发表机构
Rochester Institute of Technology; University of Macau; City University of Hong Kong（罗切斯特理工学院; 澳门大学; 香港城市大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
TerrainForge提出物理基础的道路几何编辑框架，通过统一路面模型将场景变形与车辆动力学耦合，生成反事实驾驶场景，并用筛选代理高效预测安全结果。
AI 中文摘要
道路几何（如凸坡、凹谷和减速带）和路面状况（如湿滑或结冰路面）会影响车辆的移动方式、驾驶员和车载摄像头的观察内容以及车辆之间剩余的净距。在驾驶场景中编辑这些属性需要对车辆运动进行相应的更改。要在驾驶视频中捕捉这些差异，需要将道路编辑传播到车辆运动、摄像头视角和车辆之间的净距。我们提出了TerrainForge，一个从重建的多车辆驾驶片段中生成以道路几何为重点的反事实的框架。一个统一的路面模型将场景变形与四轮车辆动力学连接起来，使得凸坡、凹谷、减速带和摩擦变化能够通过车辆运动、摄像头视角和车辆间净距进行传播。车辆动力学针对CarSim进行评估，规定的道路几何在重建的Waymo场景中得到验证。在18个片段中，将周围车辆保留在其记录轨迹上而不是重新计算其响应，预测的自我-前车距离的中值峰值差异在凸坡情况下为1.52米，在凹谷情况下为1.41米。我们进一步模拟了自我车辆对983个制动片段中的15,758次道路编辑的响应，将每次编辑与其相对于未编辑回放的安全结果配对。这些配对训练了一个第一阶段筛选代理，该代理以原始驾驶上下文和候选道路编辑参数为输入，预测自我车辆终端间隙的变化。在保留场景上，该预测的平均绝对误差比预测无变化低22-40%，因此可以在完整的多车辆展开之前廉价地筛选候选方案。
英文摘要
Road geometry (e.g., crests, sags, and speed humps) and surface conditions (e.g., wet or icy pavement) affect how vehicles move, what drivers and onboard cameras observe, and how much clearance remains between vehicles. Editing these properties in a driving scene therefore requires corresponding changes in vehicle motion. Capturing these differences in a driving video requires a road edit to propagate to vehicle motion, camera viewpoint, and the clearance between vehicles. We present TerrainForge, a framework for generating road geometry-focused counterfactuals from reconstructed multi-vehicle driving episodes. A unified road model connects scene deformation with four-wheel vehicle dynamics, allowing crests, sags, speed humps, and friction changes to propagate through vehicle motion, camera viewpoint, and inter-vehicle clearance. Vehicle dynamics are evaluated against CarSim, and prescribed road geometry is verified in reconstructed Waymo scenes. Across 18 episodes, leaving surrounding vehicles on their recorded trajectories instead of recomputing their responses produces median peak differences in predicted ego-lead distance of 1.52 m for crests and 1.41 m for sags. We further simulate the ego response to 15,758 road edits across 983 braking episodes, pairing each edit with its safety outcomes relative to an unedited replay. These pairs train a first-stage screening surrogate that takes the original driving context and candidate road-edit parameters as input and predicts the resulting change in the ego's terminal gap. On held-out scenes, this prediction achieves 22-40% lower mean absolute error than predicting no change, so candidates can be screened cheaply before the full multi-vehicle rollout.
