loss for Traffic Image Segmentation

Segmentation is a pixel-level classification of images. The accuracy and fast inference speed of image segmentation are crucial for autonomous driving safety. Fine and complex geometric objects are the most difficult but important recognition targets in traffic scene, such as pedestrians, traffic signs and lanes. In this paper, a simple and efficient geometry-sensitive energy-based loss function is proposed to Convolutional Neural Network (CNN) for multi-class segmentation on real-time traffic scene understanding. To be specific, the elastic interaction energy (EIE) between two boundaries will drive the prediction moving toward the ground truth until completely overlap. The EIE loss function is incorporated into CNN to enhance accuracy on fine-scale structure segmentation. In particular, small or irregularly shaped objects can be identified more accurately, and discontinuity issues on slender objects can be improved. Our approach can be applied to different segmentation-based problems, such as urban scene segmentation and lane detection. We quantitatively and qualitatively analyze our method on three traffic datasets, including urban scene data Cityscapes, lane data TuSimple and CULane. The results show that our approach consistently improves performance, especially when using real-time, lightweight networks as the backbones, which is more suitable for autonomous driving.